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/* Loop Vectorization Copyright (C) 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012 Free Software Foundation, Inc. Contributed by Dorit Naishlos <dorit@il.ibm.com> and Ira Rosen <irar@il.ibm.com> This file is part of GCC. GCC is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version. GCC is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with GCC; see the file COPYING3. If not see <http://www.gnu.org/licenses/>. */ #include "config.h" #include "system.h" #include "coretypes.h" #include "tm.h" #include "ggc.h" #include "tree.h" #include "basic-block.h" #include "tree-pretty-print.h" #include "gimple-pretty-print.h" #include "tree-flow.h" #include "tree-dump.h" #include "cfgloop.h" #include "cfglayout.h" #include "expr.h" #include "recog.h" #include "optabs.h" #include "params.h" #include "diagnostic-core.h" #include "tree-chrec.h" #include "tree-scalar-evolution.h" #include "tree-vectorizer.h" #include "target.h" /* Loop Vectorization Pass. This pass tries to vectorize loops. For example, the vectorizer transforms the following simple loop: short a[N]; short b[N]; short c[N]; int i; for (i=0; i<N; i++){ a[i] = b[i] + c[i]; } as if it was manually vectorized by rewriting the source code into: typedef int __attribute__((mode(V8HI))) v8hi; short a[N]; short b[N]; short c[N]; int i; v8hi *pa = (v8hi*)a, *pb = (v8hi*)b, *pc = (v8hi*)c; v8hi va, vb, vc; for (i=0; i<N/8; i++){ vb = pb[i]; vc = pc[i]; va = vb + vc; pa[i] = va; } The main entry to this pass is vectorize_loops(), in which the vectorizer applies a set of analyses on a given set of loops, followed by the actual vectorization transformation for the loops that had successfully passed the analysis phase. Throughout this pass we make a distinction between two types of data: scalars (which are represented by SSA_NAMES), and memory references ("data-refs"). These two types of data require different handling both during analysis and transformation. The types of data-refs that the vectorizer currently supports are ARRAY_REFS which base is an array DECL (not a pointer), and INDIRECT_REFS through pointers; both array and pointer accesses are required to have a simple (consecutive) access pattern. Analysis phase: =============== The driver for the analysis phase is vect_analyze_loop(). It applies a set of analyses, some of which rely on the scalar evolution analyzer (scev) developed by Sebastian Pop. During the analysis phase the vectorizer records some information per stmt in a "stmt_vec_info" struct which is attached to each stmt in the loop, as well as general information about the loop as a whole, which is recorded in a "loop_vec_info" struct attached to each loop. Transformation phase: ===================== The loop transformation phase scans all the stmts in the loop, and creates a vector stmt (or a sequence of stmts) for each scalar stmt S in the loop that needs to be vectorized. It inserts the vector code sequence just before the scalar stmt S, and records a pointer to the vector code in STMT_VINFO_VEC_STMT (stmt_info) (stmt_info is the stmt_vec_info struct attached to S). This pointer will be used for the vectorization of following stmts which use the def of stmt S. Stmt S is removed if it writes to memory; otherwise, we rely on dead code elimination for removing it. For example, say stmt S1 was vectorized into stmt VS1: VS1: vb = px[i]; S1: b = x[i]; STMT_VINFO_VEC_STMT (stmt_info (S1)) = VS1 S2: a = b; To vectorize stmt S2, the vectorizer first finds the stmt that defines the operand 'b' (S1), and gets the relevant vector def 'vb' from the vector stmt VS1 pointed to by STMT_VINFO_VEC_STMT (stmt_info (S1)). The resulting sequence would be: VS1: vb = px[i]; S1: b = x[i]; STMT_VINFO_VEC_STMT (stmt_info (S1)) = VS1 VS2: va = vb; S2: a = b; STMT_VINFO_VEC_STMT (stmt_info (S2)) = VS2 Operands that are not SSA_NAMEs, are data-refs that appear in load/store operations (like 'x[i]' in S1), and are handled differently. Target modeling: ================= Currently the only target specific information that is used is the size of the vector (in bytes) - "TARGET_VECTORIZE_UNITS_PER_SIMD_WORD". Targets that can support different sizes of vectors, for now will need to specify one value for "TARGET_VECTORIZE_UNITS_PER_SIMD_WORD". More flexibility will be added in the future. Since we only vectorize operations which vector form can be expressed using existing tree codes, to verify that an operation is supported, the vectorizer checks the relevant optab at the relevant machine_mode (e.g, optab_handler (add_optab, V8HImode)). If the value found is CODE_FOR_nothing, then there's no target support, and we can't vectorize the stmt. For additional information on this project see: http://gcc.gnu.org/projects/tree-ssa/vectorization.html */ /* Function vect_determine_vectorization_factor Determine the vectorization factor (VF). VF is the number of data elements that are operated upon in parallel in a single iteration of the vectorized loop. For example, when vectorizing a loop that operates on 4byte elements, on a target with vector size (VS) 16byte, the VF is set to 4, since 4 elements can fit in a single vector register. We currently support vectorization of loops in which all types operated upon are of the same size. Therefore this function currently sets VF according to the size of the types operated upon, and fails if there are multiple sizes in the loop. VF is also the factor by which the loop iterations are strip-mined, e.g.: original loop: for (i=0; i<N; i++){ a[i] = b[i] + c[i]; } vectorized loop: for (i=0; i<N; i+=VF){ a[i:VF] = b[i:VF] + c[i:VF]; } */ static bool vect_determine_vectorization_factor (loop_vec_info loop_vinfo) { struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo); int nbbs = loop->num_nodes; gimple_stmt_iterator si; unsigned int vectorization_factor = 0; tree scalar_type; gimple phi; tree vectype; unsigned int nunits; stmt_vec_info stmt_info; int i; HOST_WIDE_INT dummy; gimple stmt, pattern_stmt = NULL; gimple_seq pattern_def_seq = NULL; gimple_stmt_iterator pattern_def_si = gsi_start (NULL); bool analyze_pattern_stmt = false; if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "=== vect_determine_vectorization_factor ==="); for (i = 0; i < nbbs; i++) { basic_block bb = bbs[i]; for (si = gsi_start_phis (bb); !gsi_end_p (si); gsi_next (&si)) { phi = gsi_stmt (si); stmt_info = vinfo_for_stmt (phi); if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "==> examining phi: "); print_gimple_stmt (vect_dump, phi, 0, TDF_SLIM); } gcc_assert (stmt_info); if (STMT_VINFO_RELEVANT_P (stmt_info)) { gcc_assert (!STMT_VINFO_VECTYPE (stmt_info)); scalar_type = TREE_TYPE (PHI_RESULT (phi)); if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "get vectype for scalar type: "); print_generic_expr (vect_dump, scalar_type, TDF_SLIM); } vectype = get_vectype_for_scalar_type (scalar_type); if (!vectype) { if (vect_print_dump_info (REPORT_UNVECTORIZED_LOCATIONS)) { fprintf (vect_dump, "not vectorized: unsupported data-type "); print_generic_expr (vect_dump, scalar_type, TDF_SLIM); } return false; } STMT_VINFO_VECTYPE (stmt_info) = vectype; if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "vectype: "); print_generic_expr (vect_dump, vectype, TDF_SLIM); } nunits = TYPE_VECTOR_SUBPARTS (vectype); if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "nunits = %d", nunits); if (!vectorization_factor || (nunits > vectorization_factor)) vectorization_factor = nunits; } } for (si = gsi_start_bb (bb); !gsi_end_p (si) || analyze_pattern_stmt;) { tree vf_vectype; if (analyze_pattern_stmt) stmt = pattern_stmt; else stmt = gsi_stmt (si); stmt_info = vinfo_for_stmt (stmt); if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "==> examining statement: "); print_gimple_stmt (vect_dump, stmt, 0, TDF_SLIM); } gcc_assert (stmt_info); /* Skip stmts which do not need to be vectorized. */ if (!STMT_VINFO_RELEVANT_P (stmt_info) && !STMT_VINFO_LIVE_P (stmt_info)) { if (STMT_VINFO_IN_PATTERN_P (stmt_info) && (pattern_stmt = STMT_VINFO_RELATED_STMT (stmt_info)) && (STMT_VINFO_RELEVANT_P (vinfo_for_stmt (pattern_stmt)) || STMT_VINFO_LIVE_P (vinfo_for_stmt (pattern_stmt)))) { stmt = pattern_stmt; stmt_info = vinfo_for_stmt (pattern_stmt); if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "==> examining pattern statement: "); print_gimple_stmt (vect_dump, stmt, 0, TDF_SLIM); } } else { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "skip."); gsi_next (&si); continue; } } else if (STMT_VINFO_IN_PATTERN_P (stmt_info) && (pattern_stmt = STMT_VINFO_RELATED_STMT (stmt_info)) && (STMT_VINFO_RELEVANT_P (vinfo_for_stmt (pattern_stmt)) || STMT_VINFO_LIVE_P (vinfo_for_stmt (pattern_stmt)))) analyze_pattern_stmt = true; /* If a pattern statement has def stmts, analyze them too. */ if (is_pattern_stmt_p (stmt_info)) { if (pattern_def_seq == NULL) { pattern_def_seq = STMT_VINFO_PATTERN_DEF_SEQ (stmt_info); pattern_def_si = gsi_start (pattern_def_seq); } else if (!gsi_end_p (pattern_def_si)) gsi_next (&pattern_def_si); if (pattern_def_seq != NULL) { gimple pattern_def_stmt = NULL; stmt_vec_info pattern_def_stmt_info = NULL; while (!gsi_end_p (pattern_def_si)) { pattern_def_stmt = gsi_stmt (pattern_def_si); pattern_def_stmt_info = vinfo_for_stmt (pattern_def_stmt); if (STMT_VINFO_RELEVANT_P (pattern_def_stmt_info) || STMT_VINFO_LIVE_P (pattern_def_stmt_info)) break; gsi_next (&pattern_def_si); } if (!gsi_end_p (pattern_def_si)) { if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "==> examining pattern def stmt: "); print_gimple_stmt (vect_dump, pattern_def_stmt, 0, TDF_SLIM); } stmt = pattern_def_stmt; stmt_info = pattern_def_stmt_info; } else { pattern_def_si = gsi_start (NULL); analyze_pattern_stmt = false; } } else analyze_pattern_stmt = false; } if (gimple_get_lhs (stmt) == NULL_TREE) { if (vect_print_dump_info (REPORT_UNVECTORIZED_LOCATIONS)) { fprintf (vect_dump, "not vectorized: irregular stmt."); print_gimple_stmt (vect_dump, stmt, 0, TDF_SLIM); } return false; } if (VECTOR_MODE_P (TYPE_MODE (gimple_expr_type (stmt)))) { if (vect_print_dump_info (REPORT_UNVECTORIZED_LOCATIONS)) { fprintf (vect_dump, "not vectorized: vector stmt in loop:"); print_gimple_stmt (vect_dump, stmt, 0, TDF_SLIM); } return false; } if (STMT_VINFO_VECTYPE (stmt_info)) { /* The only case when a vectype had been already set is for stmts that contain a dataref, or for "pattern-stmts" (stmts generated by the vectorizer to represent/replace a certain idiom). */ gcc_assert (STMT_VINFO_DATA_REF (stmt_info) || is_pattern_stmt_p (stmt_info) || !gsi_end_p (pattern_def_si)); vectype = STMT_VINFO_VECTYPE (stmt_info); } else { gcc_assert (!STMT_VINFO_DATA_REF (stmt_info)); scalar_type = TREE_TYPE (gimple_get_lhs (stmt)); if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "get vectype for scalar type: "); print_generic_expr (vect_dump, scalar_type, TDF_SLIM); } vectype = get_vectype_for_scalar_type (scalar_type); if (!vectype) { if (vect_print_dump_info (REPORT_UNVECTORIZED_LOCATIONS)) { fprintf (vect_dump, "not vectorized: unsupported data-type "); print_generic_expr (vect_dump, scalar_type, TDF_SLIM); } return false; } STMT_VINFO_VECTYPE (stmt_info) = vectype; } /* The vectorization factor is according to the smallest scalar type (or the largest vector size, but we only support one vector size per loop). */ scalar_type = vect_get_smallest_scalar_type (stmt, &dummy, &dummy); if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "get vectype for scalar type: "); print_generic_expr (vect_dump, scalar_type, TDF_SLIM); } vf_vectype = get_vectype_for_scalar_type (scalar_type); if (!vf_vectype) { if (vect_print_dump_info (REPORT_UNVECTORIZED_LOCATIONS)) { fprintf (vect_dump, "not vectorized: unsupported data-type "); print_generic_expr (vect_dump, scalar_type, TDF_SLIM); } return false; } if ((GET_MODE_SIZE (TYPE_MODE (vectype)) != GET_MODE_SIZE (TYPE_MODE (vf_vectype)))) { if (vect_print_dump_info (REPORT_UNVECTORIZED_LOCATIONS)) { fprintf (vect_dump, "not vectorized: different sized vector " "types in statement, "); print_generic_expr (vect_dump, vectype, TDF_SLIM); fprintf (vect_dump, " and "); print_generic_expr (vect_dump, vf_vectype, TDF_SLIM); } return false; } if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "vectype: "); print_generic_expr (vect_dump, vf_vectype, TDF_SLIM); } nunits = TYPE_VECTOR_SUBPARTS (vf_vectype); if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "nunits = %d", nunits); if (!vectorization_factor || (nunits > vectorization_factor)) vectorization_factor = nunits; if (!analyze_pattern_stmt && gsi_end_p (pattern_def_si)) { pattern_def_seq = NULL; gsi_next (&si); } } } /* TODO: Analyze cost. Decide if worth while to vectorize. */ if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "vectorization factor = %d", vectorization_factor); if (vectorization_factor <= 1) { if (vect_print_dump_info (REPORT_UNVECTORIZED_LOCATIONS)) fprintf (vect_dump, "not vectorized: unsupported data-type"); return false; } LOOP_VINFO_VECT_FACTOR (loop_vinfo) = vectorization_factor; return true; } /* Function vect_is_simple_iv_evolution. FORNOW: A simple evolution of an induction variables in the loop is considered a polynomial evolution with constant step. */ static bool vect_is_simple_iv_evolution (unsigned loop_nb, tree access_fn, tree * init, tree * step) { tree init_expr; tree step_expr; tree evolution_part = evolution_part_in_loop_num (access_fn, loop_nb); /* When there is no evolution in this loop, the evolution function is not "simple". */ if (evolution_part == NULL_TREE) return false; /* When the evolution is a polynomial of degree >= 2 the evolution function is not "simple". */ if (tree_is_chrec (evolution_part)) return false; step_expr = evolution_part; init_expr = unshare_expr (initial_condition_in_loop_num (access_fn, loop_nb)); if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "step: "); print_generic_expr (vect_dump, step_expr, TDF_SLIM); fprintf (vect_dump, ", init: "); print_generic_expr (vect_dump, init_expr, TDF_SLIM); } *init = init_expr; *step = step_expr; if (TREE_CODE (step_expr) != INTEGER_CST) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "step unknown."); return false; } return true; } /* Function vect_analyze_scalar_cycles_1. Examine the cross iteration def-use cycles of scalar variables in LOOP. LOOP_VINFO represents the loop that is now being considered for vectorization (can be LOOP, or an outer-loop enclosing LOOP). */ static void vect_analyze_scalar_cycles_1 (loop_vec_info loop_vinfo, struct loop *loop) { basic_block bb = loop->header; tree dumy; VEC(gimple,heap) *worklist = VEC_alloc (gimple, heap, 64); gimple_stmt_iterator gsi; bool double_reduc; if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "=== vect_analyze_scalar_cycles ==="); /* First - identify all inductions. Reduction detection assumes that all the inductions have been identified, therefore, this order must not be changed. */ for (gsi = gsi_start_phis (bb); !gsi_end_p (gsi); gsi_next (&gsi)) { gimple phi = gsi_stmt (gsi); tree access_fn = NULL; tree def = PHI_RESULT (phi); stmt_vec_info stmt_vinfo = vinfo_for_stmt (phi); if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "Analyze phi: "); print_gimple_stmt (vect_dump, phi, 0, TDF_SLIM); } /* Skip virtual phi's. The data dependences that are associated with virtual defs/uses (i.e., memory accesses) are analyzed elsewhere. */ if (!is_gimple_reg (SSA_NAME_VAR (def))) continue; STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_unknown_def_type; /* Analyze the evolution function. */ access_fn = analyze_scalar_evolution (loop, def); if (access_fn) STRIP_NOPS (access_fn); if (access_fn && vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "Access function of PHI: "); print_generic_expr (vect_dump, access_fn, TDF_SLIM); } if (!access_fn || !vect_is_simple_iv_evolution (loop->num, access_fn, &dumy, &dumy)) { VEC_safe_push (gimple, heap, worklist, phi); continue; } if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "Detected induction."); STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_induction_def; } /* Second - identify all reductions and nested cycles. */ while (VEC_length (gimple, worklist) > 0) { gimple phi = VEC_pop (gimple, worklist); tree def = PHI_RESULT (phi); stmt_vec_info stmt_vinfo = vinfo_for_stmt (phi); gimple reduc_stmt; bool nested_cycle; if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "Analyze phi: "); print_gimple_stmt (vect_dump, phi, 0, TDF_SLIM); } gcc_assert (is_gimple_reg (SSA_NAME_VAR (def))); gcc_assert (STMT_VINFO_DEF_TYPE (stmt_vinfo) == vect_unknown_def_type); nested_cycle = (loop != LOOP_VINFO_LOOP (loop_vinfo)); reduc_stmt = vect_force_simple_reduction (loop_vinfo, phi, !nested_cycle, &double_reduc); if (reduc_stmt) { if (double_reduc) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "Detected double reduction."); STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_double_reduction_def; STMT_VINFO_DEF_TYPE (vinfo_for_stmt (reduc_stmt)) = vect_double_reduction_def; } else { if (nested_cycle) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "Detected vectorizable nested cycle."); STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_nested_cycle; STMT_VINFO_DEF_TYPE (vinfo_for_stmt (reduc_stmt)) = vect_nested_cycle; } else { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "Detected reduction."); STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_reduction_def; STMT_VINFO_DEF_TYPE (vinfo_for_stmt (reduc_stmt)) = vect_reduction_def; /* Store the reduction cycles for possible vectorization in loop-aware SLP. */ VEC_safe_push (gimple, heap, LOOP_VINFO_REDUCTIONS (loop_vinfo), reduc_stmt); } } } else if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "Unknown def-use cycle pattern."); } VEC_free (gimple, heap, worklist); } /* Function vect_analyze_scalar_cycles. Examine the cross iteration def-use cycles of scalar variables, by analyzing the loop-header PHIs of scalar variables. Classify each cycle as one of the following: invariant, induction, reduction, unknown. We do that for the loop represented by LOOP_VINFO, and also to its inner-loop, if exists. Examples for scalar cycles: Example1: reduction: loop1: for (i=0; i<N; i++) sum += a[i]; Example2: induction: loop2: for (i=0; i<N; i++) a[i] = i; */ static void vect_analyze_scalar_cycles (loop_vec_info loop_vinfo) { struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); vect_analyze_scalar_cycles_1 (loop_vinfo, loop); /* When vectorizing an outer-loop, the inner-loop is executed sequentially. Reductions in such inner-loop therefore have different properties than the reductions in the nest that gets vectorized: 1. When vectorized, they are executed in the same order as in the original scalar loop, so we can't change the order of computation when vectorizing them. 2. FIXME: Inner-loop reductions can be used in the inner-loop, so the current checks are too strict. */ if (loop->inner) vect_analyze_scalar_cycles_1 (loop_vinfo, loop->inner); } /* Function vect_get_loop_niters. Determine how many iterations the loop is executed. If an expression that represents the number of iterations can be constructed, place it in NUMBER_OF_ITERATIONS. Return the loop exit condition. */ static gimple vect_get_loop_niters (struct loop *loop, tree *number_of_iterations) { tree niters; if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "=== get_loop_niters ==="); niters = number_of_exit_cond_executions (loop); if (niters != NULL_TREE && niters != chrec_dont_know) { *number_of_iterations = niters; if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "==> get_loop_niters:" ); print_generic_expr (vect_dump, *number_of_iterations, TDF_SLIM); } } return get_loop_exit_condition (loop); } /* Function bb_in_loop_p Used as predicate for dfs order traversal of the loop bbs. */ static bool bb_in_loop_p (const_basic_block bb, const void *data) { const struct loop *const loop = (const struct loop *)data; if (flow_bb_inside_loop_p (loop, bb)) return true; return false; } /* Function new_loop_vec_info. Create and initialize a new loop_vec_info struct for LOOP, as well as stmt_vec_info structs for all the stmts in LOOP. */ static loop_vec_info new_loop_vec_info (struct loop *loop) { loop_vec_info res; basic_block *bbs; gimple_stmt_iterator si; unsigned int i, nbbs; res = (loop_vec_info) xcalloc (1, sizeof (struct _loop_vec_info)); LOOP_VINFO_LOOP (res) = loop; bbs = get_loop_body (loop); /* Create/Update stmt_info for all stmts in the loop. */ for (i = 0; i < loop->num_nodes; i++) { basic_block bb = bbs[i]; /* BBs in a nested inner-loop will have been already processed (because we will have called vect_analyze_loop_form for any nested inner-loop). Therefore, for stmts in an inner-loop we just want to update the STMT_VINFO_LOOP_VINFO field of their stmt_info to point to the new loop_info of the outer-loop we are currently considering to vectorize (instead of the loop_info of the inner-loop). For stmts in other BBs we need to create a stmt_info from scratch. */ if (bb->loop_father != loop) { /* Inner-loop bb. */ gcc_assert (loop->inner && bb->loop_father == loop->inner); for (si = gsi_start_phis (bb); !gsi_end_p (si); gsi_next (&si)) { gimple phi = gsi_stmt (si); stmt_vec_info stmt_info = vinfo_for_stmt (phi); loop_vec_info inner_loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info); gcc_assert (loop->inner == LOOP_VINFO_LOOP (inner_loop_vinfo)); STMT_VINFO_LOOP_VINFO (stmt_info) = res; } for (si = gsi_start_bb (bb); !gsi_end_p (si); gsi_next (&si)) { gimple stmt = gsi_stmt (si); stmt_vec_info stmt_info = vinfo_for_stmt (stmt); loop_vec_info inner_loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info); gcc_assert (loop->inner == LOOP_VINFO_LOOP (inner_loop_vinfo)); STMT_VINFO_LOOP_VINFO (stmt_info) = res; } } else { /* bb in current nest. */ for (si = gsi_start_phis (bb); !gsi_end_p (si); gsi_next (&si)) { gimple phi = gsi_stmt (si); gimple_set_uid (phi, 0); set_vinfo_for_stmt (phi, new_stmt_vec_info (phi, res, NULL)); } for (si = gsi_start_bb (bb); !gsi_end_p (si); gsi_next (&si)) { gimple stmt = gsi_stmt (si); gimple_set_uid (stmt, 0); set_vinfo_for_stmt (stmt, new_stmt_vec_info (stmt, res, NULL)); } } } /* CHECKME: We want to visit all BBs before their successors (except for latch blocks, for which this assertion wouldn't hold). In the simple case of the loop forms we allow, a dfs order of the BBs would the same as reversed postorder traversal, so we are safe. */ free (bbs); bbs = XCNEWVEC (basic_block, loop->num_nodes); nbbs = dfs_enumerate_from (loop->header, 0, bb_in_loop_p, bbs, loop->num_nodes, loop); gcc_assert (nbbs == loop->num_nodes); LOOP_VINFO_BBS (res) = bbs; LOOP_VINFO_NITERS (res) = NULL; LOOP_VINFO_NITERS_UNCHANGED (res) = NULL; LOOP_VINFO_COST_MODEL_MIN_ITERS (res) = 0; LOOP_VINFO_VECTORIZABLE_P (res) = 0; LOOP_PEELING_FOR_ALIGNMENT (res) = 0; LOOP_VINFO_VECT_FACTOR (res) = 0; LOOP_VINFO_LOOP_NEST (res) = VEC_alloc (loop_p, heap, 3); LOOP_VINFO_DATAREFS (res) = VEC_alloc (data_reference_p, heap, 10); LOOP_VINFO_DDRS (res) = VEC_alloc (ddr_p, heap, 10 * 10); LOOP_VINFO_UNALIGNED_DR (res) = NULL; LOOP_VINFO_MAY_MISALIGN_STMTS (res) = VEC_alloc (gimple, heap, PARAM_VALUE (PARAM_VECT_MAX_VERSION_FOR_ALIGNMENT_CHECKS)); LOOP_VINFO_MAY_ALIAS_DDRS (res) = VEC_alloc (ddr_p, heap, PARAM_VALUE (PARAM_VECT_MAX_VERSION_FOR_ALIAS_CHECKS)); LOOP_VINFO_STRIDED_STORES (res) = VEC_alloc (gimple, heap, 10); LOOP_VINFO_REDUCTIONS (res) = VEC_alloc (gimple, heap, 10); LOOP_VINFO_REDUCTION_CHAINS (res) = VEC_alloc (gimple, heap, 10); LOOP_VINFO_SLP_INSTANCES (res) = VEC_alloc (slp_instance, heap, 10); LOOP_VINFO_SLP_UNROLLING_FACTOR (res) = 1; LOOP_VINFO_PEELING_HTAB (res) = NULL; LOOP_VINFO_PEELING_FOR_GAPS (res) = false; return res; } /* Function destroy_loop_vec_info. Free LOOP_VINFO struct, as well as all the stmt_vec_info structs of all the stmts in the loop. */ void destroy_loop_vec_info (loop_vec_info loop_vinfo, bool clean_stmts) { struct loop *loop; basic_block *bbs; int nbbs; gimple_stmt_iterator si; int j; VEC (slp_instance, heap) *slp_instances; slp_instance instance; if (!loop_vinfo) return; loop = LOOP_VINFO_LOOP (loop_vinfo); bbs = LOOP_VINFO_BBS (loop_vinfo); nbbs = loop->num_nodes; if (!clean_stmts) { free (LOOP_VINFO_BBS (loop_vinfo)); free_data_refs (LOOP_VINFO_DATAREFS (loop_vinfo)); free_dependence_relations (LOOP_VINFO_DDRS (loop_vinfo)); VEC_free (loop_p, heap, LOOP_VINFO_LOOP_NEST (loop_vinfo)); VEC_free (gimple, heap, LOOP_VINFO_MAY_MISALIGN_STMTS (loop_vinfo)); VEC_free (ddr_p, heap, LOOP_VINFO_MAY_ALIAS_DDRS (loop_vinfo)); free (loop_vinfo); loop->aux = NULL; return; } for (j = 0; j < nbbs; j++) { basic_block bb = bbs[j]; for (si = gsi_start_phis (bb); !gsi_end_p (si); gsi_next (&si)) free_stmt_vec_info (gsi_stmt (si)); for (si = gsi_start_bb (bb); !gsi_end_p (si); ) { gimple stmt = gsi_stmt (si); /* Free stmt_vec_info. */ free_stmt_vec_info (stmt); gsi_next (&si); } } free (LOOP_VINFO_BBS (loop_vinfo)); free_data_refs (LOOP_VINFO_DATAREFS (loop_vinfo)); free_dependence_relations (LOOP_VINFO_DDRS (loop_vinfo)); VEC_free (loop_p, heap, LOOP_VINFO_LOOP_NEST (loop_vinfo)); VEC_free (gimple, heap, LOOP_VINFO_MAY_MISALIGN_STMTS (loop_vinfo)); VEC_free (ddr_p, heap, LOOP_VINFO_MAY_ALIAS_DDRS (loop_vinfo)); slp_instances = LOOP_VINFO_SLP_INSTANCES (loop_vinfo); FOR_EACH_VEC_ELT (slp_instance, slp_instances, j, instance) vect_free_slp_instance (instance); VEC_free (slp_instance, heap, LOOP_VINFO_SLP_INSTANCES (loop_vinfo)); VEC_free (gimple, heap, LOOP_VINFO_STRIDED_STORES (loop_vinfo)); VEC_free (gimple, heap, LOOP_VINFO_REDUCTIONS (loop_vinfo)); VEC_free (gimple, heap, LOOP_VINFO_REDUCTION_CHAINS (loop_vinfo)); if (LOOP_VINFO_PEELING_HTAB (loop_vinfo)) htab_delete (LOOP_VINFO_PEELING_HTAB (loop_vinfo)); free (loop_vinfo); loop->aux = NULL; } /* Function vect_analyze_loop_1. Apply a set of analyses on LOOP, and create a loop_vec_info struct for it. The different analyses will record information in the loop_vec_info struct. This is a subset of the analyses applied in vect_analyze_loop, to be applied on an inner-loop nested in the loop that is now considered for (outer-loop) vectorization. */ static loop_vec_info vect_analyze_loop_1 (struct loop *loop) { loop_vec_info loop_vinfo; if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "===== analyze_loop_nest_1 ====="); /* Check the CFG characteristics of the loop (nesting, entry/exit, etc. */ loop_vinfo = vect_analyze_loop_form (loop); if (!loop_vinfo) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "bad inner-loop form."); return NULL; } return loop_vinfo; } /* Function vect_analyze_loop_form. Verify that certain CFG restrictions hold, including: - the loop has a pre-header - the loop has a single entry and exit - the loop exit condition is simple enough, and the number of iterations can be analyzed (a countable loop). */ loop_vec_info vect_analyze_loop_form (struct loop *loop) { loop_vec_info loop_vinfo; gimple loop_cond; tree number_of_iterations = NULL; loop_vec_info inner_loop_vinfo = NULL; if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "=== vect_analyze_loop_form ==="); /* Different restrictions apply when we are considering an inner-most loop, vs. an outer (nested) loop. (FORNOW. May want to relax some of these restrictions in the future). */ if (!loop->inner) { /* Inner-most loop. We currently require that the number of BBs is exactly 2 (the header and latch). Vectorizable inner-most loops look like this: (pre-header) | header <--------+ | | | | +--> latch --+ | (exit-bb) */ if (loop->num_nodes != 2) { if (vect_print_dump_info (REPORT_BAD_FORM_LOOPS)) fprintf (vect_dump, "not vectorized: control flow in loop."); return NULL; } if (empty_block_p (loop->header)) { if (vect_print_dump_info (REPORT_BAD_FORM_LOOPS)) fprintf (vect_dump, "not vectorized: empty loop."); return NULL; } } else { struct loop *innerloop = loop->inner; edge entryedge; /* Nested loop. We currently require that the loop is doubly-nested, contains a single inner loop, and the number of BBs is exactly 5. Vectorizable outer-loops look like this: (pre-header) | header <---+ | | inner-loop | | | tail ------+ | (exit-bb) The inner-loop has the properties expected of inner-most loops as described above. */ if ((loop->inner)->inner || (loop->inner)->next) { if (vect_print_dump_info (REPORT_BAD_FORM_LOOPS)) fprintf (vect_dump, "not vectorized: multiple nested loops."); return NULL; } /* Analyze the inner-loop. */ inner_loop_vinfo = vect_analyze_loop_1 (loop->inner); if (!inner_loop_vinfo) { if (vect_print_dump_info (REPORT_BAD_FORM_LOOPS)) fprintf (vect_dump, "not vectorized: Bad inner loop."); return NULL; } if (!expr_invariant_in_loop_p (loop, LOOP_VINFO_NITERS (inner_loop_vinfo))) { if (vect_print_dump_info (REPORT_BAD_FORM_LOOPS)) fprintf (vect_dump, "not vectorized: inner-loop count not invariant."); destroy_loop_vec_info (inner_loop_vinfo, true); return NULL; } if (loop->num_nodes != 5) { if (vect_print_dump_info (REPORT_BAD_FORM_LOOPS)) fprintf (vect_dump, "not vectorized: control flow in loop."); destroy_loop_vec_info (inner_loop_vinfo, true); return NULL; } gcc_assert (EDGE_COUNT (innerloop->header->preds) == 2); entryedge = EDGE_PRED (innerloop->header, 0); if (EDGE_PRED (innerloop->header, 0)->src == innerloop->latch) entryedge = EDGE_PRED (innerloop->header, 1); if (entryedge->src != loop->header || !single_exit (innerloop) || single_exit (innerloop)->dest != EDGE_PRED (loop->latch, 0)->src) { if (vect_print_dump_info (REPORT_BAD_FORM_LOOPS)) fprintf (vect_dump, "not vectorized: unsupported outerloop form."); destroy_loop_vec_info (inner_loop_vinfo, true); return NULL; } if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "Considering outer-loop vectorization."); } if (!single_exit (loop) || EDGE_COUNT (loop->header->preds) != 2) { if (vect_print_dump_info (REPORT_BAD_FORM_LOOPS)) { if (!single_exit (loop)) fprintf (vect_dump, "not vectorized: multiple exits."); else if (EDGE_COUNT (loop->header->preds) != 2) fprintf (vect_dump, "not vectorized: too many incoming edges."); } if (inner_loop_vinfo) destroy_loop_vec_info (inner_loop_vinfo, true); return NULL; } /* We assume that the loop exit condition is at the end of the loop. i.e, that the loop is represented as a do-while (with a proper if-guard before the loop if needed), where the loop header contains all the executable statements, and the latch is empty. */ if (!empty_block_p (loop->latch) || !gimple_seq_empty_p (phi_nodes (loop->latch))) { if (vect_print_dump_info (REPORT_BAD_FORM_LOOPS)) fprintf (vect_dump, "not vectorized: unexpected loop form."); if (inner_loop_vinfo) destroy_loop_vec_info (inner_loop_vinfo, true); return NULL; } /* Make sure there exists a single-predecessor exit bb: */ if (!single_pred_p (single_exit (loop)->dest)) { edge e = single_exit (loop); if (!(e->flags & EDGE_ABNORMAL)) { split_loop_exit_edge (e); if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "split exit edge."); } else { if (vect_print_dump_info (REPORT_BAD_FORM_LOOPS)) fprintf (vect_dump, "not vectorized: abnormal loop exit edge."); if (inner_loop_vinfo) destroy_loop_vec_info (inner_loop_vinfo, true); return NULL; } } loop_cond = vect_get_loop_niters (loop, &number_of_iterations); if (!loop_cond) { if (vect_print_dump_info (REPORT_BAD_FORM_LOOPS)) fprintf (vect_dump, "not vectorized: complicated exit condition."); if (inner_loop_vinfo) destroy_loop_vec_info (inner_loop_vinfo, true); return NULL; } if (!number_of_iterations) { if (vect_print_dump_info (REPORT_BAD_FORM_LOOPS)) fprintf (vect_dump, "not vectorized: number of iterations cannot be computed."); if (inner_loop_vinfo) destroy_loop_vec_info (inner_loop_vinfo, true); return NULL; } if (chrec_contains_undetermined (number_of_iterations)) { if (vect_print_dump_info (REPORT_BAD_FORM_LOOPS)) fprintf (vect_dump, "Infinite number of iterations."); if (inner_loop_vinfo) destroy_loop_vec_info (inner_loop_vinfo, true); return NULL; } if (!NITERS_KNOWN_P (number_of_iterations)) { if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "Symbolic number of iterations is "); print_generic_expr (vect_dump, number_of_iterations, TDF_DETAILS); } } else if (TREE_INT_CST_LOW (number_of_iterations) == 0) { if (vect_print_dump_info (REPORT_UNVECTORIZED_LOCATIONS)) fprintf (vect_dump, "not vectorized: number of iterations = 0."); if (inner_loop_vinfo) destroy_loop_vec_info (inner_loop_vinfo, false); return NULL; } loop_vinfo = new_loop_vec_info (loop); LOOP_VINFO_NITERS (loop_vinfo) = number_of_iterations; LOOP_VINFO_NITERS_UNCHANGED (loop_vinfo) = number_of_iterations; STMT_VINFO_TYPE (vinfo_for_stmt (loop_cond)) = loop_exit_ctrl_vec_info_type; /* CHECKME: May want to keep it around it in the future. */ if (inner_loop_vinfo) destroy_loop_vec_info (inner_loop_vinfo, false); gcc_assert (!loop->aux); loop->aux = loop_vinfo; return loop_vinfo; } /* Get cost by calling cost target builtin. */ static inline int vect_get_cost (enum vect_cost_for_stmt type_of_cost) { tree dummy_type = NULL; int dummy = 0; return targetm.vectorize.builtin_vectorization_cost (type_of_cost, dummy_type, dummy); } /* Function vect_analyze_loop_operations. Scan the loop stmts and make sure they are all vectorizable. */ static bool vect_analyze_loop_operations (loop_vec_info loop_vinfo, bool slp) { struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo); int nbbs = loop->num_nodes; gimple_stmt_iterator si; unsigned int vectorization_factor = 0; int i; gimple phi; stmt_vec_info stmt_info; bool need_to_vectorize = false; int min_profitable_iters; int min_scalar_loop_bound; unsigned int th; bool only_slp_in_loop = true, ok; if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "=== vect_analyze_loop_operations ==="); gcc_assert (LOOP_VINFO_VECT_FACTOR (loop_vinfo)); vectorization_factor = LOOP_VINFO_VECT_FACTOR (loop_vinfo); if (slp) { /* If all the stmts in the loop can be SLPed, we perform only SLP, and vectorization factor of the loop is the unrolling factor required by the SLP instances. If that unrolling factor is 1, we say, that we perform pure SLP on loop - cross iteration parallelism is not exploited. */ for (i = 0; i < nbbs; i++) { basic_block bb = bbs[i]; for (si = gsi_start_bb (bb); !gsi_end_p (si); gsi_next (&si)) { gimple stmt = gsi_stmt (si); stmt_vec_info stmt_info = vinfo_for_stmt (stmt); gcc_assert (stmt_info); if ((STMT_VINFO_RELEVANT_P (stmt_info) || VECTORIZABLE_CYCLE_DEF (STMT_VINFO_DEF_TYPE (stmt_info))) && !PURE_SLP_STMT (stmt_info)) /* STMT needs both SLP and loop-based vectorization. */ only_slp_in_loop = false; } } if (only_slp_in_loop) vectorization_factor = LOOP_VINFO_SLP_UNROLLING_FACTOR (loop_vinfo); else vectorization_factor = least_common_multiple (vectorization_factor, LOOP_VINFO_SLP_UNROLLING_FACTOR (loop_vinfo)); LOOP_VINFO_VECT_FACTOR (loop_vinfo) = vectorization_factor; if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "Updating vectorization factor to %d ", vectorization_factor); } for (i = 0; i < nbbs; i++) { basic_block bb = bbs[i]; for (si = gsi_start_phis (bb); !gsi_end_p (si); gsi_next (&si)) { phi = gsi_stmt (si); ok = true; stmt_info = vinfo_for_stmt (phi); if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "examining phi: "); print_gimple_stmt (vect_dump, phi, 0, TDF_SLIM); } /* Inner-loop loop-closed exit phi in outer-loop vectorization (i.e., a phi in the tail of the outer-loop). */ if (! is_loop_header_bb_p (bb)) { /* FORNOW: we currently don't support the case that these phis are not used in the outerloop (unless it is double reduction, i.e., this phi is vect_reduction_def), cause this case requires to actually do something here. */ if ((!STMT_VINFO_RELEVANT_P (stmt_info) || STMT_VINFO_LIVE_P (stmt_info)) && STMT_VINFO_DEF_TYPE (stmt_info) != vect_double_reduction_def) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "Unsupported loop-closed phi in outer-loop."); return false; } /* If PHI is used in the outer loop, we check that its operand is defined in the inner loop. */ if (STMT_VINFO_RELEVANT_P (stmt_info)) { tree phi_op; gimple op_def_stmt; if (gimple_phi_num_args (phi) != 1) return false; phi_op = PHI_ARG_DEF (phi, 0); if (TREE_CODE (phi_op) != SSA_NAME) return false; op_def_stmt = SSA_NAME_DEF_STMT (phi_op); if (!op_def_stmt || !vinfo_for_stmt (op_def_stmt)) return false; if (STMT_VINFO_RELEVANT (vinfo_for_stmt (op_def_stmt)) != vect_used_in_outer && STMT_VINFO_RELEVANT (vinfo_for_stmt (op_def_stmt)) != vect_used_in_outer_by_reduction) return false; } continue; } gcc_assert (stmt_info); if (STMT_VINFO_LIVE_P (stmt_info)) { /* FORNOW: not yet supported. */ if (vect_print_dump_info (REPORT_UNVECTORIZED_LOCATIONS)) fprintf (vect_dump, "not vectorized: value used after loop."); return false; } if (STMT_VINFO_RELEVANT (stmt_info) == vect_used_in_scope && STMT_VINFO_DEF_TYPE (stmt_info) != vect_induction_def) { /* A scalar-dependence cycle that we don't support. */ if (vect_print_dump_info (REPORT_UNVECTORIZED_LOCATIONS)) fprintf (vect_dump, "not vectorized: scalar dependence cycle."); return false; } if (STMT_VINFO_RELEVANT_P (stmt_info)) { need_to_vectorize = true; if (STMT_VINFO_DEF_TYPE (stmt_info) == vect_induction_def) ok = vectorizable_induction (phi, NULL, NULL); } if (!ok) { if (vect_print_dump_info (REPORT_UNVECTORIZED_LOCATIONS)) { fprintf (vect_dump, "not vectorized: relevant phi not supported: "); print_gimple_stmt (vect_dump, phi, 0, TDF_SLIM); } return false; } } for (si = gsi_start_bb (bb); !gsi_end_p (si); gsi_next (&si)) { gimple stmt = gsi_stmt (si); if (!vect_analyze_stmt (stmt, &need_to_vectorize, NULL)) return false; } } /* bbs */ /* All operations in the loop are either irrelevant (deal with loop control, or dead), or only used outside the loop and can be moved out of the loop (e.g. invariants, inductions). The loop can be optimized away by scalar optimizations. We're better off not touching this loop. */ if (!need_to_vectorize) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "All the computation can be taken out of the loop."); if (vect_print_dump_info (REPORT_UNVECTORIZED_LOCATIONS)) fprintf (vect_dump, "not vectorized: redundant loop. no profit to vectorize."); return false; } if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo) && vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "vectorization_factor = %d, niters = " HOST_WIDE_INT_PRINT_DEC, vectorization_factor, LOOP_VINFO_INT_NITERS (loop_vinfo)); if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo) && (LOOP_VINFO_INT_NITERS (loop_vinfo) < vectorization_factor)) { if (vect_print_dump_info (REPORT_UNVECTORIZED_LOCATIONS)) fprintf (vect_dump, "not vectorized: iteration count too small."); if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump,"not vectorized: iteration count smaller than " "vectorization factor."); return false; } /* Analyze cost. Decide if worth while to vectorize. */ /* Once VF is set, SLP costs should be updated since the number of created vector stmts depends on VF. */ vect_update_slp_costs_according_to_vf (loop_vinfo); min_profitable_iters = vect_estimate_min_profitable_iters (loop_vinfo); LOOP_VINFO_COST_MODEL_MIN_ITERS (loop_vinfo) = min_profitable_iters; if (min_profitable_iters < 0) { if (vect_print_dump_info (REPORT_UNVECTORIZED_LOCATIONS)) fprintf (vect_dump, "not vectorized: vectorization not profitable."); if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "not vectorized: vector version will never be " "profitable."); return false; } min_scalar_loop_bound = ((PARAM_VALUE (PARAM_MIN_VECT_LOOP_BOUND) * vectorization_factor) - 1); /* Use the cost model only if it is more conservative than user specified threshold. */ th = (unsigned) min_scalar_loop_bound; if (min_profitable_iters && (!min_scalar_loop_bound || min_profitable_iters > min_scalar_loop_bound)) th = (unsigned) min_profitable_iters; if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo) && LOOP_VINFO_INT_NITERS (loop_vinfo) <= th) { if (vect_print_dump_info (REPORT_UNVECTORIZED_LOCATIONS)) fprintf (vect_dump, "not vectorized: vectorization not " "profitable."); if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "not vectorized: iteration count smaller than " "user specified loop bound parameter or minimum " "profitable iterations (whichever is more conservative)."); return false; } if (!LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo) || LOOP_VINFO_INT_NITERS (loop_vinfo) % vectorization_factor != 0 || LOOP_PEELING_FOR_ALIGNMENT (loop_vinfo)) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "epilog loop required."); if (!vect_can_advance_ivs_p (loop_vinfo)) { if (vect_print_dump_info (REPORT_UNVECTORIZED_LOCATIONS)) fprintf (vect_dump, "not vectorized: can't create epilog loop 1."); return false; } if (!slpeel_can_duplicate_loop_p (loop, single_exit (loop))) { if (vect_print_dump_info (REPORT_UNVECTORIZED_LOCATIONS)) fprintf (vect_dump, "not vectorized: can't create epilog loop 2."); return false; } } return true; } /* Function vect_analyze_loop_2. Apply a set of analyses on LOOP, and create a loop_vec_info struct for it. The different analyses will record information in the loop_vec_info struct. */ static bool vect_analyze_loop_2 (loop_vec_info loop_vinfo) { bool ok, slp = false; int max_vf = MAX_VECTORIZATION_FACTOR; int min_vf = 2; /* Find all data references in the loop (which correspond to vdefs/vuses) and analyze their evolution in the loop. Also adjust the minimal vectorization factor according to the loads and stores. FORNOW: Handle only simple, array references, which alignment can be forced, and aligned pointer-references. */ ok = vect_analyze_data_refs (loop_vinfo, NULL, &min_vf); if (!ok) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "bad data references."); return false; } /* Classify all cross-iteration scalar data-flow cycles. Cross-iteration cycles caused by virtual phis are analyzed separately. */ vect_analyze_scalar_cycles (loop_vinfo); vect_pattern_recog (loop_vinfo); /* Data-flow analysis to detect stmts that do not need to be vectorized. */ ok = vect_mark_stmts_to_be_vectorized (loop_vinfo); if (!ok) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "unexpected pattern."); return false; } /* Analyze data dependences between the data-refs in the loop and adjust the maximum vectorization factor according to the dependences. FORNOW: fail at the first data dependence that we encounter. */ ok = vect_analyze_data_ref_dependences (loop_vinfo, NULL, &max_vf); if (!ok || max_vf < min_vf) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "bad data dependence."); return false; } ok = vect_determine_vectorization_factor (loop_vinfo); if (!ok) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "can't determine vectorization factor."); return false; } if (max_vf < LOOP_VINFO_VECT_FACTOR (loop_vinfo)) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "bad data dependence."); return false; } /* Analyze the alignment of the data-refs in the loop. Fail if a data reference is found that cannot be vectorized. */ ok = vect_analyze_data_refs_alignment (loop_vinfo, NULL); if (!ok) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "bad data alignment."); return false; } /* Analyze the access patterns of the data-refs in the loop (consecutive, complex, etc.). FORNOW: Only handle consecutive access pattern. */ ok = vect_analyze_data_ref_accesses (loop_vinfo, NULL); if (!ok) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "bad data access."); return false; } /* Prune the list of ddrs to be tested at run-time by versioning for alias. It is important to call pruning after vect_analyze_data_ref_accesses, since we use grouping information gathered by interleaving analysis. */ ok = vect_prune_runtime_alias_test_list (loop_vinfo); if (!ok) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "too long list of versioning for alias " "run-time tests."); return false; } /* This pass will decide on using loop versioning and/or loop peeling in order to enhance the alignment of data references in the loop. */ ok = vect_enhance_data_refs_alignment (loop_vinfo); if (!ok) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "bad data alignment."); return false; } /* Check the SLP opportunities in the loop, analyze and build SLP trees. */ ok = vect_analyze_slp (loop_vinfo, NULL); if (ok) { /* Decide which possible SLP instances to SLP. */ slp = vect_make_slp_decision (loop_vinfo); /* Find stmts that need to be both vectorized and SLPed. */ vect_detect_hybrid_slp (loop_vinfo); } else return false; /* Scan all the operations in the loop and make sure they are vectorizable. */ ok = vect_analyze_loop_operations (loop_vinfo, slp); if (!ok) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "bad operation or unsupported loop bound."); return false; } return true; } /* Function vect_analyze_loop. Apply a set of analyses on LOOP, and create a loop_vec_info struct for it. The different analyses will record information in the loop_vec_info struct. */ loop_vec_info vect_analyze_loop (struct loop *loop) { loop_vec_info loop_vinfo; unsigned int vector_sizes; /* Autodetect first vector size we try. */ current_vector_size = 0; vector_sizes = targetm.vectorize.autovectorize_vector_sizes (); if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "===== analyze_loop_nest ====="); if (loop_outer (loop) && loop_vec_info_for_loop (loop_outer (loop)) && LOOP_VINFO_VECTORIZABLE_P (loop_vec_info_for_loop (loop_outer (loop)))) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "outer-loop already vectorized."); return NULL; } while (1) { /* Check the CFG characteristics of the loop (nesting, entry/exit). */ loop_vinfo = vect_analyze_loop_form (loop); if (!loop_vinfo) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "bad loop form."); return NULL; } if (vect_analyze_loop_2 (loop_vinfo)) { LOOP_VINFO_VECTORIZABLE_P (loop_vinfo) = 1; return loop_vinfo; } destroy_loop_vec_info (loop_vinfo, true); vector_sizes &= ~current_vector_size; if (vector_sizes == 0 || current_vector_size == 0) return NULL; /* Try the next biggest vector size. */ current_vector_size = 1 << floor_log2 (vector_sizes); if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "***** Re-trying analysis with " "vector size %d\n", current_vector_size); } } /* Function reduction_code_for_scalar_code Input: CODE - tree_code of a reduction operations. Output: REDUC_CODE - the corresponding tree-code to be used to reduce the vector of partial results into a single scalar result (which will also reside in a vector) or ERROR_MARK if the operation is a supported reduction operation, but does not have such tree-code. Return FALSE if CODE currently cannot be vectorized as reduction. */ static bool reduction_code_for_scalar_code (enum tree_code code, enum tree_code *reduc_code) { switch (code) { case MAX_EXPR: *reduc_code = REDUC_MAX_EXPR; return true; case MIN_EXPR: *reduc_code = REDUC_MIN_EXPR; return true; case PLUS_EXPR: *reduc_code = REDUC_PLUS_EXPR; return true; case MULT_EXPR: case MINUS_EXPR: case BIT_IOR_EXPR: case BIT_XOR_EXPR: case BIT_AND_EXPR: *reduc_code = ERROR_MARK; return true; default: return false; } } /* Error reporting helper for vect_is_simple_reduction below. GIMPLE statement STMT is printed with a message MSG. */ static void report_vect_op (gimple stmt, const char *msg) { fprintf (vect_dump, "%s", msg); print_gimple_stmt (vect_dump, stmt, 0, TDF_SLIM); } /* Detect SLP reduction of the form: #a1 = phi <a5, a0> a2 = operation (a1) a3 = operation (a2) a4 = operation (a3) a5 = operation (a4) #a = phi <a5> PHI is the reduction phi node (#a1 = phi <a5, a0> above) FIRST_STMT is the first reduction stmt in the chain (a2 = operation (a1)). Return TRUE if a reduction chain was detected. */ static bool vect_is_slp_reduction (loop_vec_info loop_info, gimple phi, gimple first_stmt) { struct loop *loop = (gimple_bb (phi))->loop_father; struct loop *vect_loop = LOOP_VINFO_LOOP (loop_info); enum tree_code code; gimple current_stmt = NULL, loop_use_stmt = NULL, first, next_stmt; stmt_vec_info use_stmt_info, current_stmt_info; tree lhs; imm_use_iterator imm_iter; use_operand_p use_p; int nloop_uses, size = 0, n_out_of_loop_uses; bool found = false; if (loop != vect_loop) return false; lhs = PHI_RESULT (phi); code = gimple_assign_rhs_code (first_stmt); while (1) { nloop_uses = 0; n_out_of_loop_uses = 0; FOR_EACH_IMM_USE_FAST (use_p, imm_iter, lhs) { gimple use_stmt = USE_STMT (use_p); if (is_gimple_debug (use_stmt)) continue; use_stmt = USE_STMT (use_p); /* Check if we got back to the reduction phi. */ if (use_stmt == phi) { loop_use_stmt = use_stmt; found = true; break; } if (flow_bb_inside_loop_p (loop, gimple_bb (use_stmt))) { if (vinfo_for_stmt (use_stmt) && !STMT_VINFO_IN_PATTERN_P (vinfo_for_stmt (use_stmt))) { loop_use_stmt = use_stmt; nloop_uses++; } } else n_out_of_loop_uses++; /* There are can be either a single use in the loop or two uses in phi nodes. */ if (nloop_uses > 1 || (n_out_of_loop_uses && nloop_uses)) return false; } if (found) break; /* We reached a statement with no loop uses. */ if (nloop_uses == 0) return false; /* This is a loop exit phi, and we haven't reached the reduction phi. */ if (gimple_code (loop_use_stmt) == GIMPLE_PHI) return false; if (!is_gimple_assign (loop_use_stmt) || code != gimple_assign_rhs_code (loop_use_stmt) || !flow_bb_inside_loop_p (loop, gimple_bb (loop_use_stmt))) return false; /* Insert USE_STMT into reduction chain. */ use_stmt_info = vinfo_for_stmt (loop_use_stmt); if (current_stmt) { current_stmt_info = vinfo_for_stmt (current_stmt); GROUP_NEXT_ELEMENT (current_stmt_info) = loop_use_stmt; GROUP_FIRST_ELEMENT (use_stmt_info) = GROUP_FIRST_ELEMENT (current_stmt_info); } else GROUP_FIRST_ELEMENT (use_stmt_info) = loop_use_stmt; lhs = gimple_assign_lhs (loop_use_stmt); current_stmt = loop_use_stmt; size++; } if (!found || loop_use_stmt != phi || size < 2) return false; /* Swap the operands, if needed, to make the reduction operand be the second operand. */ lhs = PHI_RESULT (phi); next_stmt = GROUP_FIRST_ELEMENT (vinfo_for_stmt (current_stmt)); while (next_stmt) { if (gimple_assign_rhs2 (next_stmt) == lhs) { tree op = gimple_assign_rhs1 (next_stmt); gimple def_stmt = NULL; if (TREE_CODE (op) == SSA_NAME) def_stmt = SSA_NAME_DEF_STMT (op); /* Check that the other def is either defined in the loop ("vect_internal_def"), or it's an induction (defined by a loop-header phi-node). */ if (def_stmt && gimple_bb (def_stmt) && flow_bb_inside_loop_p (loop, gimple_bb (def_stmt)) && (is_gimple_assign (def_stmt) || is_gimple_call (def_stmt) || STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def_stmt)) == vect_induction_def || (gimple_code (def_stmt) == GIMPLE_PHI && STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def_stmt)) == vect_internal_def && !is_loop_header_bb_p (gimple_bb (def_stmt))))) { lhs = gimple_assign_lhs (next_stmt); next_stmt = GROUP_NEXT_ELEMENT (vinfo_for_stmt (next_stmt)); continue; } return false; } else { tree op = gimple_assign_rhs2 (next_stmt); gimple def_stmt = NULL; if (TREE_CODE (op) == SSA_NAME) def_stmt = SSA_NAME_DEF_STMT (op); /* Check that the other def is either defined in the loop ("vect_internal_def"), or it's an induction (defined by a loop-header phi-node). */ if (def_stmt && gimple_bb (def_stmt) && flow_bb_inside_loop_p (loop, gimple_bb (def_stmt)) && (is_gimple_assign (def_stmt) || is_gimple_call (def_stmt) || STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def_stmt)) == vect_induction_def || (gimple_code (def_stmt) == GIMPLE_PHI && STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def_stmt)) == vect_internal_def && !is_loop_header_bb_p (gimple_bb (def_stmt))))) { if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "swapping oprnds: "); print_gimple_stmt (vect_dump, next_stmt, 0, TDF_SLIM); } swap_tree_operands (next_stmt, gimple_assign_rhs1_ptr (next_stmt), gimple_assign_rhs2_ptr (next_stmt)); mark_symbols_for_renaming (next_stmt); } else return false; } lhs = gimple_assign_lhs (next_stmt); next_stmt = GROUP_NEXT_ELEMENT (vinfo_for_stmt (next_stmt)); } /* Save the chain for further analysis in SLP detection. */ first = GROUP_FIRST_ELEMENT (vinfo_for_stmt (current_stmt)); VEC_safe_push (gimple, heap, LOOP_VINFO_REDUCTION_CHAINS (loop_info), first); GROUP_SIZE (vinfo_for_stmt (first)) = size; return true; } /* Function vect_is_simple_reduction_1 (1) Detect a cross-iteration def-use cycle that represents a simple reduction computation. We look for the following pattern: loop_header: a1 = phi < a0, a2 > a3 = ... a2 = operation (a3, a1) such that: 1. operation is commutative and associative and it is safe to change the order of the computation (if CHECK_REDUCTION is true) 2. no uses for a2 in the loop (a2 is used out of the loop) 3. no uses of a1 in the loop besides the reduction operation 4. no uses of a1 outside the loop. Conditions 1,4 are tested here. Conditions 2,3 are tested in vect_mark_stmts_to_be_vectorized. (2) Detect a cross-iteration def-use cycle in nested loops, i.e., nested cycles, if CHECK_REDUCTION is false. (3) Detect cycles of phi nodes in outer-loop vectorization, i.e., double reductions: a1 = phi < a0, a2 > inner loop (def of a3) a2 = phi < a3 > If MODIFY is true it tries also to rework the code in-place to enable detection of more reduction patterns. For the time being we rewrite "res -= RHS" into "rhs += -RHS" when it seems worthwhile. */ static gimple vect_is_simple_reduction_1 (loop_vec_info loop_info, gimple phi, bool check_reduction, bool *double_reduc, bool modify) { struct loop *loop = (gimple_bb (phi))->loop_father; struct loop *vect_loop = LOOP_VINFO_LOOP (loop_info); edge latch_e = loop_latch_edge (loop); tree loop_arg = PHI_ARG_DEF_FROM_EDGE (phi, latch_e); gimple def_stmt, def1 = NULL, def2 = NULL; enum tree_code orig_code, code; tree op1, op2, op3 = NULL_TREE, op4 = NULL_TREE; tree type; int nloop_uses; tree name; imm_use_iterator imm_iter; use_operand_p use_p; bool phi_def; *double_reduc = false; /* If CHECK_REDUCTION is true, we assume inner-most loop vectorization, otherwise, we assume outer loop vectorization. */ gcc_assert ((check_reduction && loop == vect_loop) || (!check_reduction && flow_loop_nested_p (vect_loop, loop))); name = PHI_RESULT (phi); nloop_uses = 0; FOR_EACH_IMM_USE_FAST (use_p, imm_iter, name) { gimple use_stmt = USE_STMT (use_p); if (is_gimple_debug (use_stmt)) continue; if (!flow_bb_inside_loop_p (loop, gimple_bb (use_stmt))) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "intermediate value used outside loop."); return NULL; } if (vinfo_for_stmt (use_stmt) && !is_pattern_stmt_p (vinfo_for_stmt (use_stmt))) nloop_uses++; if (nloop_uses > 1) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "reduction used in loop."); return NULL; } } if (TREE_CODE (loop_arg) != SSA_NAME) { if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "reduction: not ssa_name: "); print_generic_expr (vect_dump, loop_arg, TDF_SLIM); } return NULL; } def_stmt = SSA_NAME_DEF_STMT (loop_arg); if (!def_stmt) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "reduction: no def_stmt."); return NULL; } if (!is_gimple_assign (def_stmt) && gimple_code (def_stmt) != GIMPLE_PHI) { if (vect_print_dump_info (REPORT_DETAILS)) print_gimple_stmt (vect_dump, def_stmt, 0, TDF_SLIM); return NULL; } if (is_gimple_assign (def_stmt)) { name = gimple_assign_lhs (def_stmt); phi_def = false; } else { name = PHI_RESULT (def_stmt); phi_def = true; } nloop_uses = 0; FOR_EACH_IMM_USE_FAST (use_p, imm_iter, name) { gimple use_stmt = USE_STMT (use_p); if (is_gimple_debug (use_stmt)) continue; if (flow_bb_inside_loop_p (loop, gimple_bb (use_stmt)) && vinfo_for_stmt (use_stmt) && !is_pattern_stmt_p (vinfo_for_stmt (use_stmt))) nloop_uses++; if (nloop_uses > 1) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "reduction used in loop."); return NULL; } } /* If DEF_STMT is a phi node itself, we expect it to have a single argument defined in the inner loop. */ if (phi_def) { op1 = PHI_ARG_DEF (def_stmt, 0); if (gimple_phi_num_args (def_stmt) != 1 || TREE_CODE (op1) != SSA_NAME) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "unsupported phi node definition."); return NULL; } def1 = SSA_NAME_DEF_STMT (op1); if (flow_bb_inside_loop_p (loop, gimple_bb (def_stmt)) && loop->inner && flow_bb_inside_loop_p (loop->inner, gimple_bb (def1)) && is_gimple_assign (def1)) { if (vect_print_dump_info (REPORT_DETAILS)) report_vect_op (def_stmt, "detected double reduction: "); *double_reduc = true; return def_stmt; } return NULL; } code = orig_code = gimple_assign_rhs_code (def_stmt); /* We can handle "res -= x[i]", which is non-associative by simply rewriting this into "res += -x[i]". Avoid changing gimple instruction for the first simple tests and only do this if we're allowed to change code at all. */ if (code == MINUS_EXPR && modify && (op1 = gimple_assign_rhs1 (def_stmt)) && TREE_CODE (op1) == SSA_NAME && SSA_NAME_DEF_STMT (op1) == phi) code = PLUS_EXPR; if (check_reduction && (!commutative_tree_code (code) || !associative_tree_code (code))) { if (vect_print_dump_info (REPORT_DETAILS)) report_vect_op (def_stmt, "reduction: not commutative/associative: "); return NULL; } if (get_gimple_rhs_class (code) != GIMPLE_BINARY_RHS) { if (code != COND_EXPR) { if (vect_print_dump_info (REPORT_DETAILS)) report_vect_op (def_stmt, "reduction: not binary operation: "); return NULL; } op3 = gimple_assign_rhs1 (def_stmt); if (COMPARISON_CLASS_P (op3)) { op4 = TREE_OPERAND (op3, 1); op3 = TREE_OPERAND (op3, 0); } op1 = gimple_assign_rhs2 (def_stmt); op2 = gimple_assign_rhs3 (def_stmt); if (TREE_CODE (op1) != SSA_NAME && TREE_CODE (op2) != SSA_NAME) { if (vect_print_dump_info (REPORT_DETAILS)) report_vect_op (def_stmt, "reduction: uses not ssa_names: "); return NULL; } } else { op1 = gimple_assign_rhs1 (def_stmt); op2 = gimple_assign_rhs2 (def_stmt); if (TREE_CODE (op1) != SSA_NAME && TREE_CODE (op2) != SSA_NAME) { if (vect_print_dump_info (REPORT_DETAILS)) report_vect_op (def_stmt, "reduction: uses not ssa_names: "); return NULL; } } type = TREE_TYPE (gimple_assign_lhs (def_stmt)); if ((TREE_CODE (op1) == SSA_NAME && !types_compatible_p (type,TREE_TYPE (op1))) || (TREE_CODE (op2) == SSA_NAME && !types_compatible_p (type, TREE_TYPE (op2))) || (op3 && TREE_CODE (op3) == SSA_NAME && !types_compatible_p (type, TREE_TYPE (op3))) || (op4 && TREE_CODE (op4) == SSA_NAME && !types_compatible_p (type, TREE_TYPE (op4)))) { if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "reduction: multiple types: operation type: "); print_generic_expr (vect_dump, type, TDF_SLIM); fprintf (vect_dump, ", operands types: "); print_generic_expr (vect_dump, TREE_TYPE (op1), TDF_SLIM); fprintf (vect_dump, ","); print_generic_expr (vect_dump, TREE_TYPE (op2), TDF_SLIM); if (op3) { fprintf (vect_dump, ","); print_generic_expr (vect_dump, TREE_TYPE (op3), TDF_SLIM); } if (op4) { fprintf (vect_dump, ","); print_generic_expr (vect_dump, TREE_TYPE (op4), TDF_SLIM); } } return NULL; } /* Check that it's ok to change the order of the computation. Generally, when vectorizing a reduction we change the order of the computation. This may change the behavior of the program in some cases, so we need to check that this is ok. One exception is when vectorizing an outer-loop: the inner-loop is executed sequentially, and therefore vectorizing reductions in the inner-loop during outer-loop vectorization is safe. */ /* CHECKME: check for !flag_finite_math_only too? */ if (SCALAR_FLOAT_TYPE_P (type) && !flag_associative_math && check_reduction) { /* Changing the order of operations changes the semantics. */ if (vect_print_dump_info (REPORT_DETAILS)) report_vect_op (def_stmt, "reduction: unsafe fp math optimization: "); return NULL; } else if (INTEGRAL_TYPE_P (type) && TYPE_OVERFLOW_TRAPS (type) && check_reduction) { /* Changing the order of operations changes the semantics. */ if (vect_print_dump_info (REPORT_DETAILS)) report_vect_op (def_stmt, "reduction: unsafe int math optimization: "); return NULL; } else if (SAT_FIXED_POINT_TYPE_P (type) && check_reduction) { /* Changing the order of operations changes the semantics. */ if (vect_print_dump_info (REPORT_DETAILS)) report_vect_op (def_stmt, "reduction: unsafe fixed-point math optimization: "); return NULL; } /* If we detected "res -= x[i]" earlier, rewrite it into "res += -x[i]" now. If this turns out to be useless reassoc will clean it up again. */ if (orig_code == MINUS_EXPR) { tree rhs = gimple_assign_rhs2 (def_stmt); tree negrhs = make_ssa_name (SSA_NAME_VAR (rhs), NULL); gimple negate_stmt = gimple_build_assign_with_ops (NEGATE_EXPR, negrhs, rhs, NULL); gimple_stmt_iterator gsi = gsi_for_stmt (def_stmt); set_vinfo_for_stmt (negate_stmt, new_stmt_vec_info (negate_stmt, loop_info, NULL)); gsi_insert_before (&gsi, negate_stmt, GSI_NEW_STMT); gimple_assign_set_rhs2 (def_stmt, negrhs); gimple_assign_set_rhs_code (def_stmt, PLUS_EXPR); update_stmt (def_stmt); } /* Reduction is safe. We're dealing with one of the following: 1) integer arithmetic and no trapv 2) floating point arithmetic, and special flags permit this optimization 3) nested cycle (i.e., outer loop vectorization). */ if (TREE_CODE (op1) == SSA_NAME) def1 = SSA_NAME_DEF_STMT (op1); if (TREE_CODE (op2) == SSA_NAME) def2 = SSA_NAME_DEF_STMT (op2); if (code != COND_EXPR && ((!def1 || gimple_nop_p (def1)) && (!def2 || gimple_nop_p (def2)))) { if (vect_print_dump_info (REPORT_DETAILS)) report_vect_op (def_stmt, "reduction: no defs for operands: "); return NULL; } /* Check that one def is the reduction def, defined by PHI, the other def is either defined in the loop ("vect_internal_def"), or it's an induction (defined by a loop-header phi-node). */ if (def2 && def2 == phi && (code == COND_EXPR || !def1 || gimple_nop_p (def1) || (def1 && flow_bb_inside_loop_p (loop, gimple_bb (def1)) && (is_gimple_assign (def1) || is_gimple_call (def1) || STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def1)) == vect_induction_def || (gimple_code (def1) == GIMPLE_PHI && STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def1)) == vect_internal_def && !is_loop_header_bb_p (gimple_bb (def1))))))) { if (vect_print_dump_info (REPORT_DETAILS)) report_vect_op (def_stmt, "detected reduction: "); return def_stmt; } if (def1 && def1 == phi && (code == COND_EXPR || !def2 || gimple_nop_p (def2) || (def2 && flow_bb_inside_loop_p (loop, gimple_bb (def2)) && (is_gimple_assign (def2) || is_gimple_call (def2) || STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def2)) == vect_induction_def || (gimple_code (def2) == GIMPLE_PHI && STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def2)) == vect_internal_def && !is_loop_header_bb_p (gimple_bb (def2))))))) { if (check_reduction) { /* Swap operands (just for simplicity - so that the rest of the code can assume that the reduction variable is always the last (second) argument). */ if (vect_print_dump_info (REPORT_DETAILS)) report_vect_op (def_stmt, "detected reduction: need to swap operands: "); swap_tree_operands (def_stmt, gimple_assign_rhs1_ptr (def_stmt), gimple_assign_rhs2_ptr (def_stmt)); } else { if (vect_print_dump_info (REPORT_DETAILS)) report_vect_op (def_stmt, "detected reduction: "); } return def_stmt; } /* Try to find SLP reduction chain. */ if (check_reduction && vect_is_slp_reduction (loop_info, phi, def_stmt)) { if (vect_print_dump_info (REPORT_DETAILS)) report_vect_op (def_stmt, "reduction: detected reduction chain: "); return def_stmt; } if (vect_print_dump_info (REPORT_DETAILS)) report_vect_op (def_stmt, "reduction: unknown pattern: "); return NULL; } /* Wrapper around vect_is_simple_reduction_1, that won't modify code in-place. Arguments as there. */ static gimple vect_is_simple_reduction (loop_vec_info loop_info, gimple phi, bool check_reduction, bool *double_reduc) { return vect_is_simple_reduction_1 (loop_info, phi, check_reduction, double_reduc, false); } /* Wrapper around vect_is_simple_reduction_1, which will modify code in-place if it enables detection of more reductions. Arguments as there. */ gimple vect_force_simple_reduction (loop_vec_info loop_info, gimple phi, bool check_reduction, bool *double_reduc) { return vect_is_simple_reduction_1 (loop_info, phi, check_reduction, double_reduc, true); } /* Calculate the cost of one scalar iteration of the loop. */ int vect_get_single_scalar_iteraion_cost (loop_vec_info loop_vinfo) { struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo); int nbbs = loop->num_nodes, factor, scalar_single_iter_cost = 0; int innerloop_iters, i, stmt_cost; /* Count statements in scalar loop. Using this as scalar cost for a single iteration for now. TODO: Add outer loop support. TODO: Consider assigning different costs to different scalar statements. */ /* FORNOW. */ innerloop_iters = 1; if (loop->inner) innerloop_iters = 50; /* FIXME */ for (i = 0; i < nbbs; i++) { gimple_stmt_iterator si; basic_block bb = bbs[i]; if (bb->loop_father == loop->inner) factor = innerloop_iters; else factor = 1; for (si = gsi_start_bb (bb); !gsi_end_p (si); gsi_next (&si)) { gimple stmt = gsi_stmt (si); stmt_vec_info stmt_info = vinfo_for_stmt (stmt); if (!is_gimple_assign (stmt) && !is_gimple_call (stmt)) continue; /* Skip stmts that are not vectorized inside the loop. */ if (stmt_info && !STMT_VINFO_RELEVANT_P (stmt_info) && (!STMT_VINFO_LIVE_P (stmt_info) || !VECTORIZABLE_CYCLE_DEF (STMT_VINFO_DEF_TYPE (stmt_info))) && !STMT_VINFO_IN_PATTERN_P (stmt_info)) continue; if (STMT_VINFO_DATA_REF (vinfo_for_stmt (stmt))) { if (DR_IS_READ (STMT_VINFO_DATA_REF (vinfo_for_stmt (stmt)))) stmt_cost = vect_get_cost (scalar_load); else stmt_cost = vect_get_cost (scalar_store); } else stmt_cost = vect_get_cost (scalar_stmt); scalar_single_iter_cost += stmt_cost * factor; } } return scalar_single_iter_cost; } /* Calculate cost of peeling the loop PEEL_ITERS_PROLOGUE times. */ int vect_get_known_peeling_cost (loop_vec_info loop_vinfo, int peel_iters_prologue, int *peel_iters_epilogue, int scalar_single_iter_cost) { int peel_guard_costs = 0; int vf = LOOP_VINFO_VECT_FACTOR (loop_vinfo); if (!LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo)) { *peel_iters_epilogue = vf/2; if (vect_print_dump_info (REPORT_COST)) fprintf (vect_dump, "cost model: " "epilogue peel iters set to vf/2 because " "loop iterations are unknown ."); /* If peeled iterations are known but number of scalar loop iterations are unknown, count a taken branch per peeled loop. */ peel_guard_costs = 2 * vect_get_cost (cond_branch_taken); } else { int niters = LOOP_VINFO_INT_NITERS (loop_vinfo); peel_iters_prologue = niters < peel_iters_prologue ? niters : peel_iters_prologue; *peel_iters_epilogue = (niters - peel_iters_prologue) % vf; /* If we need to peel for gaps, but no peeling is required, we have to peel VF iterations. */ if (LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo) && !*peel_iters_epilogue) *peel_iters_epilogue = vf; } return (peel_iters_prologue * scalar_single_iter_cost) + (*peel_iters_epilogue * scalar_single_iter_cost) + peel_guard_costs; } /* Function vect_estimate_min_profitable_iters Return the number of iterations required for the vector version of the loop to be profitable relative to the cost of the scalar version of the loop. TODO: Take profile info into account before making vectorization decisions, if available. */ int vect_estimate_min_profitable_iters (loop_vec_info loop_vinfo) { int i; int min_profitable_iters; int peel_iters_prologue; int peel_iters_epilogue; int vec_inside_cost = 0; int vec_outside_cost = 0; int scalar_single_iter_cost = 0; int scalar_outside_cost = 0; int vf = LOOP_VINFO_VECT_FACTOR (loop_vinfo); struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo); int nbbs = loop->num_nodes; int npeel = LOOP_PEELING_FOR_ALIGNMENT (loop_vinfo); int peel_guard_costs = 0; int innerloop_iters = 0, factor; VEC (slp_instance, heap) *slp_instances; slp_instance instance; /* Cost model disabled. */ if (!flag_vect_cost_model) { if (vect_print_dump_info (REPORT_COST)) fprintf (vect_dump, "cost model disabled."); return 0; } /* Requires loop versioning tests to handle misalignment. */ if (LOOP_REQUIRES_VERSIONING_FOR_ALIGNMENT (loop_vinfo)) { /* FIXME: Make cost depend on complexity of individual check. */ vec_outside_cost += VEC_length (gimple, LOOP_VINFO_MAY_MISALIGN_STMTS (loop_vinfo)); if (vect_print_dump_info (REPORT_COST)) fprintf (vect_dump, "cost model: Adding cost of checks for loop " "versioning to treat misalignment.\n"); } /* Requires loop versioning with alias checks. */ if (LOOP_REQUIRES_VERSIONING_FOR_ALIAS (loop_vinfo)) { /* FIXME: Make cost depend on complexity of individual check. */ vec_outside_cost += VEC_length (ddr_p, LOOP_VINFO_MAY_ALIAS_DDRS (loop_vinfo)); if (vect_print_dump_info (REPORT_COST)) fprintf (vect_dump, "cost model: Adding cost of checks for loop " "versioning aliasing.\n"); } if (LOOP_REQUIRES_VERSIONING_FOR_ALIGNMENT (loop_vinfo) || LOOP_REQUIRES_VERSIONING_FOR_ALIAS (loop_vinfo)) vec_outside_cost += vect_get_cost (cond_branch_taken); /* Count statements in scalar loop. Using this as scalar cost for a single iteration for now. TODO: Add outer loop support. TODO: Consider assigning different costs to different scalar statements. */ /* FORNOW. */ if (loop->inner) innerloop_iters = 50; /* FIXME */ for (i = 0; i < nbbs; i++) { gimple_stmt_iterator si; basic_block bb = bbs[i]; if (bb->loop_father == loop->inner) factor = innerloop_iters; else factor = 1; for (si = gsi_start_bb (bb); !gsi_end_p (si); gsi_next (&si)) { gimple stmt = gsi_stmt (si); stmt_vec_info stmt_info = vinfo_for_stmt (stmt); if (STMT_VINFO_IN_PATTERN_P (stmt_info)) { stmt = STMT_VINFO_RELATED_STMT (stmt_info); stmt_info = vinfo_for_stmt (stmt); } /* Skip stmts that are not vectorized inside the loop. */ if (!STMT_VINFO_RELEVANT_P (stmt_info) && (!STMT_VINFO_LIVE_P (stmt_info) || !VECTORIZABLE_CYCLE_DEF (STMT_VINFO_DEF_TYPE (stmt_info)))) continue; vec_inside_cost += STMT_VINFO_INSIDE_OF_LOOP_COST (stmt_info) * factor; /* FIXME: for stmts in the inner-loop in outer-loop vectorization, some of the "outside" costs are generated inside the outer-loop. */ vec_outside_cost += STMT_VINFO_OUTSIDE_OF_LOOP_COST (stmt_info); if (is_pattern_stmt_p (stmt_info) && STMT_VINFO_PATTERN_DEF_SEQ (stmt_info)) { gimple_stmt_iterator gsi; for (gsi = gsi_start (STMT_VINFO_PATTERN_DEF_SEQ (stmt_info)); !gsi_end_p (gsi); gsi_next (&gsi)) { gimple pattern_def_stmt = gsi_stmt (gsi); stmt_vec_info pattern_def_stmt_info = vinfo_for_stmt (pattern_def_stmt); if (STMT_VINFO_RELEVANT_P (pattern_def_stmt_info) || STMT_VINFO_LIVE_P (pattern_def_stmt_info)) { vec_inside_cost += STMT_VINFO_INSIDE_OF_LOOP_COST (pattern_def_stmt_info) * factor; vec_outside_cost += STMT_VINFO_OUTSIDE_OF_LOOP_COST (pattern_def_stmt_info); } } } } } scalar_single_iter_cost = vect_get_single_scalar_iteraion_cost (loop_vinfo); /* Add additional cost for the peeled instructions in prologue and epilogue loop. FORNOW: If we don't know the value of peel_iters for prologue or epilogue at compile-time - we assume it's vf/2 (the worst would be vf-1). TODO: Build an expression that represents peel_iters for prologue and epilogue to be used in a run-time test. */ if (npeel < 0) { peel_iters_prologue = vf/2; if (vect_print_dump_info (REPORT_COST)) fprintf (vect_dump, "cost model: " "prologue peel iters set to vf/2."); /* If peeling for alignment is unknown, loop bound of main loop becomes unknown. */ peel_iters_epilogue = vf/2; if (vect_print_dump_info (REPORT_COST)) fprintf (vect_dump, "cost model: " "epilogue peel iters set to vf/2 because " "peeling for alignment is unknown ."); /* If peeled iterations are unknown, count a taken branch and a not taken branch per peeled loop. Even if scalar loop iterations are known, vector iterations are not known since peeled prologue iterations are not known. Hence guards remain the same. */ peel_guard_costs += 2 * (vect_get_cost (cond_branch_taken) + vect_get_cost (cond_branch_not_taken)); vec_outside_cost += (peel_iters_prologue * scalar_single_iter_cost) + (peel_iters_epilogue * scalar_single_iter_cost) + peel_guard_costs; } else { peel_iters_prologue = npeel; vec_outside_cost += vect_get_known_peeling_cost (loop_vinfo, peel_iters_prologue, &peel_iters_epilogue, scalar_single_iter_cost); } /* FORNOW: The scalar outside cost is incremented in one of the following ways: 1. The vectorizer checks for alignment and aliasing and generates a condition that allows dynamic vectorization. A cost model check is ANDED with the versioning condition. Hence scalar code path now has the added cost of the versioning check. if (cost > th & versioning_check) jmp to vector code Hence run-time scalar is incremented by not-taken branch cost. 2. The vectorizer then checks if a prologue is required. If the cost model check was not done before during versioning, it has to be done before the prologue check. if (cost <= th) prologue = scalar_iters if (prologue == 0) jmp to vector code else execute prologue if (prologue == num_iters) go to exit Hence the run-time scalar cost is incremented by a taken branch, plus a not-taken branch, plus a taken branch cost. 3. The vectorizer then checks if an epilogue is required. If the cost model check was not done before during prologue check, it has to be done with the epilogue check. if (prologue == 0) jmp to vector code else execute prologue if (prologue == num_iters) go to exit vector code: if ((cost <= th) | (scalar_iters-prologue-epilogue == 0)) jmp to epilogue Hence the run-time scalar cost should be incremented by 2 taken branches. TODO: The back end may reorder the BBS's differently and reverse conditions/branch directions. Change the estimates below to something more reasonable. */ /* If the number of iterations is known and we do not do versioning, we can decide whether to vectorize at compile time. Hence the scalar version do not carry cost model guard costs. */ if (!LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo) || LOOP_REQUIRES_VERSIONING_FOR_ALIGNMENT (loop_vinfo) || LOOP_REQUIRES_VERSIONING_FOR_ALIAS (loop_vinfo)) { /* Cost model check occurs at versioning. */ if (LOOP_REQUIRES_VERSIONING_FOR_ALIGNMENT (loop_vinfo) || LOOP_REQUIRES_VERSIONING_FOR_ALIAS (loop_vinfo)) scalar_outside_cost += vect_get_cost (cond_branch_not_taken); else { /* Cost model check occurs at prologue generation. */ if (LOOP_PEELING_FOR_ALIGNMENT (loop_vinfo) < 0) scalar_outside_cost += 2 * vect_get_cost (cond_branch_taken) + vect_get_cost (cond_branch_not_taken); /* Cost model check occurs at epilogue generation. */ else scalar_outside_cost += 2 * vect_get_cost (cond_branch_taken); } } /* Add SLP costs. */ slp_instances = LOOP_VINFO_SLP_INSTANCES (loop_vinfo); FOR_EACH_VEC_ELT (slp_instance, slp_instances, i, instance) { vec_outside_cost += SLP_INSTANCE_OUTSIDE_OF_LOOP_COST (instance); vec_inside_cost += SLP_INSTANCE_INSIDE_OF_LOOP_COST (instance); } /* Calculate number of iterations required to make the vector version profitable, relative to the loop bodies only. The following condition must hold true: SIC * niters + SOC > VIC * ((niters-PL_ITERS-EP_ITERS)/VF) + VOC where SIC = scalar iteration cost, VIC = vector iteration cost, VOC = vector outside cost, VF = vectorization factor, PL_ITERS = prologue iterations, EP_ITERS= epilogue iterations SOC = scalar outside cost for run time cost model check. */ if ((scalar_single_iter_cost * vf) > vec_inside_cost) { if (vec_outside_cost <= 0) min_profitable_iters = 1; else { min_profitable_iters = ((vec_outside_cost - scalar_outside_cost) * vf - vec_inside_cost * peel_iters_prologue - vec_inside_cost * peel_iters_epilogue) / ((scalar_single_iter_cost * vf) - vec_inside_cost); if ((scalar_single_iter_cost * vf * min_profitable_iters) <= ((vec_inside_cost * min_profitable_iters) + ((vec_outside_cost - scalar_outside_cost) * vf))) min_profitable_iters++; } } /* vector version will never be profitable. */ else { if (vect_print_dump_info (REPORT_COST)) fprintf (vect_dump, "cost model: the vector iteration cost = %d " "divided by the scalar iteration cost = %d " "is greater or equal to the vectorization factor = %d.", vec_inside_cost, scalar_single_iter_cost, vf); return -1; } if (vect_print_dump_info (REPORT_COST)) { fprintf (vect_dump, "Cost model analysis: \n"); fprintf (vect_dump, " Vector inside of loop cost: %d\n", vec_inside_cost); fprintf (vect_dump, " Vector outside of loop cost: %d\n", vec_outside_cost); fprintf (vect_dump, " Scalar iteration cost: %d\n", scalar_single_iter_cost); fprintf (vect_dump, " Scalar outside cost: %d\n", scalar_outside_cost); fprintf (vect_dump, " prologue iterations: %d\n", peel_iters_prologue); fprintf (vect_dump, " epilogue iterations: %d\n", peel_iters_epilogue); fprintf (vect_dump, " Calculated minimum iters for profitability: %d\n", min_profitable_iters); } min_profitable_iters = min_profitable_iters < vf ? vf : min_profitable_iters; /* Because the condition we create is: if (niters <= min_profitable_iters) then skip the vectorized loop. */ min_profitable_iters--; if (vect_print_dump_info (REPORT_COST)) fprintf (vect_dump, " Profitability threshold = %d\n", min_profitable_iters); return min_profitable_iters; } /* TODO: Close dependency between vect_model_*_cost and vectorizable_* functions. Design better to avoid maintenance issues. */ /* Function vect_model_reduction_cost. Models cost for a reduction operation, including