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      C++
    
    
      library
    
    
      profile
    
  

 
Profile Mode
 
 

  Intro
  
  Goal: Give performance improvement advice based on
  recognition of suboptimal usage patterns of the standard library.
  
 
  
  Method: Wrap the standard library code.  Insert
  calls to an instrumentation library to record the internal state of
  various components at interesting entry/exit points to/from the standard
  library.  Process trace, recognize suboptimal patterns, give advice.
  For details, see
  paper presented at
   CGO 2009.
  
  
  Strengths: 

  
  Unintrusive solution.  The application code does not require any
  modification.
  
   The advice is call context sensitive, thus capable of
  identifying precisely interesting dynamic performance behavior.
  
  
  The overhead model is pay-per-view.  When you turn off a diagnostic class
  at compile time, its overhead disappears.
  

  
  
  Drawbacks: 

  
  You must recompile the application code with custom options.
  
  You must run the application on representative input.
  The advice is input dependent.
  
  
  The execution time will increase, in some cases by factors.
  

  
 
 

  Using the Profile Mode
 
  
  This is the anticipated common workflow for program foo.cc:

$ cat foo.cc
#include <vector>
int main() {
  vector<int> v;
  for (int k = 0; k < 1024; ++k) v.insert(v.begin(), k);
}
 
$ g++ -D_GLIBCXX_PROFILE foo.cc
$ ./a.out
$ cat libstdcxx-profile.txt
vector-to-list: improvement = 5: call stack = 0x804842c ...
    : advice = change std::vector to std::list
vector-size: improvement = 3: call stack = 0x804842c ...
    : advice = change initial container size from 0 to 1024

  
 
  
  Anatomy of a warning:
  
  
  
  Warning id.  This is a short descriptive string for the class
  that this warning belongs to.  E.g., "vector-to-list".
  
  
  
  
  Estimated improvement.  This is an approximation of the benefit expected
  from implementing the change suggested by the warning.  It is given on
  a log10 scale.  Negative values mean that the alternative would actually
  do worse than the current choice.
  In the example above, 5 comes from the fact that the overhead of
  inserting at the beginning of a vector vs. a list is around 1024 * 1024 / 2,
  which is around 10e5.  The improvement from setting the initial size to
  1024 is in the range of 10e3, since the overhead of dynamic resizing is
  linear in this case.
  
  
  
  
  Call stack.  Currently, the addresses are printed without
  symbol name or code location attribution.
  Users are expected to postprocess the output using, for instance, addr2line.
  
  
  
  
  The warning message.  For some warnings, this is static text, e.g.,
  "change vector to list".  For other warnings, such as the one above,
  the message contains numeric advice, e.g., the suggested initial size
  of the vector.
  
  
  
  
 
  Three files are generated.  libstdcxx-profile.txt
   contains human readable advice.  libstdcxx-profile.raw
   contains implementation specific data about each diagnostic.
   Their format is not documented.  They are sufficient to generate
   all the advice given in libstdcxx-profile.txt.  The advantage
   of keeping this raw format is that traces from multiple executions can
   be aggregated simply by concatenating the raw traces.  We intend to
   offer an external utility program that can issue advice from a trace.
   libstdcxx-profile.conf.out lists the actual diagnostic
   parameters used.  To alter parameters, edit this file and rename it to
   libstdcxx-profile.conf.
  
 
  Advice is given regardless whether the transformation is valid.
  For instance, we advise changing a map to an unordered_map even if the
  application semantics require that data be ordered.
  We believe such warnings can help users understand the performance
  behavior of their application better, which can lead to changes
  at a higher abstraction level.
  
 

 

  Tuning the Profile Mode
 
  Compile time switches and environment variables (see also file
   profiler.h).  Unless specified otherwise, they can be set at compile time
   using -D_<name> or by setting variable <name>
   in the environment where the program is run, before starting execution.
  
  
   _GLIBCXX_PROFILE_NO_<diagnostic>:
   disable specific diagnostics.
   See section Diagnostics for possible values.
   (Environment variables not supported.)
   
  
   _GLIBCXX_PROFILE_TRACE_PATH_ROOT: set an alternative root
   path for the output files.
   
  _GLIBCXX_PROFILE_MAX_WARN_COUNT: set it to the maximum
   number of warnings desired.  The default value is 10.
  
   _GLIBCXX_PROFILE_MAX_STACK_DEPTH: if set to 0,
   the advice will
   be collected and reported for the program as a whole, and not for each
   call context.
   This could also be used in continuous regression tests, where you
   just need to know whether there is a regression or not.
   The default value is 32.
   
  
   _GLIBCXX_PROFILE_MEM_PER_DIAGNOSTIC:
   set a limit on how much memory to use for the accounting tables for each
   diagnostic type.  When this limit is reached, new events are ignored
   until the memory usage decreases under the limit.  Generally, this means
   that newly created containers will not be instrumented until some
   live containers are deleted.  The default is 128 MB.
   
