Table 19.1. Profile Code Location
| Code Location | Use |
|---|---|
libstdc++-v3/include/std/* | Preprocessor code to redirect to profile extension headers. |
libstdc++-v3/include/profile/* | Profile extension public headers (map, vector, ...). |
libstdc++-v3/include/profile/impl/* | Profile extension internals. Implementation files are
only included from impl/profiler.h, which is the only
file included from the public headers. |
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.
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.
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.
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.
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.
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.
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.
Table 19.1. Profile Code Location