the vector ops generated within the strip-mine loop, the initial definition before the loop, and the epilogue code that must be generated. */ static bool vect_model_reduction_cost (stmt_vec_info stmt_info, enum tree_code reduc_code, int ncopies) { int outer_cost = 0; enum tree_code code; optab optab; tree vectype; gimple stmt, orig_stmt; tree reduction_op; enum machine_mode mode; loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info); struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); /* Cost of reduction op inside loop. */ STMT_VINFO_INSIDE_OF_LOOP_COST (stmt_info) += ncopies * vect_get_cost (vector_stmt); stmt = STMT_VINFO_STMT (stmt_info); switch (get_gimple_rhs_class (gimple_assign_rhs_code (stmt))) { case GIMPLE_SINGLE_RHS: gcc_assert (TREE_OPERAND_LENGTH (gimple_assign_rhs1 (stmt)) == ternary_op); reduction_op = TREE_OPERAND (gimple_assign_rhs1 (stmt), 2); break; case GIMPLE_UNARY_RHS: reduction_op = gimple_assign_rhs1 (stmt); break; case GIMPLE_BINARY_RHS: reduction_op = gimple_assign_rhs2 (stmt); break; case GIMPLE_TERNARY_RHS: reduction_op = gimple_assign_rhs3 (stmt); break; default: gcc_unreachable (); } vectype = get_vectype_for_scalar_type (TREE_TYPE (reduction_op)); if (!vectype) { if (vect_print_dump_info (REPORT_COST)) { fprintf (vect_dump, "unsupported data-type "); print_generic_expr (vect_dump, TREE_TYPE (reduction_op), TDF_SLIM); } return false; } mode = TYPE_MODE (vectype); orig_stmt = STMT_VINFO_RELATED_STMT (stmt_info); if (!orig_stmt) orig_stmt = STMT_VINFO_STMT (stmt_info); code = gimple_assign_rhs_code (orig_stmt); /* Add in cost for initial definition. */ outer_cost += vect_get_cost (scalar_to_vec); /* Determine cost of epilogue code. We have a reduction operator that will reduce the vector in one statement. Also requires scalar extract. */ if (!nested_in_vect_loop_p (loop, orig_stmt)) { if (reduc_code != ERROR_MARK) outer_cost += vect_get_cost (vector_stmt) + vect_get_cost (vec_to_scalar); else { int vec_size_in_bits = tree_low_cst (TYPE_SIZE (vectype), 1); tree bitsize = TYPE_SIZE (TREE_TYPE (gimple_assign_lhs (orig_stmt))); int element_bitsize = tree_low_cst (bitsize, 1); int nelements = vec_size_in_bits / element_bitsize; optab = optab_for_tree_code (code, vectype, optab_default); /* We have a whole vector shift available. */ if (VECTOR_MODE_P (mode) && optab_handler (optab, mode) != CODE_FOR_nothing && optab_handler (vec_shr_optab, mode) != CODE_FOR_nothing) /* Final reduction via vector shifts and the reduction operator. Also requires scalar extract. */ outer_cost += ((exact_log2(nelements) * 2) * vect_get_cost (vector_stmt) + vect_get_cost (vec_to_scalar)); else /* Use extracts and reduction op for final reduction. For N elements, we have N extracts and N-1 reduction ops. */ outer_cost += ((nelements + nelements - 1) * vect_get_cost (vector_stmt)); } } STMT_VINFO_OUTSIDE_OF_LOOP_COST (stmt_info) = outer_cost; if (vect_print_dump_info (REPORT_COST)) fprintf (vect_dump, "vect_model_reduction_cost: inside_cost = %d, " "outside_cost = %d .", STMT_VINFO_INSIDE_OF_LOOP_COST (stmt_info), STMT_VINFO_OUTSIDE_OF_LOOP_COST (stmt_info)); return true; } /* Function vect_model_induction_cost. Models cost for induction operations. */ static void vect_model_induction_cost (stmt_vec_info stmt_info, int ncopies) { /* loop cost for vec_loop. */ STMT_VINFO_INSIDE_OF_LOOP_COST (stmt_info) = ncopies * vect_get_cost (vector_stmt); /* prologue cost for vec_init and vec_step. */ STMT_VINFO_OUTSIDE_OF_LOOP_COST (stmt_info) = 2 * vect_get_cost (scalar_to_vec); if (vect_print_dump_info (REPORT_COST)) fprintf (vect_dump, "vect_model_induction_cost: inside_cost = %d, " "outside_cost = %d .", STMT_VINFO_INSIDE_OF_LOOP_COST (stmt_info), STMT_VINFO_OUTSIDE_OF_LOOP_COST (stmt_info)); } /* Function get_initial_def_for_induction Input: STMT - a stmt that performs an induction operation in the loop. IV_PHI - the initial value of the induction variable Output: Return a vector variable, initialized with the first VF values of the induction variable. E.g., for an iv with IV_PHI='X' and evolution S, for a vector of 4 units, we want to return: [X, X + S, X + 2*S, X + 3*S]. */ static tree get_initial_def_for_induction (gimple iv_phi) { stmt_vec_info stmt_vinfo = vinfo_for_stmt (iv_phi); loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_vinfo); struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); tree scalar_type; tree vectype; int nunits; edge pe = loop_preheader_edge (loop); struct loop *iv_loop; basic_block new_bb; tree vec, vec_init, vec_step, t; tree access_fn; tree new_var; tree new_name; gimple init_stmt, induction_phi, new_stmt; tree induc_def, vec_def, vec_dest; tree init_expr, step_expr; int vf = LOOP_VINFO_VECT_FACTOR (loop_vinfo); int i; bool ok; int ncopies; tree expr; stmt_vec_info phi_info = vinfo_for_stmt (iv_phi); bool nested_in_vect_loop = false; gimple_seq stmts = NULL; imm_use_iterator imm_iter; use_operand_p use_p; gimple exit_phi; edge latch_e; tree loop_arg; gimple_stmt_iterator si; basic_block bb = gimple_bb (iv_phi); tree stepvectype; tree resvectype; /* Is phi in an inner-loop, while vectorizing an enclosing outer-loop? */ if (nested_in_vect_loop_p (loop, iv_phi)) { nested_in_vect_loop = true; iv_loop = loop->inner; } else iv_loop = loop; gcc_assert (iv_loop == (gimple_bb (iv_phi))->loop_father); latch_e = loop_latch_edge (iv_loop); loop_arg = PHI_ARG_DEF_FROM_EDGE (iv_phi, latch_e); access_fn = analyze_scalar_evolution (iv_loop, PHI_RESULT (iv_phi)); gcc_assert (access_fn); STRIP_NOPS (access_fn); ok = vect_is_simple_iv_evolution (iv_loop->num, access_fn, &init_expr, &step_expr); gcc_assert (ok); pe = loop_preheader_edge (iv_loop); scalar_type = TREE_TYPE (init_expr); vectype = get_vectype_for_scalar_type (scalar_type); resvectype = get_vectype_for_scalar_type (TREE_TYPE (PHI_RESULT (iv_phi))); gcc_assert (vectype); nunits = TYPE_VECTOR_SUBPARTS (vectype); ncopies = vf / nunits; gcc_assert (phi_info); gcc_assert (ncopies >= 1); /* Find the first insertion point in the BB. */ si = gsi_after_labels (bb); /* Create the vector that holds the initial_value of the induction. */ if (nested_in_vect_loop) { /* iv_loop is nested in the loop to be vectorized. init_expr had already been created during vectorization of previous stmts. We obtain it from the STMT_VINFO_VEC_STMT of the defining stmt. */ tree iv_def = PHI_ARG_DEF_FROM_EDGE (iv_phi, loop_preheader_edge (iv_loop)); vec_init = vect_get_vec_def_for_operand (iv_def, iv_phi, NULL); } else { /* iv_loop is the loop to be vectorized. Create: vec_init = [X, X+S, X+2*S, X+3*S] (S = step_expr, X = init_expr) */ new_var = vect_get_new_vect_var (scalar_type, vect_scalar_var, "var_"); add_referenced_var (new_var); new_name = force_gimple_operand (init_expr, &stmts, false, new_var); if (stmts) { new_bb = gsi_insert_seq_on_edge_immediate (pe, stmts); gcc_assert (!new_bb); } t = NULL_TREE; t = tree_cons (NULL_TREE, new_name, t); for (i = 1; i < nunits; i++) { /* Create: new_name_i = new_name + step_expr */ enum tree_code code = POINTER_TYPE_P (scalar_type) ? POINTER_PLUS_EXPR : PLUS_EXPR; init_stmt = gimple_build_assign_with_ops (code, new_var, new_name, step_expr); new_name = make_ssa_name (new_var, init_stmt); gimple_assign_set_lhs (init_stmt, new_name); new_bb = gsi_insert_on_edge_immediate (pe, init_stmt); gcc_assert (!new_bb); if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "created new init_stmt: "); print_gimple_stmt (vect_dump, init_stmt, 0, TDF_SLIM); } t = tree_cons (NULL_TREE, new_name, t); } /* Create a vector from [new_name_0, new_name_1, ..., new_name_nunits-1] */ vec = build_constructor_from_list (vectype, nreverse (t)); vec_init = vect_init_vector (iv_phi, vec, vectype, NULL); } /* Create the vector that holds the step of the induction. */ if (nested_in_vect_loop) /* iv_loop is nested in the loop to be vectorized. Generate: vec_step = [S, S, S, S] */ new_name = step_expr; else { /* iv_loop is the loop to be vectorized. Generate: vec_step = [VF*S, VF*S, VF*S, VF*S] */ expr = build_int_cst (TREE_TYPE (step_expr), vf); new_name = fold_build2 (MULT_EXPR, TREE_TYPE (step_expr), expr, step_expr); } t = unshare_expr (new_name); gcc_assert (CONSTANT_CLASS_P (new_name)); stepvectype = get_vectype_for_scalar_type (TREE_TYPE (new_name)); gcc_assert (stepvectype); vec = build_vector_from_val (stepvectype, t); vec_step = vect_init_vector (iv_phi, vec, stepvectype, NULL); /* Create the following def-use cycle: loop prolog: vec_init = ... vec_step = ... loop: vec_iv = PHI <vec_init, vec_loop> ... STMT ... vec_loop = vec_iv + vec_step; */ /* Create the induction-phi that defines the induction-operand. */ vec_dest = vect_get_new_vect_var (vectype, vect_simple_var, "vec_iv_"); add_referenced_var (vec_dest); induction_phi = create_phi_node (vec_dest, iv_loop->header); set_vinfo_for_stmt (induction_phi, new_stmt_vec_info (induction_phi, loop_vinfo, NULL)); induc_def = PHI_RESULT (induction_phi); /* Create the iv update inside the loop */ new_stmt = gimple_build_assign_with_ops (PLUS_EXPR, vec_dest, induc_def, vec_step); vec_def = make_ssa_name (vec_dest, new_stmt); gimple_assign_set_lhs (new_stmt, vec_def); gsi_insert_before (&si, new_stmt, GSI_SAME_STMT); set_vinfo_for_stmt (new_stmt, new_stmt_vec_info (new_stmt, loop_vinfo, NULL)); /* Set the arguments of the phi node: */ add_phi_arg (induction_phi, vec_init, pe, UNKNOWN_LOCATION); add_phi_arg (induction_phi, vec_def, loop_latch_edge (iv_loop), UNKNOWN_LOCATION); /* In case that vectorization factor (VF) is bigger than the number of elements that we can fit in a vectype (nunits), we have to generate more than one vector stmt - i.e - we need to "unroll" the vector stmt by a factor VF/nunits. For more details see documentation in vectorizable_operation. */ if (ncopies > 1) { stmt_vec_info prev_stmt_vinfo; /* FORNOW. This restriction should be relaxed. */ gcc_assert (!nested_in_vect_loop); /* Create the vector that holds the step of the induction. */ expr = build_int_cst (TREE_TYPE (step_expr), nunits); new_name = fold_build2 (MULT_EXPR, TREE_TYPE (step_expr), expr, step_expr); t = unshare_expr (new_name); gcc_assert (CONSTANT_CLASS_P (new_name)); vec = build_vector_from_val (stepvectype, t); vec_step = vect_init_vector (iv_phi, vec, stepvectype, NULL); vec_def = induc_def; prev_stmt_vinfo = vinfo_for_stmt (induction_phi); for (i = 1; i < ncopies; i++) { /* vec_i = vec_prev + vec_step */ new_stmt = gimple_build_assign_with_ops (PLUS_EXPR, vec_dest, vec_def, vec_step); vec_def = make_ssa_name (vec_dest, new_stmt); gimple_assign_set_lhs (new_stmt, vec_def); gsi_insert_before (&si, new_stmt, GSI_SAME_STMT); if (!useless_type_conversion_p (resvectype, vectype)) { new_stmt = gimple_build_assign_with_ops (VIEW_CONVERT_EXPR, vect_get_new_vect_var (resvectype, vect_simple_var, "vec_iv_"), build1 (VIEW_CONVERT_EXPR, resvectype, gimple_assign_lhs (new_stmt)), NULL_TREE); gimple_assign_set_lhs (new_stmt, make_ssa_name (gimple_assign_lhs (new_stmt), new_stmt)); gsi_insert_before (&si, new_stmt, GSI_SAME_STMT); } set_vinfo_for_stmt (new_stmt, new_stmt_vec_info (new_stmt, loop_vinfo, NULL)); STMT_VINFO_RELATED_STMT (prev_stmt_vinfo) = new_stmt; prev_stmt_vinfo = vinfo_for_stmt (new_stmt); } } if (nested_in_vect_loop) { /* Find the loop-closed exit-phi of the induction, and record the final vector of induction results: */ exit_phi = NULL; FOR_EACH_IMM_USE_FAST (use_p, imm_iter, loop_arg) { if (!flow_bb_inside_loop_p (iv_loop, gimple_bb (USE_STMT (use_p)))) { exit_phi = USE_STMT (use_p); break; } } if (exit_phi) { stmt_vec_info stmt_vinfo = vinfo_for_stmt (exit_phi); /* FORNOW. Currently not supporting the case that an inner-loop induction is not used in the outer-loop (i.e. only outside the outer-loop). */ gcc_assert (STMT_VINFO_RELEVANT_P (stmt_vinfo) && !STMT_VINFO_LIVE_P (stmt_vinfo)); STMT_VINFO_VEC_STMT (stmt_vinfo) = new_stmt; if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "vector of inductions after inner-loop:"); print_gimple_stmt (vect_dump, new_stmt, 0, TDF_SLIM); } } } if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "transform induction: created def-use cycle: "); print_gimple_stmt (vect_dump, induction_phi, 0, TDF_SLIM); fprintf (vect_dump, "\n"); print_gimple_stmt (vect_dump, SSA_NAME_DEF_STMT (vec_def), 0, TDF_SLIM); } STMT_VINFO_VEC_STMT (phi_info) = induction_phi; if (!useless_type_conversion_p (resvectype, vectype)) { new_stmt = gimple_build_assign_with_ops (VIEW_CONVERT_EXPR, vect_get_new_vect_var (resvectype, vect_simple_var, "vec_iv_"), build1 (VIEW_CONVERT_EXPR, resvectype, induc_def), NULL_TREE); induc_def = make_ssa_name (gimple_assign_lhs (new_stmt), new_stmt); gimple_assign_set_lhs (new_stmt, induc_def); si = gsi_start_bb (bb); gsi_insert_before (&si, new_stmt, GSI_SAME_STMT); set_vinfo_for_stmt (new_stmt, new_stmt_vec_info (new_stmt, loop_vinfo, NULL)); STMT_VINFO_RELATED_STMT (vinfo_for_stmt (new_stmt)) = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (induction_phi)); } return induc_def; } /* Function get_initial_def_for_reduction Input: STMT - a stmt that performs a reduction operation in the loop. INIT_VAL - the initial value of the reduction variable Output: ADJUSTMENT_DEF - a tree that holds a value to be added to the final result of the reduction (used for adjusting the epilog - see below). Return a vector variable, initialized according to the operation that STMT performs. This vector will be used as the initial value of the vector of partial results. Option1 (adjust in epilog): Initialize the vector as follows: add/bit or/xor: [0,0,...,0,0] mult/bit and: [1,1,...,1,1] min/max/cond_expr: [init_val,init_val,..,init_val,init_val] and when necessary (e.g. add/mult case) let the caller know that it needs to adjust the result by init_val. Option2: Initialize the vector as follows: add/bit or/xor: [init_val,0,0,...,0] mult/bit and: [init_val,1,1,...,1] min/max/cond_expr: [init_val,init_val,...,init_val] and no adjustments are needed. For example, for the following code: s = init_val; for (i=0;i<n;i++) s = s + a[i]; STMT is 's = s + a[i]', and the reduction variable is 's'. For a vector of 4 units, we want to return either [0,0,0,init_val], or [0,0,0,0] and let the caller know that it needs to adjust the result at the end by 'init_val'. FORNOW, we are using the 'adjust in epilog' scheme, because this way the initialization vector is simpler (same element in all entries), if ADJUSTMENT_DEF is not NULL, and Option2 otherwise. A cost model should help decide between these two schemes. */ tree get_initial_def_for_reduction (gimple stmt, tree init_val, tree *adjustment_def) { stmt_vec_info stmt_vinfo = vinfo_for_stmt (stmt); loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_vinfo); struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); tree scalar_type = TREE_TYPE (init_val); tree vectype = get_vectype_for_scalar_type (scalar_type); int nunits; enum tree_code code = gimple_assign_rhs_code (stmt); tree def_for_init; tree init_def; tree t = NULL_TREE; int i; bool nested_in_vect_loop = false; tree init_value; REAL_VALUE_TYPE real_init_val = dconst0; int int_init_val = 0; gimple def_stmt = NULL; gcc_assert (vectype); nunits = TYPE_VECTOR_SUBPARTS (vectype); gcc_assert (POINTER_TYPE_P (scalar_type) || INTEGRAL_TYPE_P (scalar_type) || SCALAR_FLOAT_TYPE_P (scalar_type)); if (nested_in_vect_loop_p (loop, stmt)) nested_in_vect_loop = true; else gcc_assert (loop == (gimple_bb (stmt))->loop_father); /* In case of double reduction we only create a vector variable to be put in the reduction phi node. The actual statement creation is done in vect_create_epilog_for_reduction. */ if (adjustment_def && nested_in_vect_loop && TREE_CODE (init_val) == SSA_NAME && (def_stmt = SSA_NAME_DEF_STMT (init_val)) && gimple_code (def_stmt) == GIMPLE_PHI && flow_bb_inside_loop_p (loop, gimple_bb (def_stmt)) && vinfo_for_stmt (def_stmt) && STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def_stmt)) == vect_double_reduction_def) { *adjustment_def = NULL; return vect_create_destination_var (init_val, vectype); } if (TREE_CONSTANT (init_val)) { if (SCALAR_FLOAT_TYPE_P (scalar_type)) init_value = build_real (scalar_type, TREE_REAL_CST (init_val)); else init_value = build_int_cst (scalar_type, TREE_INT_CST_LOW (init_val)); } else init_value = init_val; switch (code) { case WIDEN_SUM_EXPR: case DOT_PROD_EXPR: case PLUS_EXPR: case MINUS_EXPR: case BIT_IOR_EXPR: case BIT_XOR_EXPR: case MULT_EXPR: case BIT_AND_EXPR: /* ADJUSMENT_DEF is NULL when called from vect_create_epilog_for_reduction to vectorize double reduction. */ if (adjustment_def) { if (nested_in_vect_loop) *adjustment_def = vect_get_vec_def_for_operand (init_val, stmt, NULL); else *adjustment_def = init_val; } if (code == MULT_EXPR) { real_init_val = dconst1; int_init_val = 1; } if (code == BIT_AND_EXPR) int_init_val = -1; if (SCALAR_FLOAT_TYPE_P (scalar_type)) def_for_init = build_real (scalar_type, real_init_val); else def_for_init = build_int_cst (scalar_type, int_init_val); /* Create a vector of '0' or '1' except the first element. */ for (i = nunits - 2; i >= 0; --i) t = tree_cons (NULL_TREE, def_for_init, t); /* Option1: the first element is '0' or '1' as well. */ if (adjustment_def) { t = tree_cons (NULL_TREE, def_for_init, t); init_def = build_vector (vectype, t); break; } /* Option2: the first element is INIT_VAL. */ t = tree_cons (NULL_TREE, init_value, t); if (TREE_CONSTANT (init_val)) init_def = build_vector (vectype, t); else init_def = build_constructor_from_list (vectype, t); break; case MIN_EXPR: case MAX_EXPR: case COND_EXPR: if (adjustment_def) { *adjustment_def = NULL_TREE; init_def = vect_get_vec_def_for_operand (init_val, stmt, NULL); break; } init_def = build_vector_from_val (vectype, init_value); break; default: gcc_unreachable (); } return init_def; } /* Function vect_create_epilog_for_reduction Create code at the loop-epilog to finalize the result of a reduction computation. VECT_DEFS is list of vector of partial results, i.e., the lhs's of vector reduction statements. STMT is the scalar reduction stmt that is being vectorized. NCOPIES is > 1 in case the vectorization factor (VF) is bigger than the number of elements that we can fit in a vectype (nunits). In this case we have to generate more than one vector stmt - i.e - we need to "unroll" the vector stmt by a factor VF/nunits. For more details see documentation in vectorizable_operation. REDUC_CODE is the tree-code for the epilog reduction. REDUCTION_PHIS is a list of the phi-nodes that carry the reduction computation. REDUC_INDEX is the index of the operand in the right hand side of the statement that is defined by REDUCTION_PHI. DOUBLE_REDUC is TRUE if double reduction phi nodes should be handled. SLP_NODE is an SLP node containing a group of reduction statements. The first one in this group is STMT. This function: 1. Creates the reduction def-use cycles: sets the arguments for REDUCTION_PHIS: The loop-entry argument is the vectorized initial-value of the reduction. The loop-latch argument is taken from VECT_DEFS - the vector of partial sums. 2. "Reduces" each vector of partial results VECT_DEFS into a single result, by applying the operation specified by REDUC_CODE if available, or by other means (whole-vector shifts or a scalar loop). The function also creates a new phi node at the loop exit to preserve