  
   _GLIBCXX_PROFILE_NO_THREADS:
   Make the library not use threads.  If thread local storage (TLS) is not
   available, you will get a preprocessor error asking you to set
   -D_GLIBCXX_PROFILE_NO_THREADS if your program is single-threaded.
   Multithreaded execution without TLS is not supported.
   (Environment variable not supported.)
   
  
   _GLIBCXX_HAVE_EXECINFO_H:
   This name should be defined automatically at library configuration time.
   If your library was configured without execinfo.h, but
   you have it in your include path, you can define it explicitly.  Without
   it, advice is collected for the program as a whole, and not for each
   call context.
   (Environment variable not supported.)
   
  
  
 

 

 
 

  Design
 



 


 

 xreflabel="Wrapper">
Wrapper Model
  
  In order to get our instrumented library version included instead of the
  release one,
  we use the same wrapper model as the debug mode.
  We subclass entities from the release version.  Wherever
  _GLIBCXX_PROFILE is defined, the release namespace is
  std::__norm, whereas the profile namespace is
  std::__profile.  Using plain std translates
  into std::__profile.
  
  
  Whenever possible, we try to wrap at the public interface level, e.g.,
  in unordered_set rather than in hashtable,
  in order not to depend on implementation.
  
  
  Mixing object files built with and without the profile mode must
  not affect the program execution.  However, there are no guarantees to
  the accuracy of diagnostics when using even a single object not built with
  -D_GLIBCXX_PROFILE.
  Currently, mixing the profile mode with debug and parallel extensions is
  not allowed.  Mixing them at compile time will result in preprocessor errors.
  Mixing them at link time is undefined.
  

 
 

 xreflabel="Instrumentation">
Instrumentation
  
  Instead of instrumenting every public entry and exit point,
  we chose to add instrumentation on demand, as needed
  by individual diagnostics.
  The main reason is that some diagnostics require us to extract bits of
  internal state that are particular only to that diagnostic.
  We plan to formalize this later, after we learn more about the requirements
  of several diagnostics.
  
  
  All the instrumentation points can be switched on and off using
  -D[_NO]_GLIBCXX_PROFILE_<diagnostic> options.
  With all the instrumentation calls off, there should be negligible
  overhead over the release version.  This property is needed to support
  diagnostics based on timing of internal operations.  For such diagnostics,
  we anticipate turning most of the instrumentation off in order to prevent
  profiling overhead from polluting time measurements, and thus diagnostics.
  
  
  All the instrumentation on/off compile time switches live in
  include/profile/profiler.h.
  

 
 

 xreflabel="Run Time Behavior">
Run Time Behavior
  
  For practical reasons, the instrumentation library processes the trace
  partially
  rather than dumping it to disk in raw form.  Each event is processed when
  it occurs.  It is usually attached a cost and it is aggregated into
  the database of a specific diagnostic class.  The cost model
  is based largely on the standard performance guarantees, but in some
  cases we use knowledge about GCC's standard library implementation.
  
  
  Information is indexed by (1) call stack and (2) instance id or address
  to be able to understand and summarize precise creation-use-destruction
  dynamic chains.  Although the analysis is sensitive to dynamic instances,
  the reports are only sensitive to call context.  Whenever a dynamic instance
  is destroyed, we accumulate its effect to the corresponding entry for the
  call stack of its constructor location.
  
 
  
  For details, see
   paper presented at
   CGO 2009.
  

 
 

 xreflabel="Analysis and Diagnostics">
Analysis and Diagnostics
  
  Final analysis takes place offline, and it is based entirely on the
  generated trace and debugging info in the application binary.
  See section Diagnostics for a list of analysis types that we plan to support.
  
  
  The input to the analysis is a table indexed by profile type and call stack.
  The data type for each entry depends on the profile type.
  

 
 

 xreflabel="Cost Model">
Cost Model
  
  While it is likely that cost models become complex as we get into
  more sophisticated analysis, we will try to follow a simple set of rules
  at the beginning.
  

  Relative benefit estimation:
  The idea is to estimate or measure the cost of all operations
  in the original scenario versus the scenario we advise to switch to.
  For instance, when advising to change a vector to a list, an occurrence
  of the insert method will generally count as a benefit.
  Its magnitude depends on (1) the number of elements that get shifted
  and (2) whether it triggers a reallocation.
  
  Synthetic measurements:
  We will measure the relative difference between similar operations on
  different containers.  We plan to write a battery of small tests that
  compare the times of the executions of similar methods on different
  containers.  The idea is to run these tests on the target machine.
  If this training phase is very quick, we may decide to perform it at
  library initialization time.  The results can be cached on disk and reused
  across runs.
  
  Timers:
  We plan to use timers for operations of larger granularity, such as sort.
  For instance, we can switch between different sort methods on the fly
  and report the one that performs best for each call context.
  
  Show stoppers:
  We may decide that the presence of an operation nullifies the advice.
  For instance, when considering switching from set to
  unordered_set, if we detect use of operator ++,
  we will simply not issue the advice, since this could signal that the use
  care require a sorted container.

 

 
 

 xreflabel="Reports">
Reports
  
There are two types of reports.  First, if we recognize a pattern for which
we have a substitute that is likely to give better performance, we print
the advice and estimated performance gain.  The advice is usually associated
to a code position and possibly a call stack.
  
  
Second, we report performance characteristics for which we do not have
a clear solution for improvement.  For instance, we can point to the user
the top 10 multimap locations
which have the worst data locality in actual traversals.
Although this does not offer a solution,
it helps the user focus on the key problems and ignore the uninteresting ones.
  

 
 

 xreflabel="Testing">
Testing
  
  First, we want to make sure we preserve the behavior of the release mode.
  You can just type "make check-profile", which
  builds and runs the whole test suite in profile mode.
  
  
  Second, we want to test the correctness of each diagnostic.
  We created a profile directory in the test suite.
  Each diagnostic must come with at least two tests, one for false positives
  and one for false negatives.
  

 

 

 xreflabel="API">
Extensions for Custom Containers
 
  
  Many large projects use their own data structures instead of the ones in the
  standard library.  If these data structures are similar in functionality
  to the standard library, they can be instrumented with the same hooks
  that are used to instrument the standard library.
  The instrumentation API is exposed in file
  profiler.h (look for "Instrumentation hooks").
  
 

 
 

 xreflabel="Cost Model">
Empirical Cost Model
 
  
  Currently, the cost model uses formulas with predefined relative weights
  for alternative containers or container implementations.  For instance,
  iterating through a vector is X times faster than iterating through a list.
  
  
  (Under development.)
  We are working on customizing this to a particular machine by providing
  an automated way to compute the actual relative weights for operations
  on the given machine.
  
  
  (Under development.)
  We plan to provide a performance parameter database format that can be
  filled in either by hand or by an automated training mechanism.
  The analysis module will then use this database instead of the built in.
  generic parameters.
  
 

 
 

 xreflabel="Implementation">
Implementation Issues
 
 

 xreflabel="Stack Traces">
Stack Traces
  
  Accurate stack traces are needed during profiling since we group events by
  call context and dynamic instance.  Without accurate traces, diagnostics
  may be hard to interpret.  For instance, when giving advice to the user
  it is imperative to reference application code, not library code.
  
  
  Currently we are using the libc backtrace routine to get
  stack traces.
  _GLIBCXX_PROFILE_STACK_DEPTH can be set
  to 0 if you are willing to give up call context information, or to a small
  positive value to reduce run time overhead.
  

 
 

 xreflabel="Symbolization">
Symbolization of Instruction Addresses
  
  The profiling and analysis phases use only instruction addresses.
  An external utility such as addr2line is needed to postprocess the result.
  We do not plan to add symbolization support in the profile extension.
  This would require access to symbol tables, debug information tables,
  external programs or libraries and other system dependent information.
  

 
 

 xreflabel="Concurrency">
Concurrency
  
  Our current model is simplistic, but precise.
  We cannot afford to approximate because some of our diagnostics require
  precise matching of operations to container instance and call context.
  During profiling, we keep a single information table per diagnostic.
  There is a single lock per information table.
  

 
 

 xreflabel="Using the Standard Library in the Runtime Library">
Using the Standard Library in the Instrumentation Implementation
  
  As much as we would like to avoid uses of libstdc++ within our
  instrumentation library, containers such as unordered_map are very
  appealing.  We plan to use them as long as they are named properly
  to avoid ambiguity.
  

 
 

 xreflabel="Malloc Hooks">
Malloc Hooks
  
  User applications/libraries can provide malloc hooks.
  When the implementation of the malloc hooks uses stdlibc++, there can
  be an infinite cycle between the profile mode instrumentation and the
  malloc hook code.
  
  
  We protect against reentrance to the profile mode instrumentation code,
  which should avoid this problem in most cases.
  The protection mechanism is thread safe and exception safe.
  This mechanism does not prevent reentrance to the malloc hook itself,
  which could still result in deadlock, if, for instance, the malloc hook
  uses non-recursive locks.
  XXX: A definitive solution to this problem would be for the profile extension
  to use a custom allocator internally, and perhaps not to use libstdc++.
  

 
 

 xreflabel="Construction and Destruction of Global Objects">
Construction and Destruction of Global Objects
  
  The profiling library state is initialized at the first call to a profiling
  method.  This allows us to record the construction of all global objects.
  However, we cannot do the same at destruction time.  The trace is written
  by a function registered by atexit, thus invoked by
  exit.
  

 

 
 

 xreflabel="Developer Information">
Developer Information
 

 xreflabel="Big Picture">
Big Picture
 
  The profile mode headers are included with
   -D_GLIBCXX_PROFILE through preprocessor directives in
   include/std/*.
  
 
  Instrumented implementations are provided in
   include/profile/*.  All instrumentation hooks are macros
   defined in include/profile/profiler.h.
  