loop-closed form, as illustrated below. The flow at the entry to this function: loop: vec_def = phi <null, null> # REDUCTION_PHI VECT_DEF = vector_stmt # vectorized form of STMT s_loop = scalar_stmt # (scalar) STMT loop_exit: s_out0 = phi <s_loop> # (scalar) EXIT_PHI use <s_out0> use <s_out0> The above is transformed by this function into: loop: vec_def = phi <vec_init, VECT_DEF> # REDUCTION_PHI VECT_DEF = vector_stmt # vectorized form of STMT s_loop = scalar_stmt # (scalar) STMT loop_exit: s_out0 = phi <s_loop> # (scalar) EXIT_PHI v_out1 = phi <VECT_DEF> # NEW_EXIT_PHI v_out2 = reduce <v_out1> s_out3 = extract_field <v_out2, 0> s_out4 = adjust_result <s_out3> use <s_out4> use <s_out4> */ static void vect_create_epilog_for_reduction (VEC (tree, heap) *vect_defs, gimple stmt, int ncopies, enum tree_code reduc_code, VEC (gimple, heap) *reduction_phis, int reduc_index, bool double_reduc, slp_tree slp_node) { stmt_vec_info stmt_info = vinfo_for_stmt (stmt); stmt_vec_info prev_phi_info; tree vectype; enum machine_mode mode; loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info); struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo), *outer_loop = NULL; basic_block exit_bb; tree scalar_dest; tree scalar_type; gimple new_phi = NULL, phi; gimple_stmt_iterator exit_gsi; tree vec_dest; tree new_temp = NULL_TREE, new_dest, new_name, new_scalar_dest; gimple epilog_stmt = NULL; enum tree_code code = gimple_assign_rhs_code (stmt); gimple exit_phi; tree bitsize, bitpos; tree adjustment_def = NULL; tree vec_initial_def = NULL; tree reduction_op, expr, def; tree orig_name, scalar_result; imm_use_iterator imm_iter, phi_imm_iter; use_operand_p use_p, phi_use_p; bool extract_scalar_result = false; gimple use_stmt, orig_stmt, reduction_phi = NULL; bool nested_in_vect_loop = false; VEC (gimple, heap) *new_phis = NULL; VEC (gimple, heap) *inner_phis = NULL; enum vect_def_type dt = vect_unknown_def_type; int j, i; VEC (tree, heap) *scalar_results = NULL; unsigned int group_size = 1, k, ratio; VEC (tree, heap) *vec_initial_defs = NULL; VEC (gimple, heap) *phis; bool slp_reduc = false; tree new_phi_result; gimple inner_phi = NULL; if (slp_node) group_size = VEC_length (gimple, SLP_TREE_SCALAR_STMTS (slp_node)); if (nested_in_vect_loop_p (loop, stmt)) { outer_loop = loop; loop = loop->inner; nested_in_vect_loop = true; gcc_assert (!slp_node); } switch (get_gimple_rhs_class (gimple_assign_rhs_code (stmt))) { case GIMPLE_SINGLE_RHS: gcc_assert (TREE_OPERAND_LENGTH (gimple_assign_rhs1 (stmt)) == ternary_op); reduction_op = TREE_OPERAND (gimple_assign_rhs1 (stmt), reduc_index); break; case GIMPLE_UNARY_RHS: reduction_op = gimple_assign_rhs1 (stmt); break; case GIMPLE_BINARY_RHS: reduction_op = reduc_index ? gimple_assign_rhs2 (stmt) : gimple_assign_rhs1 (stmt); break; case GIMPLE_TERNARY_RHS: reduction_op = gimple_op (stmt, reduc_index + 1); break; default: gcc_unreachable (); } vectype = get_vectype_for_scalar_type (TREE_TYPE (reduction_op)); gcc_assert (vectype); mode = TYPE_MODE (vectype); /* 1. Create the reduction def-use cycle: Set the arguments of REDUCTION_PHIS, i.e., transform loop: vec_def = phi <null, null> # REDUCTION_PHI VECT_DEF = vector_stmt # vectorized form of STMT ... into: loop: vec_def = phi <vec_init, VECT_DEF> # REDUCTION_PHI VECT_DEF = vector_stmt # vectorized form of STMT ... (in case of SLP, do it for all the phis). */ /* Get the loop-entry arguments. */ if (slp_node) vect_get_vec_defs (reduction_op, NULL_TREE, stmt, &vec_initial_defs, NULL, slp_node, reduc_index); else { vec_initial_defs = VEC_alloc (tree, heap, 1); /* For the case of reduction, vect_get_vec_def_for_operand returns the scalar def before the loop, that defines the initial value of the reduction variable. */ vec_initial_def = vect_get_vec_def_for_operand (reduction_op, stmt, &adjustment_def); VEC_quick_push (tree, vec_initial_defs, vec_initial_def); } /* Set phi nodes arguments. */ FOR_EACH_VEC_ELT (gimple, reduction_phis, i, phi) { tree vec_init_def = VEC_index (tree, vec_initial_defs, i); tree def = VEC_index (tree, vect_defs, i); for (j = 0; j < ncopies; j++) { /* Set the loop-entry arg of the reduction-phi. */ add_phi_arg (phi, vec_init_def, loop_preheader_edge (loop), UNKNOWN_LOCATION); /* Set the loop-latch arg for the reduction-phi. */ if (j > 0) def = vect_get_vec_def_for_stmt_copy (vect_unknown_def_type, def); add_phi_arg (phi, def, loop_latch_edge (loop), UNKNOWN_LOCATION); if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "transform reduction: created def-use" " cycle: "); print_gimple_stmt (vect_dump, phi, 0, TDF_SLIM); fprintf (vect_dump, "\n"); print_gimple_stmt (vect_dump, SSA_NAME_DEF_STMT (def), 0, TDF_SLIM); } phi = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (phi)); } } VEC_free (tree, heap, vec_initial_defs); /* 2. Create epilog code. The reduction epilog code operates across the elements of the vector of partial results computed by the vectorized loop. The reduction epilog code consists of: step 1: compute the scalar result in a vector (v_out2) step 2: extract the scalar result (s_out3) from the vector (v_out2) step 3: adjust the scalar result (s_out3) if needed. Step 1 can be accomplished using one the following three schemes: (scheme 1) using reduc_code, if available. (scheme 2) using whole-vector shifts, if available. (scheme 3) using a scalar loop. In this case steps 1+2 above are combined. The overall epilog code looks like this: s_out0 = phi <s_loop> # original EXIT_PHI v_out1 = phi <VECT_DEF> # NEW_EXIT_PHI v_out2 = reduce <v_out1> # step 1 s_out3 = extract_field <v_out2, 0> # step 2 s_out4 = adjust_result <s_out3> # step 3 (step 3 is optional, and steps 1 and 2 may be combined). Lastly, the uses of s_out0 are replaced by s_out4. */ /* 2.1 Create new loop-exit-phis to preserve loop-closed form: v_out1 = phi <VECT_DEF> Store them in NEW_PHIS. */ exit_bb = single_exit (loop)->dest; prev_phi_info = NULL; new_phis = VEC_alloc (gimple, heap, VEC_length (tree, vect_defs)); FOR_EACH_VEC_ELT (tree, vect_defs, i, def) { for (j = 0; j < ncopies; j++) { phi = create_phi_node (SSA_NAME_VAR (def), exit_bb); set_vinfo_for_stmt (phi, new_stmt_vec_info (phi, loop_vinfo, NULL)); if (j == 0) VEC_quick_push (gimple, new_phis, phi); else { def = vect_get_vec_def_for_stmt_copy (dt, def); STMT_VINFO_RELATED_STMT (prev_phi_info) = phi; } SET_PHI_ARG_DEF (phi, single_exit (loop)->dest_idx, def); prev_phi_info = vinfo_for_stmt (phi); } } /* The epilogue is created for the outer-loop, i.e., for the loop being vectorized. Create exit phis for the outer loop. */ if (double_reduc) { loop = outer_loop; exit_bb = single_exit (loop)->dest; inner_phis = VEC_alloc (gimple, heap, VEC_length (tree, vect_defs)); FOR_EACH_VEC_ELT (gimple, new_phis, i, phi) { gimple outer_phi = create_phi_node (SSA_NAME_VAR (PHI_RESULT (phi)), exit_bb); SET_PHI_ARG_DEF (outer_phi, single_exit (loop)->dest_idx, PHI_RESULT (phi)); set_vinfo_for_stmt (outer_phi, new_stmt_vec_info (outer_phi, loop_vinfo, NULL)); VEC_quick_push (gimple, inner_phis, phi); VEC_replace (gimple, new_phis, i, outer_phi); prev_phi_info = vinfo_for_stmt (outer_phi); while (STMT_VINFO_RELATED_STMT (vinfo_for_stmt (phi))) { phi = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (phi)); outer_phi = create_phi_node (SSA_NAME_VAR (PHI_RESULT (phi)), exit_bb); SET_PHI_ARG_DEF (outer_phi, single_exit (loop)->dest_idx, PHI_RESULT (phi)); set_vinfo_for_stmt (outer_phi, new_stmt_vec_info (outer_phi, loop_vinfo, NULL)); STMT_VINFO_RELATED_STMT (prev_phi_info) = outer_phi; prev_phi_info = vinfo_for_stmt (outer_phi); } } } exit_gsi = gsi_after_labels (exit_bb); /* 2.2 Get the relevant tree-code to use in the epilog for schemes 2,3 (i.e. when reduc_code is not available) and in the final adjustment code (if needed). Also get the original scalar reduction variable as defined in the loop. In case STMT is a "pattern-stmt" (i.e. - it represents a reduction pattern), the tree-code and scalar-def are taken from the original stmt that the pattern-stmt (STMT) replaces. Otherwise (it is a regular reduction) - the tree-code and scalar-def are taken from STMT. */ orig_stmt = STMT_VINFO_RELATED_STMT (stmt_info); if (!orig_stmt) { /* Regular reduction */ orig_stmt = stmt; } else { /* Reduction pattern */ stmt_vec_info stmt_vinfo = vinfo_for_stmt (orig_stmt); gcc_assert (STMT_VINFO_IN_PATTERN_P (stmt_vinfo)); gcc_assert (STMT_VINFO_RELATED_STMT (stmt_vinfo) == stmt); } code = gimple_assign_rhs_code (orig_stmt); /* For MINUS_EXPR the initial vector is [init_val,0,...,0], therefore, partial results are added and not subtracted. */ if (code == MINUS_EXPR) code = PLUS_EXPR; scalar_dest = gimple_assign_lhs (orig_stmt); scalar_type = TREE_TYPE (scalar_dest); scalar_results = VEC_alloc (tree, heap, group_size); new_scalar_dest = vect_create_destination_var (scalar_dest, NULL); bitsize = TYPE_SIZE (scalar_type); /* In case this is a reduction in an inner-loop while vectorizing an outer loop - we don't need to extract a single scalar result at the end of the inner-loop (unless it is double reduction, i.e., the use of reduction is outside the outer-loop). The final vector of partial results will be used in the vectorized outer-loop, or reduced to a scalar result at the end of the outer-loop. */ if (nested_in_vect_loop && !double_reduc) goto vect_finalize_reduction; /* SLP reduction without reduction chain, e.g., # a1 = phi <a2, a0> # b1 = phi <b2, b0> a2 = operation (a1) b2 = operation (b1) */ slp_reduc = (slp_node && !GROUP_FIRST_ELEMENT (vinfo_for_stmt (stmt))); /* In case of reduction chain, e.g., # a1 = phi <a3, a0> a2 = operation (a1) a3 = operation (a2), we may end up with more than one vector result. Here we reduce them to one vector. */ if (GROUP_FIRST_ELEMENT (vinfo_for_stmt (stmt))) { tree first_vect = PHI_RESULT (VEC_index (gimple, new_phis, 0)); tree tmp; gimple new_vec_stmt = NULL; vec_dest = vect_create_destination_var (scalar_dest, vectype); for (k = 1; k < VEC_length (gimple, new_phis); k++) { gimple next_phi = VEC_index (gimple, new_phis, k); tree second_vect = PHI_RESULT (next_phi); tmp = build2 (code, vectype, first_vect, second_vect); new_vec_stmt = gimple_build_assign (vec_dest, tmp); first_vect = make_ssa_name (vec_dest, new_vec_stmt); gimple_assign_set_lhs (new_vec_stmt, first_vect); gsi_insert_before (&exit_gsi, new_vec_stmt, GSI_SAME_STMT); } new_phi_result = first_vect; if (new_vec_stmt) { VEC_truncate (gimple, new_phis, 0); VEC_safe_push (gimple, heap, new_phis, new_vec_stmt); } } else new_phi_result = PHI_RESULT (VEC_index (gimple, new_phis, 0)); /* 2.3 Create the reduction code, using one of the three schemes described above. In SLP we simply need to extract all the elements from the vector (without reducing them), so we use scalar shifts. */ if (reduc_code != ERROR_MARK && !slp_reduc) { tree tmp; /*** Case 1: Create: v_out2 = reduc_expr <v_out1> */ if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "Reduce using direct vector reduction."); vec_dest = vect_create_destination_var (scalar_dest, vectype); tmp = build1 (reduc_code, vectype, new_phi_result); epilog_stmt = gimple_build_assign (vec_dest, tmp); new_temp = make_ssa_name (vec_dest, epilog_stmt); gimple_assign_set_lhs (epilog_stmt, new_temp); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); extract_scalar_result = true; } else { enum tree_code shift_code = ERROR_MARK; bool have_whole_vector_shift = true; int bit_offset; int element_bitsize = tree_low_cst (bitsize, 1); int vec_size_in_bits = tree_low_cst (TYPE_SIZE (vectype), 1); tree vec_temp; if (optab_handler (vec_shr_optab, mode) != CODE_FOR_nothing) shift_code = VEC_RSHIFT_EXPR; else have_whole_vector_shift = false; /* Regardless of whether we have a whole vector shift, if we're emulating the operation via tree-vect-generic, we don't want to use it. Only the first round of the reduction is likely to still be profitable via emulation. */ /* ??? It might be better to emit a reduction tree code here, so that tree-vect-generic can expand the first round via bit tricks. */ if (!VECTOR_MODE_P (mode)) have_whole_vector_shift = false; else { optab optab = optab_for_tree_code (code, vectype, optab_default); if (optab_handler (optab, mode) == CODE_FOR_nothing) have_whole_vector_shift = false; } if (have_whole_vector_shift && !slp_reduc) { /*** Case 2: Create: for (offset = VS/2; offset >= element_size; offset/=2) { Create: va' = vec_shift <va, offset> Create: va = vop <va, va'> } */ if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "Reduce using vector shifts"); vec_dest = vect_create_destination_var (scalar_dest, vectype); new_temp = new_phi_result; for (bit_offset = vec_size_in_bits/2; bit_offset >= element_bitsize; bit_offset /= 2) { tree bitpos = size_int (bit_offset); epilog_stmt = gimple_build_assign_with_ops (shift_code, vec_dest, new_temp, bitpos); new_name = make_ssa_name (vec_dest, epilog_stmt); gimple_assign_set_lhs (epilog_stmt, new_name); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); epilog_stmt = gimple_build_assign_with_ops (code, vec_dest, new_name, new_temp); new_temp = make_ssa_name (vec_dest, epilog_stmt); gimple_assign_set_lhs (epilog_stmt, new_temp); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); } extract_scalar_result = true; } else { tree rhs; /*** Case 3: Create: s = extract_field <v_out2, 0> for (offset = element_size; offset < vector_size; offset += element_size;) { Create: s' = extract_field <v_out2, offset> Create: s = op <s, s'> // For non SLP cases } */ if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "Reduce using scalar code. "); vec_size_in_bits = tree_low_cst (TYPE_SIZE (vectype), 1); FOR_EACH_VEC_ELT (gimple, new_phis, i, new_phi) { if (gimple_code (new_phi) == GIMPLE_PHI) vec_temp = PHI_RESULT (new_phi); else vec_temp = gimple_assign_lhs (new_phi); rhs = build3 (BIT_FIELD_REF, scalar_type, vec_temp, bitsize, bitsize_zero_node); epilog_stmt = gimple_build_assign (new_scalar_dest, rhs); new_temp = make_ssa_name (new_scalar_dest, epilog_stmt); gimple_assign_set_lhs (epilog_stmt, new_temp); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); /* In SLP we don't need to apply reduction operation, so we just collect s' values in SCALAR_RESULTS. */ if (slp_reduc) VEC_safe_push (tree, heap, scalar_results, new_temp); for (bit_offset = element_bitsize; bit_offset < vec_size_in_bits; bit_offset += element_bitsize) { tree bitpos = bitsize_int (bit_offset); tree rhs = build3 (BIT_FIELD_REF, scalar_type, vec_temp, bitsize, bitpos); epilog_stmt = gimple_build_assign (new_scalar_dest, rhs); new_name = make_ssa_name (new_scalar_dest, epilog_stmt); gimple_assign_set_lhs (epilog_stmt, new_name); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); if (slp_reduc) { /* In SLP we don't need to apply reduction operation, so we just collect s' values in SCALAR_RESULTS. */ new_temp = new_name; VEC_safe_push (tree, heap, scalar_results, new_name); } else { epilog_stmt = gimple_build_assign_with_ops (code, new_scalar_dest, new_name, new_temp); new_temp = make_ssa_name (new_scalar_dest, epilog_stmt); gimple_assign_set_lhs (epilog_stmt, new_temp); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); } } } /* The only case where we need to reduce scalar results in SLP, is unrolling. If the size of SCALAR_RESULTS is greater than GROUP_SIZE, we reduce them combining elements modulo GROUP_SIZE. */ if (slp_reduc) { tree res, first_res, new_res; gimple new_stmt; /* Reduce multiple scalar results in case of SLP unrolling. */ for (j = group_size; VEC_iterate (tree, scalar_results, j, res); j++) { first_res = VEC_index (tree, scalar_results, j % group_size); new_stmt = gimple_build_assign_with_ops (code, new_scalar_dest, first_res, res); new_res = make_ssa_name (new_scalar_dest, new_stmt); gimple_assign_set_lhs (new_stmt, new_res); gsi_insert_before (&exit_gsi, new_stmt, GSI_SAME_STMT); VEC_replace (tree, scalar_results, j % group_size, new_res); } } else /* Not SLP - we have one scalar to keep in SCALAR_RESULTS. */ VEC_safe_push (tree, heap, scalar_results, new_temp); extract_scalar_result = false; } } /* 2.4 Extract the final scalar result. Create: s_out3 = extract_field <v_out2, bitpos> */ if (extract_scalar_result) { tree rhs; if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "extract scalar result"); if (BYTES_BIG_ENDIAN) bitpos = size_binop (MULT_EXPR, bitsize_int (TYPE_VECTOR_SUBPARTS (vectype) - 1), TYPE_SIZE (scalar_type)); else bitpos = bitsize_zero_node; rhs = build3 (BIT_FIELD_REF, scalar_type, new_temp, bitsize, bitpos); epilog_stmt = gimple_build_assign (new_scalar_dest, rhs); new_temp = make_ssa_name (new_scalar_dest, epilog_stmt); gimple_assign_set_lhs (epilog_stmt, new_temp); gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); VEC_safe_push (tree, heap, scalar_results, new_temp); } vect_finalize_reduction: if (double_reduc) loop = loop->inner; /* 2.5 Adjust the final result by the initial value of the reduction variable. (When such adjustment is not needed, then 'adjustment_def' is zero). For example, if code is PLUS we create: new_temp = loop_exit_def + adjustment_def */ if (adjustment_def) { gcc_assert (!slp_reduc); if (nested_in_vect_loop) { new_phi = VEC_index (gimple, new_phis, 0); gcc_assert (TREE_CODE (TREE_TYPE (adjustment_def)) == VECTOR_TYPE); expr = build2 (code, vectype, PHI_RESULT (new_phi), adjustment_def); new_dest = vect_create_destination_var (scalar_dest, vectype); } else { new_temp = VEC_index (tree, scalar_results, 0); gcc_assert (TREE_CODE (TREE_TYPE (adjustment_def)) != VECTOR_TYPE); expr = build2 (code, scalar_type, new_temp, adjustment_def); new_dest = vect_create_destination_var (scalar_dest, scalar_type); } epilog_stmt = gimple_build_assign (new_dest, expr); new_temp = make_ssa_name (new_dest, epilog_stmt); gimple_assign_set_lhs (epilog_stmt, new_temp); SSA_NAME_DEF_STMT (new_temp) = epilog_stmt; gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT); if (nested_in_vect_loop) { set_vinfo_for_stmt (epilog_stmt, new_stmt_vec_info (epilog_stmt, loop_vinfo, NULL)); STMT_VINFO_RELATED_STMT (vinfo_for_stmt (epilog_stmt)) = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (new_phi)); if (!double_reduc) VEC_quick_push (tree, scalar_results, new_temp); else VEC_replace (tree, scalar_results, 0, new_temp); } else VEC_replace (tree, scalar_results, 0, new_temp); VEC_replace (gimple, new_phis, 0, epilog_stmt); } /* 2.6 Handle the loop-exit phis. Replace the uses of scalar loop-exit phis with new adjusted scalar results, i.e., replace use <s_out0> with use <s_out4>. Transform: loop_exit: s_out0 = phi <s_loop> # (scalar) EXIT_PHI v_out1 = phi <VECT_DEF> # NEW_EXIT_PHI v_out2 = reduce <v_out1> s_out3 = extract_field <v_out2, 0> s_out4 = adjust_result <s_out3> use <s_out0> use <s_out0> into: loop_exit: s_out0 = phi <s_loop> # (scalar) EXIT_PHI v_out1 = phi <VECT_DEF> # NEW_EXIT_PHI v_out2 = reduce <v_out1> s_out3 = extract_field <v_out2, 0> s_out4 = adjust_result <s_out3> use <s_out4> use <s_out4> */ /* In SLP reduction chain we reduce vector results into one vector if necessary, hence we set here GROUP_SIZE to 1. SCALAR_DEST is the LHS of the last stmt in the reduction chain, since we are looking for the loop exit phi node. */ if (GROUP_FIRST_ELEMENT (vinfo_for_stmt (stmt))) { scalar_dest = gimple_assign_lhs (VEC_index (gimple, SLP_TREE_SCALAR_STMTS (slp_node), group_size - 1)); group_size = 1; } /* In SLP we may have several statements in NEW_PHIS and REDUCTION_PHIS (in case that GROUP_SIZE is greater than vectorization factor). Therefore, we need to match SCALAR_RESULTS with corresponding statements. The first (GROUP_SIZE / number of new vector stmts) scalar results correspond to the first vector stmt, etc. (RATIO is equal to (GROUP_SIZE / number of new vector stmts)). */ if (group_size > VEC_length (gimple, new_phis)) { ratio = group_size / VEC_length (gimple, new_phis); gcc_assert (!