 
  All the implementation of the instrumentation hooks is in
   include/profile/impl/*.  Although all the code gets included,
   thus is publicly visible, only a small number of functions are called from
   outside this directory.  All calls to hook implementations must be
   done through macros defined in profiler.h.  The macro
   must ensure (1) that the call is guarded against reentrance and
   (2) that the call can be turned off at compile time using a
   -D_GLIBCXX_PROFILE_... compiler option.
  
 

 

 xreflabel="How To Add A Diagnostic">
How To Add A Diagnostic
 
  Let's say the diagnostic name is "magic".
  
 
  If you need to instrument a header not already under
   include/profile/*, first edit the corresponding header
   under include/std/ and add a preprocessor directive such
   as the one in include/std/vector:

#ifdef _GLIBCXX_PROFILE
# include <profile/vector>
#endif

  
 
  If the file you need to instrument is not yet under
   include/profile/, make a copy of the one in
   include/debug, or the main implementation.
   You'll need to include the main implementation and inherit the classes
   you want to instrument.  Then define the methods you want to instrument,
   define the instrumentation hooks and add calls to them.
   Look at include/profile/vector for an example.
  
 
  Add macros for the instrumentation hooks in
   include/profile/impl/profiler.h.
   Hook names must start with __profcxx_.
   Make sure they transform
   in no code with -D_NO_GLBICXX_PROFILE_MAGIC.
   Make sure all calls to any method in namespace __gnu_profile
   is protected against reentrance using macro
   _GLIBCXX_PROFILE_REENTRANCE_GUARD.
   All names of methods in namespace __gnu_profile called from
   profiler.h must start with __trace_magic_.
  
 
  Add the implementation of the diagnostic.
   
     
      Create new file include/profile/impl/profiler_magic.h.
     
     
      Define class __magic_info: public __object_info_base.
      This is the representation of a line in the object table.
      The __merge method is used to aggregate information
      across all dynamic instances created at the same call context.
      The __magnitude must return the estimation of the benefit
      as a number of small operations, e.g., number of words copied.
      The __write method is used to produce the raw trace.
      The __advice method is used to produce the advice string.
     
     
      Define class __magic_stack_info: public __magic_info.
      This defines the content of a line in the stack table.
     
     
      Define class __trace_magic: public __trace_base<__magic_info,
      __magic_stack_info>.
      It defines the content of the trace associated with this diagnostic.
     
    
  
 
  Add initialization and reporting calls in
   include/profile/impl/profiler_trace.h.  Use
   __trace_vector_to_list as an example.
  
 
  Add documentation in file doc/xml/manual/profile_mode.xml.
  


 

Diagnostics
 
  
  The table below presents all the diagnostics we intend to implement.
  Each diagnostic has a corresponding compile time switch
  -D_GLIBCXX_PROFILE_<diagnostic>.
  Groups of related diagnostics can be turned on with a single switch.
  For instance, -D_GLIBCXX_PROFILE_LOCALITY is equivalent to
  -D_GLIBCXX_PROFILE_SOFTWARE_PREFETCH
  -D_GLIBCXX_PROFILE_RBTREE_LOCALITY.
  
 
  
  The benefit, cost, expected frequency and accuracy of each diagnostic
  was given a grade from 1 to 10, where 10 is highest.
  A high benefit means that, if the diagnostic is accurate, the expected
  performance improvement is high.
  A high cost means that turning this diagnostic on leads to high slowdown.
  A high frequency means that we expect this to occur relatively often.
  A high accuracy means that the diagnostic is unlikely to be wrong.
  These grades are not perfect.  They are just meant to guide users with
  specific needs or time budgets.
  
 

 

 xreflabel="Template">
Diagnostic Template

  Switch:
  _GLIBCXX_PROFILE_<diagnostic>.
  
  Goal:  What problem will it diagnose?
  
  Fundamentals:.
  What is the fundamental reason why this is a problem
  Sample runtime reduction:
  Percentage reduction in execution time.  When reduction is more than
  a constant factor, describe the reduction rate formula.
  
  Recommendation:
  What would the advise look like?
  To instrument:
  What stdlibc++ components need to be instrumented?
  Analysis:
  How do we decide when to issue the advice?
  Cost model:
  How do we measure benefits?  Math goes here.
  Example:

program code
...
advice sample




 
 

 xreflabel="Containers">
Containers
 

Switch:
  _GLIBCXX_PROFILE_CONTAINERS.

 

 xreflabel="Hashtable Too Small">
Hashtable Too Small

  Switch:
  _GLIBCXX_PROFILE_HASHTABLE_TOO_SMALL.
  
  Goal: Detect hashtables with many
  rehash operations, small construction size and large destruction size.
  
  Fundamentals: Rehash is very expensive.
  Read content, follow chains within bucket, evaluate hash function, place at
  new location in different order.
  Sample runtime reduction: 36%.
  Code similar to example below.
  
  Recommendation:
  Set initial size to N at construction site S.
  
  To instrument:
  unordered_set, unordered_map constructor, destructor, rehash.
  
  Analysis:
  For each dynamic instance of unordered_[multi]set|map,
  record initial size and call context of the constructor.
  Record size increase, if any, after each relevant operation such as insert.
  Record the estimated rehash cost.
  Cost model:
  Number of individual rehash operations * cost per rehash.
  Example:

1 unordered_set<int> us;
2 for (int k = 0; k < 1000000; ++k) {
3   us.insert(k);
4 }
 
foo.cc:1: advice: Changing initial unordered_set size from 10 to 1000000 saves 1025530 rehash operations.




 
 

 xreflabel="Hashtable Too Large">
Hashtable Too Large

  Switch:
  _GLIBCXX_PROFILE_HASHTABLE_TOO_LARGE.
  
  Goal: Detect hashtables which are
  never filled up because fewer elements than reserved are ever
  inserted.
  
  Fundamentals: Save memory, which
  is good in itself and may also improve memory reference performance through
  fewer cache and TLB misses.
  Sample runtime reduction: unknown.
  
  Recommendation:
  Set initial size to N at construction site S.
  
  To instrument:
  unordered_set, unordered_map constructor, destructor, rehash.
  
  Analysis:
  For each dynamic instance of unordered_[multi]set|map,
  record initial size and call context of the constructor, and correlate it
  with its size at destruction time.
  
  Cost model:
  Number of iteration operations + memory saved.
  Example:

1 vector<unordered_set<int>> v(100000, unordered_set<int>(100)) ;
2 for (int k = 0; k < 100000; ++k) {
3   for (int j = 0; j < 10; ++j) {
4     v[k].insert(k + j);
5  }
6 }
 
foo.cc:1: advice: Changing initial unordered_set size from 100 to 10 saves N
bytes of memory and M iteration steps.




 

 xreflabel="Inefficient Hash">
Inefficient Hash

  Switch:
  _GLIBCXX_PROFILE_INEFFICIENT_HASH.
  
  Goal: Detect hashtables with polarized
  distribution.
  
  Fundamentals: A non-uniform
  distribution may lead to long chains, thus possibly increasing complexity
  by a factor up to the number of elements.
  
  Sample runtime reduction: factor up
   to container size.
  
  Recommendation: Change hash function
  for container built at site S.  Distribution score = N.  Access score = S.
  Longest chain = C, in bucket B.
  
  To instrument:
  unordered_set, unordered_map constructor, destructor, [],
  insert, iterator.
  
  Analysis:
  Count the exact number of link traversals.
  
  Cost model:
  Total number of links traversed.
  Example:

class dumb_hash {
 public:
  size_t operator() (int i) const { return 0; }
};
...
  unordered_set<int, dumb_hash> hs;
  ...
  for (int i = 0; i < COUNT; ++i) {
    hs.find(i);
  }




 

 xreflabel="Vector Too Small">
Vector Too Small

  Switch:
  _GLIBCXX_PROFILE_VECTOR_TOO_SMALL.
  
  Goal:Detect vectors with many
  resize operations, small construction size and large destruction size..
  
  Fundamentals:Resizing can be expensive.
  Copying large amounts of data takes time.  Resizing many small vectors may
  have allocation overhead and affect locality.
  Sample runtime reduction:%.
  
  Recommendation:
  Set initial size to N at construction site S.
  To instrument:vector.
  
  Analysis:
  For each dynamic instance of vector,
  record initial size and call context of the constructor.
  Record size increase, if any, after each relevant operation such as
  push_back.  Record the estimated resize cost.
  
  Cost model:
  Total number of words copied * time to copy a word.
  Example:

1 vector<int> v;
2 for (int k = 0; k < 1000000; ++k) {
3   v.push_back(k);
4 }
 
foo.cc:1: advice: Changing initial vector size from 10 to 1000000 saves
copying 4000000 bytes and 20 memory allocations and deallocations.




 

 xreflabel="Vector Too Large">
Vector Too Large

  Switch:
  _GLIBCXX_PROFILE_VECTOR_TOO_LARGE
  
  Goal:Detect vectors which are
  never filled up because fewer elements than reserved are ever
  inserted.
  
  Fundamentals:Save memory, which
  is good in itself and may also improve memory reference performance through
  fewer cache and TLB misses.
  Sample runtime reduction:%.
  
  Recommendation:
  Set initial size to N at construction site S.
  To instrument:vector.
  
  Analysis:
  For each dynamic instance of vector,
  record initial size and call context of the constructor, and correlate it
  with its size at destruction time.
  Cost model:
  Total amount of memory saved.
  Example:

1 vector<vector<int>> v(100000, vector<int>(100)) ;
2 for (int k = 0; k < 100000; ++k) {
3   for (int j = 0; j < 10; ++j) {
4     v[k].insert(k + j);
5  }
6 }
 
foo.cc:1: advice: Changing initial vector size from 100 to 10 saves N
bytes of memory and may reduce the number of cache and TLB misses.




 

 xreflabel="Vector to Hashtable">
Vector to Hashtable

  Switch:
  _GLIBCXX_PROFILE_VECTOR_TO_HASHTABLE.
  