(group_size % VEC_length (gimple, new_phis))); } else ratio = 1; for (k = 0; k < group_size; k++) { if (k % ratio == 0) { epilog_stmt = VEC_index (gimple, new_phis, k / ratio); reduction_phi = VEC_index (gimple, reduction_phis, k / ratio); if (double_reduc) inner_phi = VEC_index (gimple, inner_phis, k / ratio); } if (slp_reduc) { gimple current_stmt = VEC_index (gimple, SLP_TREE_SCALAR_STMTS (slp_node), k); orig_stmt = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (current_stmt)); /* SLP statements can't participate in patterns. */ gcc_assert (!orig_stmt); scalar_dest = gimple_assign_lhs (current_stmt); } phis = VEC_alloc (gimple, heap, 3); /* Find the loop-closed-use at the loop exit of the original scalar result. (The reduction result is expected to have two immediate uses - one at the latch block, and one at the loop exit). */ FOR_EACH_IMM_USE_FAST (use_p, imm_iter, scalar_dest) if (!flow_bb_inside_loop_p (loop, gimple_bb (USE_STMT (use_p)))) VEC_safe_push (gimple, heap, phis, USE_STMT (use_p)); /* We expect to have found an exit_phi because of loop-closed-ssa form. */ gcc_assert (!VEC_empty (gimple, phis)); FOR_EACH_VEC_ELT (gimple, phis, i, exit_phi) { if (outer_loop) { stmt_vec_info exit_phi_vinfo = vinfo_for_stmt (exit_phi); gimple vect_phi; /* FORNOW. Currently not supporting the case that an inner-loop reduction is not used in the outer-loop (but only outside the outer-loop), unless it is double reduction. */ gcc_assert ((STMT_VINFO_RELEVANT_P (exit_phi_vinfo) && !STMT_VINFO_LIVE_P (exit_phi_vinfo)) || double_reduc); STMT_VINFO_VEC_STMT (exit_phi_vinfo) = epilog_stmt; if (!double_reduc || STMT_VINFO_DEF_TYPE (exit_phi_vinfo) != vect_double_reduction_def) continue; /* Handle double reduction: stmt1: s1 = phi <s0, s2> - double reduction phi (outer loop) stmt2: s3 = phi <s1, s4> - (regular) reduc phi (inner loop) stmt3: s4 = use (s3) - (regular) reduc stmt (inner loop) stmt4: s2 = phi <s4> - double reduction stmt (outer loop) At that point the regular reduction (stmt2 and stmt3) is already vectorized, as well as the exit phi node, stmt4. Here we vectorize the phi node of double reduction, stmt1, and update all relevant statements. */ /* Go through all the uses of s2 to find double reduction phi node, i.e., stmt1 above. */ orig_name = PHI_RESULT (exit_phi); FOR_EACH_IMM_USE_STMT (use_stmt, imm_iter, orig_name) { stmt_vec_info use_stmt_vinfo = vinfo_for_stmt (use_stmt); stmt_vec_info new_phi_vinfo; tree vect_phi_init, preheader_arg, vect_phi_res, init_def; basic_block bb = gimple_bb (use_stmt); gimple use; /* Check that USE_STMT is really double reduction phi node. */ if (gimple_code (use_stmt) != GIMPLE_PHI || gimple_phi_num_args (use_stmt) != 2 || !use_stmt_vinfo || STMT_VINFO_DEF_TYPE (use_stmt_vinfo) != vect_double_reduction_def || bb->loop_father != outer_loop) continue; /* Create vector phi node for double reduction: vs1 = phi <vs0, vs2> vs1 was created previously in this function by a call to vect_get_vec_def_for_operand and is stored in vec_initial_def; vs2 is defined by INNER_PHI, the vectorized EXIT_PHI; vs0 is created here. */ /* Create vector phi node. */ vect_phi = create_phi_node (vec_initial_def, bb); new_phi_vinfo = new_stmt_vec_info (vect_phi, loop_vec_info_for_loop (outer_loop), NULL); set_vinfo_for_stmt (vect_phi, new_phi_vinfo); /* Create vs0 - initial def of the double reduction phi. */ preheader_arg = PHI_ARG_DEF_FROM_EDGE (use_stmt, loop_preheader_edge (outer_loop)); init_def = get_initial_def_for_reduction (stmt, preheader_arg, NULL); vect_phi_init = vect_init_vector (use_stmt, init_def, vectype, NULL); /* Update phi node arguments with vs0 and vs2. */ add_phi_arg (vect_phi, vect_phi_init, loop_preheader_edge (outer_loop), UNKNOWN_LOCATION); add_phi_arg (vect_phi, PHI_RESULT (inner_phi), loop_latch_edge (outer_loop), UNKNOWN_LOCATION); if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "created double reduction phi " "node: "); print_gimple_stmt (vect_dump, vect_phi, 0, TDF_SLIM); } vect_phi_res = PHI_RESULT (vect_phi); /* Replace the use, i.e., set the correct vs1 in the regular reduction phi node. FORNOW, NCOPIES is always 1, so the loop is redundant. */ use = reduction_phi; for (j = 0; j < ncopies; j++) { edge pr_edge = loop_preheader_edge (loop); SET_PHI_ARG_DEF (use, pr_edge->dest_idx, vect_phi_res); use = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (use)); } } } } VEC_free (gimple, heap, phis); if (nested_in_vect_loop) { if (double_reduc) loop = outer_loop; else continue; } phis = VEC_alloc (gimple, heap, 3); /* Find the loop-closed-use at the loop exit of the original scalar result. (The reduction result is expected to have two immediate uses, one at the latch block, and one at the loop exit). For double reductions we are looking for exit phis of the outer loop. */ FOR_EACH_IMM_USE_FAST (use_p, imm_iter, scalar_dest) { if (!flow_bb_inside_loop_p (loop, gimple_bb (USE_STMT (use_p)))) VEC_safe_push (gimple, heap, phis, USE_STMT (use_p)); else { if (double_reduc && gimple_code (USE_STMT (use_p)) == GIMPLE_PHI) { tree phi_res = PHI_RESULT (USE_STMT (use_p)); FOR_EACH_IMM_USE_FAST (phi_use_p, phi_imm_iter, phi_res) { if (!flow_bb_inside_loop_p (loop, gimple_bb (USE_STMT (phi_use_p)))) VEC_safe_push (gimple, heap, phis, USE_STMT (phi_use_p)); } } } } FOR_EACH_VEC_ELT (gimple, phis, i, exit_phi) { /* Replace the uses: */ orig_name = PHI_RESULT (exit_phi); scalar_result = VEC_index (tree, scalar_results, k); FOR_EACH_IMM_USE_STMT (use_stmt, imm_iter, orig_name) FOR_EACH_IMM_USE_ON_STMT (use_p, imm_iter) SET_USE (use_p, scalar_result); } VEC_free (gimple, heap, phis); } VEC_free (tree, heap, scalar_results); VEC_free (gimple, heap, new_phis); } /* Function vectorizable_reduction. Check if STMT performs a reduction operation that can be vectorized. If VEC_STMT is also passed, vectorize the STMT: create a vectorized stmt to replace it, put it in VEC_STMT, and insert it at GSI. Return FALSE if not a vectorizable STMT, TRUE otherwise. This function also handles reduction idioms (patterns) that have been recognized in advance during vect_pattern_recog. In this case, STMT may be of this form: X = pattern_expr (arg0, arg1, ..., X) and it's STMT_VINFO_RELATED_STMT points to the last stmt in the original sequence that had been detected and replaced by the pattern-stmt (STMT). In some cases of reduction patterns, the type of the reduction variable X is different than the type of the other arguments of STMT. In such cases, the vectype that is used when transforming STMT into a vector stmt is different than the vectype that is used to determine the vectorization factor, because it consists of a different number of elements than the actual number of elements that are being operated upon in parallel. For example, consider an accumulation of shorts into an int accumulator. On some targets it's possible to vectorize this pattern operating on 8 shorts at a time (hence, the vectype for purposes of determining the vectorization factor should be V8HI); on the other hand, the vectype that is used to create the vector form is actually V4SI (the type of the result). Upon entry to this function, STMT_VINFO_VECTYPE records the vectype that indicates what is the actual level of parallelism (V8HI in the example), so that the right vectorization factor would be derived. This vectype corresponds to the type of arguments to the reduction stmt, and should *NOT* be used to create the vectorized stmt. The right vectype for the vectorized stmt is obtained from the type of the result X: get_vectype_for_scalar_type (TREE_TYPE (X)) This means that, contrary to "regular" reductions (or "regular" stmts in general), the following equation: STMT_VINFO_VECTYPE == get_vectype_for_scalar_type (TREE_TYPE (X)) does *NOT* necessarily hold for reduction patterns. */ bool vectorizable_reduction (gimple stmt, gimple_stmt_iterator *gsi, gimple *vec_stmt, slp_tree slp_node) { tree vec_dest; tree scalar_dest; tree loop_vec_def0 = NULL_TREE, loop_vec_def1 = NULL_TREE; stmt_vec_info stmt_info = vinfo_for_stmt (stmt); tree vectype_out = STMT_VINFO_VECTYPE (stmt_info); tree vectype_in = NULL_TREE; loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info); struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); enum tree_code code, orig_code, epilog_reduc_code; enum machine_mode vec_mode; int op_type; optab optab, reduc_optab; tree new_temp = NULL_TREE; tree def; gimple def_stmt; enum vect_def_type dt; gimple new_phi = NULL; tree scalar_type; bool is_simple_use; gimple orig_stmt; stmt_vec_info orig_stmt_info; tree expr = NULL_TREE; int i; int ncopies; int epilog_copies; stmt_vec_info prev_stmt_info, prev_phi_info; bool single_defuse_cycle = false; tree reduc_def = NULL_TREE; gimple new_stmt = NULL; int j; tree ops[3]; bool nested_cycle = false, found_nested_cycle_def = false; gimple reduc_def_stmt = NULL; /* The default is that the reduction variable is the last in statement. */ int reduc_index = 2; bool double_reduc = false, dummy; basic_block def_bb; struct loop * def_stmt_loop, *outer_loop = NULL; tree def_arg; gimple def_arg_stmt; VEC (tree, heap) *vec_oprnds0 = NULL, *vec_oprnds1 = NULL, *vect_defs = NULL; VEC (gimple, heap) *phis = NULL; int vec_num; tree def0, def1, tem, op0, op1 = NULL_TREE; /* In case of reduction chain we switch to the first stmt in the chain, but we don't update STMT_INFO, since only the last stmt is marked as reduction and has reduction properties. */ if (GROUP_FIRST_ELEMENT (vinfo_for_stmt (stmt))) stmt = GROUP_FIRST_ELEMENT (stmt_info); if (nested_in_vect_loop_p (loop, stmt)) { outer_loop = loop; loop = loop->inner; nested_cycle = true; } /* 1. Is vectorizable reduction? */ /* Not supportable if the reduction variable is used in the loop, unless it's a reduction chain. */ if (STMT_VINFO_RELEVANT (stmt_info) > vect_used_in_outer && !GROUP_FIRST_ELEMENT (stmt_info)) return false; /* Reductions that are not used even in an enclosing outer-loop, are expected to be "live" (used out of the loop). */ if (STMT_VINFO_RELEVANT (stmt_info) == vect_unused_in_scope && !STMT_VINFO_LIVE_P (stmt_info)) return false; /* Make sure it was already recognized as a reduction computation. */ if (STMT_VINFO_DEF_TYPE (stmt_info) != vect_reduction_def && STMT_VINFO_DEF_TYPE (stmt_info) != vect_nested_cycle) return false; /* 2. Has this been recognized as a reduction pattern? Check if STMT represents a pattern that has been recognized in earlier analysis stages. For stmts that represent a pattern, the STMT_VINFO_RELATED_STMT field records the last stmt in the original sequence that constitutes the pattern. */ orig_stmt = STMT_VINFO_RELATED_STMT (stmt_info); if (orig_stmt) { orig_stmt_info = vinfo_for_stmt (orig_stmt); gcc_assert (STMT_VINFO_RELATED_STMT (orig_stmt_info) == stmt); gcc_assert (STMT_VINFO_IN_PATTERN_P (orig_stmt_info)); gcc_assert (!STMT_VINFO_IN_PATTERN_P (stmt_info)); } /* 3. Check the operands of the operation. The first operands are defined inside the loop body. The last operand is the reduction variable, which is defined by the loop-header-phi. */ gcc_assert (is_gimple_assign (stmt)); /* Flatten RHS. */ switch (get_gimple_rhs_class (gimple_assign_rhs_code (stmt))) { case GIMPLE_SINGLE_RHS: op_type = TREE_OPERAND_LENGTH (gimple_assign_rhs1 (stmt)); if (op_type == ternary_op) { tree rhs = gimple_assign_rhs1 (stmt); ops[0] = TREE_OPERAND (rhs, 0); ops[1] = TREE_OPERAND (rhs, 1); ops[2] = TREE_OPERAND (rhs, 2); code = TREE_CODE (rhs); } else return false; break; case GIMPLE_BINARY_RHS: code = gimple_assign_rhs_code (stmt); op_type = TREE_CODE_LENGTH (code); gcc_assert (op_type == binary_op); ops[0] = gimple_assign_rhs1 (stmt); ops[1] = gimple_assign_rhs2 (stmt); break; case GIMPLE_TERNARY_RHS: code = gimple_assign_rhs_code (stmt); op_type = TREE_CODE_LENGTH (code); gcc_assert (op_type == ternary_op); ops[0] = gimple_assign_rhs1 (stmt); ops[1] = gimple_assign_rhs2 (stmt); ops[2] = gimple_assign_rhs3 (stmt); break; case GIMPLE_UNARY_RHS: return false; default: gcc_unreachable (); } if (code == COND_EXPR && slp_node) return false; scalar_dest = gimple_assign_lhs (stmt); scalar_type = TREE_TYPE (scalar_dest); if (!POINTER_TYPE_P (scalar_type) && !INTEGRAL_TYPE_P (scalar_type) && !SCALAR_FLOAT_TYPE_P (scalar_type)) return false; /* Do not try to vectorize bit-precision reductions. */ if ((TYPE_PRECISION (scalar_type) != GET_MODE_PRECISION (TYPE_MODE (scalar_type)))) return false; /* All uses but the last are expected to be defined in the loop. The last use is the reduction variable. In case of nested cycle this assumption is not true: we use reduc_index to record the index of the reduction variable. */ for (i = 0; i < op_type-1; i++) { /* The condition of COND_EXPR is checked in vectorizable_condition(). */ if (i == 0 && code == COND_EXPR) continue; is_simple_use = vect_is_simple_use_1 (ops[i], stmt, loop_vinfo, NULL, &def_stmt, &def, &dt, &tem); if (!vectype_in) vectype_in = tem; gcc_assert (is_simple_use); if (dt != vect_internal_def && dt != vect_external_def && dt != vect_constant_def && dt != vect_induction_def && !(dt == vect_nested_cycle && nested_cycle)) return false; if (dt == vect_nested_cycle) { found_nested_cycle_def = true; reduc_def_stmt = def_stmt; reduc_index = i; } } is_simple_use = vect_is_simple_use_1 (ops[i], stmt, loop_vinfo, NULL, &def_stmt, &def, &dt, &tem); if (!vectype_in) vectype_in = tem; gcc_assert (is_simple_use); gcc_assert (dt == vect_reduction_def || dt == vect_nested_cycle || ((dt == vect_internal_def || dt == vect_external_def || dt == vect_constant_def || dt == vect_induction_def) && nested_cycle && found_nested_cycle_def)); if (!found_nested_cycle_def) reduc_def_stmt = def_stmt; gcc_assert (gimple_code (reduc_def_stmt) == GIMPLE_PHI); if (orig_stmt) gcc_assert (orig_stmt == vect_is_simple_reduction (loop_vinfo, reduc_def_stmt, !nested_cycle, &dummy)); else { gimple tmp = vect_is_simple_reduction (loop_vinfo, reduc_def_stmt, !nested_cycle, &dummy); /* We changed STMT to be the first stmt in reduction chain, hence we check that in this case the first element in the chain is STMT. */ gcc_assert (stmt == tmp || GROUP_FIRST_ELEMENT (vinfo_for_stmt (tmp)) == stmt); } if (STMT_VINFO_LIVE_P (vinfo_for_stmt (reduc_def_stmt))) return false; if (slp_node || PURE_SLP_STMT (stmt_info)) ncopies = 1; else ncopies = (LOOP_VINFO_VECT_FACTOR (loop_vinfo) / TYPE_VECTOR_SUBPARTS (vectype_in)); gcc_assert (ncopies >= 1); vec_mode = TYPE_MODE (vectype_in); if (code == COND_EXPR) { if (!vectorizable_condition (stmt, gsi, NULL, ops[reduc_index], 0, NULL)) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "unsupported condition in reduction"); return false; } } else { /* 4. Supportable by target? */ /* 4.1. check support for the operation in the loop */ optab = optab_for_tree_code (code, vectype_in, optab_default); if (!optab) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "no optab."); return false; } if (optab_handler (optab, vec_mode) == CODE_FOR_nothing) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "op not supported by target."); if (GET_MODE_SIZE (vec_mode) != UNITS_PER_WORD || LOOP_VINFO_VECT_FACTOR (loop_vinfo) < vect_min_worthwhile_factor (code)) return false; if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "proceeding using word mode."); } /* Worthwhile without SIMD support? */ if (!VECTOR_MODE_P (TYPE_MODE (vectype_in)) && LOOP_VINFO_VECT_FACTOR (loop_vinfo) < vect_min_worthwhile_factor (code)) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "not worthwhile without SIMD support."); return false; } } /* 4.2. Check support for the epilog operation. If STMT represents a reduction pattern, then the type of the reduction variable may be different than the type of the rest of the arguments. For example, consider the case of accumulation of shorts into an int accumulator; The original code: S1: int_a = (int) short_a; orig_stmt-> S2: int_acc = plus <int_a ,int_acc>; was replaced with: STMT: int_acc = widen_sum <short_a, int_acc> This means that: 1. The tree-code that is used to create the vector operation in the epilog code (that reduces the partial results) is not the tree-code of STMT, but is rather the tree-code of the original stmt from the pattern that STMT is replacing. I.e, in the example above we want to use 'widen_sum' in the loop, but 'plus' in the epilog. 