  Goal: Detect uses of
  vector that can be substituted with unordered_set
  to reduce execution time.
  
  Fundamentals:
  Linear search in a vector is very expensive, whereas searching in a hashtable
  is very quick.
  Sample runtime reduction:factor up
   to container size.
  
  Recommendation:Replace
  vector with unordered_set at site S.
  
  To instrument:vector
  operations and access methods.
  Analysis:
  For each dynamic instance of vector,
  record call context of the constructor.  Issue the advice only if the
  only methods called on this vector are push_back,
  insert and find.
  
  Cost model:
  Cost(vector::push_back) + cost(vector::insert) + cost(find, vector) -
  cost(unordered_set::insert) + cost(unordered_set::find).
  
  Example:

1  vector<int> v;
...
2  for (int i = 0; i < 1000; ++i) {
3    find(v.begin(), v.end(), i);
4  }
 
foo.cc:1: advice: Changing "vector" to "unordered_set" will save about 500,000
comparisons.




 

 xreflabel="Hashtable to Vector">
Hashtable to Vector

  Switch:
  _GLIBCXX_PROFILE_HASHTABLE_TO_VECTOR.
  
  Goal: Detect uses of
  unordered_set that can be substituted with vector
  to reduce execution time.
  
  Fundamentals:
  Hashtable iterator is slower than vector iterator.
  Sample runtime reduction:95%.
  
  Recommendation:Replace
  unordered_set with vector at site S.
  
  To instrument:unordered_set
  operations and access methods.
  Analysis:
  For each dynamic instance of unordered_set,
  record call context of the constructor.  Issue the advice only if the
  number of find, insert and []
  operations on this unordered_set are small relative to the
  number of elements, and methods begin or end
  are invoked (suggesting iteration).
  Cost model:
  Number of .
  Example:

1  unordered_set<int> us;
...
2  int s = 0;
3  for (unordered_set<int>::iterator it = us.begin(); it != us.end(); ++it) {
4    s += *it;
5  }
 
foo.cc:1: advice: Changing "unordered_set" to "vector" will save about N
indirections and may achieve better data locality.




 

 xreflabel="Vector to List">
Vector to List

  Switch:
  _GLIBCXX_PROFILE_VECTOR_TO_LIST.
  
  Goal: Detect cases where
  vector could be substituted with list for
  better performance.
  
  Fundamentals:
  Inserting in the middle of a vector is expensive compared to inserting in a
  list.
  
  Sample runtime reduction:factor up to
   container size.
  
  Recommendation:Replace vector with list
  at site S.
  To instrument:vector
  operations and access methods.
  Analysis:
  For each dynamic instance of vector,
  record the call context of the constructor.  Record the overhead of each
  insert operation based on current size and insert position.
  Report instance with high insertion overhead.
  
  Cost model:
  (Sum(cost(vector::method)) - Sum(cost(list::method)), for
  method in [push_back, insert, erase])
  + (Cost(iterate vector) - Cost(iterate list))
  Example:

1  vector<int> v;
2  for (int i = 0; i < 10000; ++i) {
3    v.insert(v.begin(), i);
4  }
 
foo.cc:1: advice: Changing "vector" to "list" will save about 5,000,000
operations.




 

 xreflabel="List to Vector">
List to Vector

  Switch:
  _GLIBCXX_PROFILE_LIST_TO_VECTOR.
  
  Goal: Detect cases where
  list could be substituted with vector for
  better performance.
  
  Fundamentals:
  Iterating through a vector is faster than through a list.
  
  Sample runtime reduction:64%.
  
  Recommendation:Replace list with vector
  at site S.
  To instrument:vector
  operations and access methods.
  Analysis:
  Issue the advice if there are no insert operations.
  
  Cost model:
    (Sum(cost(vector::method)) - Sum(cost(list::method)), for
  method in [push_back, insert, erase])
  + (Cost(iterate vector) - Cost(iterate list))
  Example:

1  list<int> l;
...
2  int sum = 0;
3  for (list<int>::iterator it = l.begin(); it != l.end(); ++it) {
4    sum += *it;
5  }
 
foo.cc:1: advice: Changing "list" to "vector" will save about 1000000 indirect
memory references.




 

 xreflabel="List to Forward List">
List to Forward List (Slist)

  Switch:
  _GLIBCXX_PROFILE_LIST_TO_SLIST.
  
  Goal: Detect cases where
  list could be substituted with forward_list for
  better performance.
  
  Fundamentals:
  The memory footprint of a forward_list is smaller than that of a list.
  This has beneficial effects on memory subsystem, e.g., fewer cache misses.
  
  Sample runtime reduction:40%.
  Note that the reduction is only noticeable if the size of the forward_list
  node is in fact larger than that of the list node.  For memory allocators
  with size classes, you will only notice an effect when the two node sizes
  belong to different allocator size classes.
  
  Recommendation:Replace list with
  forward_list at site S.
  To instrument:list
  operations and iteration methods.
  Analysis:
  Issue the advice if there are no backwards traversals
  or insertion before a given node.
  
  Cost model:
  Always true.
  Example:

1  list<int> l;
...
2  int sum = 0;
3  for (list<int>::iterator it = l.begin(); it != l.end(); ++it) {
4    sum += *it;
5  }
 
foo.cc:1: advice: Change "list" to "forward_list".




 

 xreflabel="Ordered to Unordered Associative Container">
Ordered to Unordered Associative Container

  Switch:
  _GLIBCXX_PROFILE_ORDERED_TO_UNORDERED.
  
  Goal:  Detect cases where ordered
  associative containers can be replaced with unordered ones.
  
  Fundamentals:
  Insert and search are quicker in a hashtable than in
  a red-black tree.
  Sample runtime reduction:52%.
  
  Recommendation:
  Replace set with unordered_set at site S.
  To instrument:
  set, multiset, map,
  multimap methods.
  Analysis:
  Issue the advice only if we are not using operator ++ on any
  iterator on a particular [multi]set|map.
  
  Cost model:
  (Sum(cost(hashtable::method)) - Sum(cost(rbtree::method)), for
  method in [insert, erase, find])
  + (Cost(iterate hashtable) - Cost(iterate rbtree))
  Example:

1  set<int> s;
2  for (int i = 0; i < 100000; ++i) {
3    s.insert(i);
4  }
5  int sum = 0;
6  for (int i = 0; i < 100000; ++i) {
7    sum += *s.find(i);
8  }




 

 
 
 

 xreflabel="Algorithms">
Algorithms
 
  Switch:
  _GLIBCXX_PROFILE_ALGORITHMS.
  
 

 xreflabel="Sorting">
Sort Algorithm Performance

  Switch:
  _GLIBCXX_PROFILE_SORT.
  
  Goal: Give measure of sort algorithm
  performance based on actual input.  For instance, advise Radix Sort over
  Quick Sort for a particular call context.
  
  Fundamentals:
  See papers:
  
  A framework for adaptive algorithm selection in STAPL and
  
  Optimizing Sorting with Machine Learning Algorithms.
  
  Sample runtime reduction:60%.
  
  Recommendation: Change sort algorithm
  at site S from X Sort to Y Sort.
  To instrument: sort
  algorithm.
  Analysis:
  Issue the advice if the cost model tells us that another sort algorithm
  would do better on this input.  Requires us to know what algorithm we
  are using in our sort implementation in release mode.
  Cost model:
  Runtime(algo) for algo in [radix, quick, merge, ...]
  Example:





 

 
 

 xreflabel="Data Locality">
Data Locality
 
  Switch:
  _GLIBCXX_PROFILE_LOCALITY.
  
 

 xreflabel="Need Software Prefetch">
Need Software Prefetch

  Switch:
  _GLIBCXX_PROFILE_SOFTWARE_PREFETCH.
  
  Goal: Discover sequences of indirect
  memory accesses that are not regular, thus cannot be predicted by
  hardware prefetchers.
  
  Fundamentals:
  Indirect references are hard to predict and are very expensive when they
  miss in caches.
  Sample runtime reduction:25%.
  
  Recommendation: Insert prefetch
  instruction.
  To instrument: Vector iterator and
  access operator [].
  
  Analysis:
  First, get cache line size and page size from system.
  Then record iterator dereference sequences for which the value is a pointer.
  For each sequence within a container, issue a warning if successive pointer
  addresses are not within cache lines and do not form a linear pattern
  (otherwise they may be prefetched by hardware).
  If they also step across page boundaries, make the warning stronger.
  
  The same analysis applies to containers other than vector.
  However, we cannot give the same advice for linked structures, such as list,
  as there is no random access to the n-th element.  The user may still be
  able to benefit from this information, for instance by employing frays (user
  level light weight threads) to hide the latency of chasing pointers.
  
  
  This analysis is a little oversimplified.  A better cost model could be
  created by understanding the capability of the hardware prefetcher.
  This model could be trained automatically by running a set of synthetic
  cases.
  
  
  Cost model:
  Total distance between pointer values of successive elements in vectors
  of pointers.
  Example:

1 int zero = 0;
2 vector<int*> v(10000000, &zero);
3 for (int k = 0; k < 10000000; ++k) {
4   v[random() % 10000000] = new int(k);
5 }
6 for (int j = 0; j < 10000000; ++j) {
7   count += (*v[j] == 0 ? 0 : 1);
8 }
 
foo.cc:7: advice: Insert prefetch instruction.




 

 xreflabel="Linked Structure Locality">
Linked Structure Locality

  Switch:
  _GLIBCXX_PROFILE_RBTREE_LOCALITY.
  
  Goal: Give measure of locality of
  objects stored in linked structures (lists, red-black trees and hashtables)
  with respect to their actual traversal patterns.
  
  Fundamentals:Allocation can be tuned
  to a specific traversal pattern, to result in better data locality.
  See paper:
  
  Custom Memory Allocation for Free.
  
  Sample runtime reduction:30%.
  