2. The type (mode) we use to check available target support for the vector operation to be created in the *epilog*, is determined by the type of the reduction variable (in the example above we'd check this: optab_handler (plus_optab, vect_int_mode])). However the type (mode) we use to check available target support for the vector operation to be created *inside the loop*, is determined by the type of the other arguments to STMT (in the example we'd check this: optab_handler (widen_sum_optab, vect_short_mode)). This is contrary to "regular" reductions, in which the types of all the arguments are the same as the type of the reduction variable. For "regular" reductions we can therefore use the same vector type (and also the same tree-code) when generating the epilog code and when generating the code inside the loop. */ if (orig_stmt) { /* This is a reduction pattern: get the vectype from the type of the reduction variable, and get the tree-code from orig_stmt. */ orig_code = gimple_assign_rhs_code (orig_stmt); gcc_assert (vectype_out); vec_mode = TYPE_MODE (vectype_out); } else { /* Regular reduction: use the same vectype and tree-code as used for the vector code inside the loop can be used for the epilog code. */ orig_code = code; } if (nested_cycle) { def_bb = gimple_bb (reduc_def_stmt); def_stmt_loop = def_bb->loop_father; def_arg = PHI_ARG_DEF_FROM_EDGE (reduc_def_stmt, loop_preheader_edge (def_stmt_loop)); if (TREE_CODE (def_arg) == SSA_NAME && (def_arg_stmt = SSA_NAME_DEF_STMT (def_arg)) && gimple_code (def_arg_stmt) == GIMPLE_PHI && flow_bb_inside_loop_p (outer_loop, gimple_bb (def_arg_stmt)) && vinfo_for_stmt (def_arg_stmt) && STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def_arg_stmt)) == vect_double_reduction_def) double_reduc = true; } epilog_reduc_code = ERROR_MARK; if (reduction_code_for_scalar_code (orig_code, &epilog_reduc_code)) { reduc_optab = optab_for_tree_code (epilog_reduc_code, vectype_out, optab_default); if (!reduc_optab) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "no optab for reduction."); epilog_reduc_code = ERROR_MARK; } if (reduc_optab && optab_handler (reduc_optab, vec_mode) == CODE_FOR_nothing) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "reduc op not supported by target."); epilog_reduc_code = ERROR_MARK; } } else { if (!nested_cycle || double_reduc) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "no reduc code for scalar code."); return false; } } if (double_reduc && ncopies > 1) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "multiple types in double reduction"); return false; } /* In case of widenning multiplication by a constant, we update the type of the constant to be the type of the other operand. We check that the constant fits the type in the pattern recognition pass. */ if (code == DOT_PROD_EXPR && !types_compatible_p (TREE_TYPE (ops[0]), TREE_TYPE (ops[1]))) { if (TREE_CODE (ops[0]) == INTEGER_CST) ops[0] = fold_convert (TREE_TYPE (ops[1]), ops[0]); else if (TREE_CODE (ops[1]) == INTEGER_CST) ops[1] = fold_convert (TREE_TYPE (ops[0]), ops[1]); else { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "invalid types in dot-prod"); return false; } } if (!vec_stmt) /* transformation not required. */ { if (!vect_model_reduction_cost (stmt_info, epilog_reduc_code, ncopies)) return false; STMT_VINFO_TYPE (stmt_info) = reduc_vec_info_type; return true; } /** Transform. **/ if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "transform reduction."); /* FORNOW: Multiple types are not supported for condition. */ if (code == COND_EXPR) gcc_assert (ncopies == 1); /* Create the destination vector */ vec_dest = vect_create_destination_var (scalar_dest, vectype_out); /* In case the vectorization factor (VF) is bigger than the number of elements that we can fit in a vectype (nunits), we have to generate more than one vector stmt - i.e - we need to "unroll" the vector stmt by a factor VF/nunits. For more details see documentation in vectorizable_operation. */ /* If the reduction is used in an outer loop we need to generate VF intermediate results, like so (e.g. for ncopies=2): r0 = phi (init, r0) r1 = phi (init, r1) r0 = x0 + r0; r1 = x1 + r1; (i.e. we generate VF results in 2 registers). In this case we have a separate def-use cycle for each copy, and therefore for each copy we get the vector def for the reduction variable from the respective phi node created for this copy. Otherwise (the reduction is unused in the loop nest), we can combine together intermediate results, like so (e.g. for ncopies=2): r = phi (init, r) r = x0 + r; r = x1 + r; (i.e. we generate VF/2 results in a single register). In this case for each copy we get the vector def for the reduction variable from the vectorized reduction operation generated in the previous iteration. */ if (STMT_VINFO_RELEVANT (stmt_info) == vect_unused_in_scope) { single_defuse_cycle = true; epilog_copies = 1; } else epilog_copies = ncopies; prev_stmt_info = NULL; prev_phi_info = NULL; if (slp_node) { vec_num = SLP_TREE_NUMBER_OF_VEC_STMTS (slp_node); gcc_assert (TYPE_VECTOR_SUBPARTS (vectype_out) == TYPE_VECTOR_SUBPARTS (vectype_in)); } else { vec_num = 1; vec_oprnds0 = VEC_alloc (tree, heap, 1); if (op_type == ternary_op) vec_oprnds1 = VEC_alloc (tree, heap, 1); } phis = VEC_alloc (gimple, heap, vec_num); vect_defs = VEC_alloc (tree, heap, vec_num); if (!slp_node) VEC_quick_push (tree, vect_defs, NULL_TREE); for (j = 0; j < ncopies; j++) { if (j == 0 || !single_defuse_cycle) { for (i = 0; i < vec_num; i++) { /* Create the reduction-phi that defines the reduction operand. */ new_phi = create_phi_node (vec_dest, loop->header); set_vinfo_for_stmt (new_phi, new_stmt_vec_info (new_phi, loop_vinfo, NULL)); if (j == 0 || slp_node) VEC_quick_push (gimple, phis, new_phi); } } if (code == COND_EXPR) { gcc_assert (!slp_node); vectorizable_condition (stmt, gsi, vec_stmt, PHI_RESULT (VEC_index (gimple, phis, 0)), reduc_index, NULL); /* Multiple types are not supported for condition. */ break; } /* Handle uses. */ if (j == 0) { op0 = ops[!reduc_index]; if (op_type == ternary_op) { if (reduc_index == 0) op1 = ops[2]; else op1 = ops[1]; } if (slp_node) vect_get_vec_defs (op0, op1, stmt, &vec_oprnds0, &vec_oprnds1, slp_node, -1); else { loop_vec_def0 = vect_get_vec_def_for_operand (ops[!reduc_index], stmt, NULL); VEC_quick_push (tree, vec_oprnds0, loop_vec_def0); if (op_type == ternary_op) { loop_vec_def1 = vect_get_vec_def_for_operand (op1, stmt, NULL); VEC_quick_push (tree, vec_oprnds1, loop_vec_def1); } } } else { if (!slp_node) { enum vect_def_type dt; gimple dummy_stmt; tree dummy; vect_is_simple_use (ops[!reduc_index], stmt, loop_vinfo, NULL, &dummy_stmt, &dummy, &dt); loop_vec_def0 = vect_get_vec_def_for_stmt_copy (dt, loop_vec_def0); VEC_replace (tree, vec_oprnds0, 0, loop_vec_def0); if (op_type == ternary_op) { vect_is_simple_use (op1, stmt, loop_vinfo, NULL, &dummy_stmt, &dummy, &dt); loop_vec_def1 = vect_get_vec_def_for_stmt_copy (dt, loop_vec_def1); VEC_replace (tree, vec_oprnds1, 0, loop_vec_def1); } } if (single_defuse_cycle) reduc_def = gimple_assign_lhs (new_stmt); STMT_VINFO_RELATED_STMT (prev_phi_info) = new_phi; } FOR_EACH_VEC_ELT (tree, vec_oprnds0, i, def0) { if (slp_node) reduc_def = PHI_RESULT (VEC_index (gimple, phis, i)); else { if (!single_defuse_cycle || j == 0) reduc_def = PHI_RESULT (new_phi); } def1 = ((op_type == ternary_op) ? VEC_index (tree, vec_oprnds1, i) : NULL); if (op_type == binary_op) { if (reduc_index == 0) expr = build2 (code, vectype_out, reduc_def, def0); else expr = build2 (code, vectype_out, def0, reduc_def); } else { if (reduc_index == 0) expr = build3 (code, vectype_out, reduc_def, def0, def1); else { if (reduc_index == 1) expr = build3 (code, vectype_out, def0, reduc_def, def1); else expr = build3 (code, vectype_out, def0, def1, reduc_def); } } new_stmt = gimple_build_assign (vec_dest, expr); new_temp = make_ssa_name (vec_dest, new_stmt); gimple_assign_set_lhs (new_stmt, new_temp); vect_finish_stmt_generation (stmt, new_stmt, gsi); if (slp_node) { VEC_quick_push (gimple, SLP_TREE_VEC_STMTS (slp_node), new_stmt); VEC_quick_push (tree, vect_defs, new_temp); } else VEC_replace (tree, vect_defs, 0, new_temp); } if (slp_node) continue; if (j == 0) STMT_VINFO_VEC_STMT (stmt_info) = *vec_stmt = new_stmt; else STMT_VINFO_RELATED_STMT (prev_stmt_info) = new_stmt; prev_stmt_info = vinfo_for_stmt (new_stmt); prev_phi_info = vinfo_for_stmt (new_phi); } /* Finalize the reduction-phi (set its arguments) and create the epilog reduction code. */ if ((!single_defuse_cycle || code == COND_EXPR) && !slp_node) { new_temp = gimple_assign_lhs (*vec_stmt); VEC_replace (tree, vect_defs, 0, new_temp); } vect_create_epilog_for_reduction (vect_defs, stmt, epilog_copies, epilog_reduc_code, phis, reduc_index, double_reduc, slp_node); VEC_free (gimple, heap, phis); VEC_free (tree, heap, vec_oprnds0); if (vec_oprnds1) VEC_free (tree, heap, vec_oprnds1); return true; } /* Function vect_min_worthwhile_factor. For a loop where we could vectorize the operation indicated by CODE, return the minimum vectorization factor that makes it worthwhile to use generic vectors. */ int vect_min_worthwhile_factor (enum tree_code code) { switch (code) { case PLUS_EXPR: case MINUS_EXPR: case NEGATE_EXPR: return 4; case BIT_AND_EXPR: case BIT_IOR_EXPR: case BIT_XOR_EXPR: case BIT_NOT_EXPR: return 2; default: return INT_MAX; } } /* Function vectorizable_induction Check if PHI performs an induction computation that can be vectorized. If VEC_STMT is also passed, vectorize the induction PHI: create a vectorized phi to replace it, put it in VEC_STMT, and add it to the same basic block. Return FALSE if not a vectorizable STMT, TRUE otherwise. */ bool vectorizable_induction (gimple phi, gimple_stmt_iterator *gsi ATTRIBUTE_UNUSED, gimple *vec_stmt) { stmt_vec_info stmt_info = vinfo_for_stmt (phi); tree vectype = STMT_VINFO_VECTYPE (stmt_info); loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info); struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); int nunits = TYPE_VECTOR_SUBPARTS (vectype); int ncopies = LOOP_VINFO_VECT_FACTOR (loop_vinfo) / nunits; tree vec_def; gcc_assert (ncopies >= 1); /* FORNOW. This restriction should be relaxed. */ if (nested_in_vect_loop_p (loop, phi) && ncopies > 1) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "multiple types in nested loop."); return false; } if (!STMT_VINFO_RELEVANT_P (stmt_info)) return false; /* FORNOW: SLP not supported. */ if (STMT_SLP_TYPE (stmt_info)) return false; gcc_assert (STMT_VINFO_DEF_TYPE (stmt_info) == vect_induction_def); if (gimple_code (phi) != GIMPLE_PHI) return false; if (!vec_stmt) /* transformation not required. */ { STMT_VINFO_TYPE (stmt_info) = induc_vec_info_type; if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "=== vectorizable_induction ==="); vect_model_induction_cost (stmt_info, ncopies); return true; } /** Transform. **/ if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "transform induction phi."); vec_def = get_initial_def_for_induction (phi); *vec_stmt = SSA_NAME_DEF_STMT (vec_def); return true; } /* Function vectorizable_live_operation. STMT computes a value that is used outside the loop. Check if it can be supported. */ bool vectorizable_live_operation (gimple stmt, gimple_stmt_iterator *gsi ATTRIBUTE_UNUSED, gimple *vec_stmt ATTRIBUTE_UNUSED) { stmt_vec_info stmt_info = vinfo_for_stmt (stmt); loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info); struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); int i; int op_type; tree op; tree def; gimple def_stmt; enum vect_def_type dt; enum tree_code code; enum gimple_rhs_class rhs_class; gcc_assert (STMT_VINFO_LIVE_P (stmt_info)); if (STMT_VINFO_DEF_TYPE (stmt_info) == vect_reduction_def) return false; if (!is_gimple_assign (stmt)) return false; if (TREE_CODE (gimple_assign_lhs (stmt)) != SSA_NAME) return false; /* FORNOW. CHECKME. */ if (nested_in_vect_loop_p (loop, stmt)) return false; code = gimple_assign_rhs_code (stmt); op_type = TREE_CODE_LENGTH (code); rhs_class = get_gimple_rhs_class (code); gcc_assert (rhs_class != GIMPLE_UNARY_RHS || op_type == unary_op); gcc_assert (rhs_class != GIMPLE_BINARY_RHS || op_type == binary_op); /* FORNOW: support only if all uses are invariant. This means that the scalar operations can remain in place, unvectorized. The original last scalar value that they compute will be used. */ for (i = 0; i < op_type; i++) { if (rhs_class == GIMPLE_SINGLE_RHS) op = TREE_OPERAND (gimple_op (stmt, 1), i); else op = gimple_op (stmt, i + 1); if (op && !vect_is_simple_use (op, stmt, loop_vinfo, NULL, &def_stmt, &def, &dt)) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "use not simple."); return false; } if (dt != vect_external_def && dt != vect_constant_def) return false; } /* No transformation is required for the cases we currently support. */ return true; } /* Kill any debug uses outside LOOP of SSA names defined in STMT. */ static void vect_loop_kill_debug_uses (struct loop *loop, gimple stmt) { ssa_op_iter op_iter; imm_use_iterator imm_iter; def_operand_p def_p; gimple ustmt; FOR_EACH_PHI_OR_STMT_DEF (def_p, stmt, op_iter, SSA_OP_DEF) { FOR_EACH_IMM_USE_STMT (ustmt, imm_iter, DEF_FROM_PTR (def_p)) { basic_block bb; if (!is_gimple_debug (ustmt)) continue; bb = gimple_bb (ustmt); if (!flow_bb_inside_loop_p (loop, bb)) { if (gimple_debug_bind_p (ustmt)) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "killing debug use"); gimple_debug_bind_reset_value (ustmt); update_stmt (ustmt); } else gcc_unreachable (); } } } } /* Function vect_transform_loop. The analysis phase has determined that the loop is vectorizable. Vectorize the loop - created vectorized stmts to replace the scalar stmts in the loop, and update the loop exit condition. */ void vect_transform_loop (loop_vec_info loop_vinfo) { struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo); basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo); int nbbs = loop->num_nodes; gimple_stmt_iterator si; int i; tree ratio = NULL; int vectorization_factor = LOOP_VINFO_VECT_FACTOR (loop_vinfo); bool strided_store; bool slp_scheduled = false; unsigned int nunits; tree cond_expr = NULL_TREE; gimple_seq cond_expr_stmt_list = NULL; bool do_peeling_for_loop_bound; gimple stmt, pattern_stmt; gimple_seq pattern_def_seq = NULL; gimple_stmt_iterator pattern_def_si = gsi_start (NULL); bool transform_pattern_stmt = false; if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "=== vec_transform_loop ==="); /* Peel the loop if there are data refs with unknown alignment. Only one data ref with unknown store is allowed. */ if (LOOP_PEELING_FOR_ALIGNMENT (loop_vinfo)) vect_do_peeling_for_alignment (loop_vinfo); do_peeling_for_loop_bound = (!LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo) || (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo) && LOOP_VINFO_INT_NITERS (loop_vinfo) % vectorization_factor != 0) || LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo)); if (LOOP_REQUIRES_VERSIONING_FOR_ALIGNMENT (loop_vinfo) || LOOP_REQUIRES_VERSIONING_FOR_ALIAS (loop_vinfo)) vect_loop_versioning (loop_vinfo, !do_peeling_for_loop_bound, &cond_expr, &cond_expr_stmt_list); /* If the loop has a symbolic number of iterations 'n' (i.e. it's not a compile time constant), or it is a constant that doesn't divide by the vectorization factor, then an epilog loop needs to be created. We therefore duplicate the loop: the original loop will be vectorized, and will compute the first (n/VF) iterations. The second copy of the loop will remain scalar and will compute the remaining (n%VF) iterations. (VF is the vectorization factor). */ if (do_peeling_for_loop_bound) vect_do_peeling_for_loop_bound (loop_vinfo, &ratio, cond_expr, cond_expr_stmt_list); else ratio = build_int_cst (TREE_TYPE (LOOP_VINFO_NITERS (loop_vinfo)), LOOP_VINFO_INT_NITERS (loop_vinfo) / vectorization_factor); /* 1) Make sure the loop header has exactly two entries 2) Make sure we have a preheader basic block. */ gcc_assert (EDGE_COUNT (loop->header->preds) == 2); split_edge (loop_preheader_edge (loop)); /* FORNOW: the vectorizer supports only loops which body consist of one basic block (header + empty latch). When the vectorizer will support more involved loop forms, the order by which the BBs are traversed need to be reconsidered. */ for (i = 0; i < nbbs; i++) { basic_block bb = bbs[i]; stmt_vec_info stmt_info; gimple phi; for (si = gsi_start_phis (bb); !gsi_end_p (si); gsi_next (&si)) { phi = gsi_stmt (si); if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "------>vectorizing phi: "); print_gimple_stmt (vect_dump, phi, 0, TDF_SLIM); } stmt_info = vinfo_for_stmt (phi); if (!stmt_info) continue; if (MAY_HAVE_DEBUG_STMTS && !STMT_VINFO_LIVE_P (stmt_info)) vect_loop_kill_debug_uses (loop, phi); if (!STMT_VINFO_RELEVANT_P (stmt_info) && !STMT_VINFO_LIVE_P (stmt_info)) continue; if ((TYPE_VECTOR_SUBPARTS (STMT_VINFO_VECTYPE (stmt_info)) != (unsigned HOST_WIDE_INT) vectorization_factor) && vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "multiple-types."); if (STMT_VINFO_DEF_TYPE (stmt_info) == vect_induction_def) { if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "transform phi."); vect_transform_stmt (phi, NULL, NULL, NULL, NULL); } } pattern_stmt = NULL; for (si = gsi_start_bb (bb); !gsi_end_p (si) || transform_pattern_stmt;) { bool is_store; if (transform_pattern_stmt) stmt = pattern_stmt; else stmt = gsi_stmt (si); if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "------>vectorizing statement: "); print_gimple_stmt (vect_dump, stmt, 0, TDF_SLIM); } stmt_info = vinfo_for_stmt (stmt); /* vector stmts created in the outer-loop during vectorization of stmts in an inner-loop may not have a stmt_info, and do not need to be vectorized. */ if (!stmt_info) { gsi_next (&si); continue; } if (MAY_HAVE_DEBUG_STMTS && !STMT_VINFO_LIVE_P (stmt_info)) vect_loop_kill_debug_uses (loop, stmt); if (!STMT_VINFO_RELEVANT_P (stmt_info) && !STMT_VINFO_LIVE_P (stmt_info)) { if (STMT_VINFO_IN_PATTERN_P (stmt_info) && (pattern_stmt = STMT_VINFO_RELATED_STMT (stmt_info)) && (STMT_VINFO_RELEVANT_P (vinfo_for_stmt (pattern_stmt)) || STMT_VINFO_LIVE_P (vinfo_for_stmt (pattern_stmt)))) { stmt = pattern_stmt; stmt_info = vinfo_for_stmt (stmt); } else { gsi_next (&si); continue; } } else if (STMT_VINFO_IN_PATTERN_P (stmt_info) && (pattern_stmt = STMT_VINFO_RELATED_STMT (stmt_info)) && (STMT_VINFO_RELEVANT_P (vinfo_for_stmt (pattern_stmt)) || STMT_VINFO_LIVE_P (vinfo_for_stmt (pattern_stmt)))) transform_pattern_stmt = true; /* If pattern statement has def stmts, vectorize them too. */ if (is_pattern_stmt_p (stmt_info)) { if (pattern_def_seq == NULL) { pattern_def_seq = STMT_VINFO_PATTERN_DEF_SEQ (stmt_info); pattern_def_si = gsi_start (pattern_def_seq); } else if (!gsi_end_p (pattern_def_si)) gsi_next (&pattern_def_si); if (pattern_def_seq != NULL) { gimple pattern_def_stmt = NULL; stmt_vec_info pattern_def_stmt_info = NULL; while (!gsi_end_p (pattern_def_si)) { pattern_def_stmt = gsi_stmt (pattern_def_si); pattern_def_stmt_info = vinfo_for_stmt (pattern_def_stmt); if (STMT_VINFO_RELEVANT_P (pattern_def_stmt_info) || STMT_VINFO_LIVE_P (pattern_def_stmt_info)) break; gsi_next (&pattern_def_si); } if (!gsi_end_p (pattern_def_si)) { if (vect_print_dump_info (REPORT_DETAILS)) { fprintf (vect_dump, "==> vectorizing pattern def" " stmt: "); print_gimple_stmt (vect_dump, pattern_def_stmt, 0, TDF_SLIM); } stmt = pattern_def_stmt; stmt_info = pattern_def_stmt_info; } else { pattern_def_si = gsi_start (NULL); transform_pattern_stmt = false; } } else transform_pattern_stmt = false; } gcc_assert (STMT_VINFO_VECTYPE (stmt_info)); nunits = (unsigned int) TYPE_VECTOR_SUBPARTS ( STMT_VINFO_VECTYPE (stmt_info)); if (!STMT_SLP_TYPE (stmt_info) && nunits != (unsigned int) vectorization_factor && vect_print_dump_info (REPORT_DETAILS)) /* For SLP VF is set according to unrolling factor, and not to vector size, hence for SLP this print is not valid. */ fprintf (vect_dump, "multiple-types."); /* SLP. Schedule all the SLP instances when the first SLP stmt is reached. */ if (STMT_SLP_TYPE (stmt_info)) { if (!slp_scheduled) { slp_scheduled = true; if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "=== scheduling SLP instances ==="); vect_schedule_slp (loop_vinfo, NULL); } /* Hybrid SLP stmts must be vectorized in addition to SLP. */ if (!vinfo_for_stmt (stmt) || PURE_SLP_STMT (stmt_info)) { if (!transform_pattern_stmt && gsi_end_p (pattern_def_si)) { pattern_def_seq = NULL; gsi_next (&si); } continue; } } /* -------- vectorize statement ------------ */ if (vect_print_dump_info (REPORT_DETAILS)) fprintf (vect_dump, "transform statement."); strided_store = false; is_store = vect_transform_stmt (stmt, &si, &strided_store, NULL, NULL); if (is_store) { if (STMT_VINFO_STRIDED_ACCESS (stmt_info)) { /* Interleaving. If IS_STORE is TRUE, the vectorization of the interleaving chain was completed - free all the stores in the chain. */ gsi_next (&si); vect_remove_stores (GROUP_FIRST_ELEMENT (stmt_info)); continue; } else { /* Free the attached stmt_vec_info and remove the stmt. */ free_stmt_vec_info (gsi_stmt (si)); gsi_remove (&si, true); continue; } } if (!transform_pattern_stmt && gsi_end_p (pattern_def_si)) { pattern_def_seq = NULL; gsi_next (&si); } } /* stmts in BB */ } /* BBs in loop */ slpeel_make_loop_iterate_ntimes (loop, ratio); /* The memory tags and pointers in vectorized statements need to have their SSA forms updated. FIXME, why can't this be delayed until all the loops have been transformed? */ update_ssa (TODO_update_ssa); if (vect_print_dump_info (REPORT_VECTORIZED_LOCATIONS)) fprintf (vect_dump, "LOOP VECTORIZED."); if (loop->inner && vect_print_dump_info (REPORT_VECTORIZED_LOCATIONS)) fprintf (vect_dump, "OUTER LOOP VECTORIZED."); }
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