  Recommendation:
  High scatter score N for container built at site S.
  Consider changing allocation sequence or choosing a structure conscious
  allocator.
  To instrument: Methods of all
  containers using linked structures.
  Analysis:
  First, get cache line size and page size from system.
  Then record the number of successive elements that are on different line
  or page, for each traversal method such as find.  Give advice
  only if the ratio between this number and the number of total node hops
  is above a threshold.
  Cost model:
  Sum(same_cache_line(this,previous))
  Example:

 1  set<int> s;
 2  for (int i = 0; i < 10000000; ++i) {
 3    s.insert(i);
 4  }
 5  set<int> s1, s2;
 6  for (int i = 0; i < 10000000; ++i) {
 7    s1.insert(i);
 8    s2.insert(i);
 9  }
...
      // Fast, better locality.
10    for (set<int>::iterator it = s.begin(); it != s.end(); ++it) {
11      sum += *it;
12    }
      // Slow, elements are further apart.
13    for (set<int>::iterator it = s1.begin(); it != s1.end(); ++it) {
14      sum += *it;
15    }
 
foo.cc:5: advice: High scatter score NNN for set built here.  Consider changing
the allocation sequence or switching to a structure conscious allocator.




 

 
 

 xreflabel="Multithreaded Data Access">
Multithreaded Data Access
 
  
  The diagnostics in this group are not meant to be implemented short term.
  They require compiler support to know when container elements are written
  to.  Instrumentation can only tell us when elements are referenced.
  
 
  Switch:
  _GLIBCXX_PROFILE_MULTITHREADED.
  
 

 xreflabel="Dependence Violations at Container Level">
Data Dependence Violations at Container Level

  Switch:
  _GLIBCXX_PROFILE_DDTEST.
  
  Goal: Detect container elements
  that are referenced from multiple threads in the parallel region or
  across parallel regions.
  
  Fundamentals:
  Sharing data between threads requires communication and perhaps locking,
  which may be expensive.
  
  Sample runtime reduction:?%.
  
  Recommendation: Change data
  distribution or parallel algorithm.
  To instrument: Container access methods
  and iterators.
  
  Analysis:
  Keep a shadow for each container.  Record iterator dereferences and
  container member accesses.  Issue advice for elements referenced by
  multiple threads.
  See paper: 
  The LRPD test: speculative run-time parallelization of loops with
  privatization and reduction parallelization.
  
  Cost model:
  Number of accesses to elements referenced from multiple threads
  
  Example:





 

 xreflabel="False Sharing">
False Sharing

  Switch:
  _GLIBCXX_PROFILE_FALSE_SHARING.
  
  Goal: Detect elements in the
  same container which share a cache line, are written by at least one
  thread, and accessed by different threads.
  
  Fundamentals: Under these assumptions,
  cache protocols require
  communication to invalidate lines, which may be expensive.
  
  Sample runtime reduction:68%.
  
  Recommendation: Reorganize container
  or use padding to avoid false sharing.
  To instrument: Container access methods
  and iterators.
  
  Analysis:
  First, get the cache line size.
  For each shared container, record all the associated iterator dereferences
  and member access methods with the thread id.  Compare the address lists
  across threads to detect references in two different threads to the same
  cache line.  Issue a warning only if the ratio to total references is
  significant.  Do the same for iterator dereference values if they are
  pointers.
  Cost model:
  Number of accesses to same cache line from different threads.
  
  Example:

1     vector<int> v(2, 0);
2 #pragma omp parallel for shared(v, SIZE) schedule(static, 1)
3     for (i = 0; i < SIZE; ++i) {
4       v[i % 2] += i;
5     }
 
OMP_NUM_THREADS=2 ./a.out
foo.cc:1: advice: Change container structure or padding to avoid false
sharing in multithreaded access at foo.cc:4.  Detected N shared cache lines.




 

 
 

 xreflabel="Statistics">
Statistics
 

Switch:
  _GLIBCXX_PROFILE_STATISTICS.

 

  In some cases the cost model may not tell us anything because the costs
  appear to offset the benefits.  Consider the choice between a vector and
  a list.  When there are both inserts and iteration, an automatic advice
  may not be issued.  However, the programmer may still be able to make use
  of this information in a different way.


  This diagnostic will not issue any advice, but it will print statistics for
  each container construction site.  The statistics will contain the cost
  of each operation actually performed on the container.

 

 
 

 
 

Bibliography
 
  
    </code></pre></td>
      </tr>
      <tr valign="middle">
         <td>1740</td>
         <td></td>
         <td></td>
         <td class="code"><pre><code>      Perflint: A Context Sensitive Performance Advisor for C++ Programs</code></pre></td>
      </tr>
      <tr valign="middle">
         <td>1741</td>
         <td></td>
         <td></td>
         <td class="code"><pre><code>    
 
    
      Lixia
      Liu
    
    
      Silvius
      Rus
    
 
    
      2009
      
    
 
    
      
        Proceedings of the 2009 International Symposium on Code Generation
        and Optimization
      
    
  

 
 

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