This is something I wanted to clean-up since a very long time indeed, but always postponed it, since it requires a lot of nasty clean-up and disentangling.... Several parts had been stashed away into `src/lib/test`, which are not really related: * the testrunner and testsuite builder * generic helper utilities * specialised test-support facilities, e.g. the EventLog This changeset introduces a new top-level test folder and a namespace `::test` Code from this folder is linked into a shared library `libtestsupport.so`, which resides in the linkage hierarchy between »Vessel« and »Vault«, yet is no longer part of the Lumiera application and only linked into the test-suite.
199 lines
8 KiB
C++
199 lines
8 KiB
C++
/*
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MICROBENCHMARK.hpp - multithreaded timing measurement
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Copyright (C)
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2018, Hermann Vosseler <Ichthyostega@web.de>
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**Lumiera** is free software; you can redistribute it and/or modify it
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under the terms of the GNU General Public License as published by the
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Free Software Foundation; either version 2 of the License, or (at your
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option) any later version. See the file COPYING for further details.
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*/
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/** @file microbenchmark.hpp
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** Functions to perform (multithreaded) timing measurement on a given functor.
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** This helper simplifies micro benchmarks of isolated implementation details.
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** The test subject, given as function object or lambda, is invoked numerous times
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** within a tight loop. In the [multithreaded variant](\ref threadBenchmark()),
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** the lambda is copied into N threads and performed in each thread in parallel;
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** after waiting on termination of the test threads, results are summed up and then
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** averaged into milliseconds per single invocation. The actual timing measurement
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** relies on `chrono::duration`, which means to count micro ticks of the OS.
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** @warning care has to bee taken when optimisation is involved!
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** Optimisation usually has quite some impact on the results, but since
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** this function is inline, the lambda can typically be inlined and the
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** loop possibly be optimised away altogether. A simple workaround is
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** to define a _volatile_ variable in the call context, close the lambda
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** by reference, and perform a comparison with that volatile variable
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** in each invocation. The compiler is required actually to access the
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** value of the volatile each time.
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** @remarks some interesting observations (in my setup, 8 core AMD FX-8350)
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** - if we replace the global volatile by a local variable within the
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** test subject, the initialisation of that local typically costs +5ns
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** per invocation.
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** - incrementing the volatile costs +10ns
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** - multithreaded (unlocked) incrementing of the _global_ volatile
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** creates massive overhead and increases the running time by factor 100.
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** This nicely confirms that the x86_64 platform has strong cache coherence.
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**
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*/
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#ifndef TEST_MICROBENCHMARK_H
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#define TEST_MICROBENCHMARK_H
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#include "lib/meta/function.hpp"
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#include "lib/scoped-collection.hpp"
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#include "lib/sync-barrier.hpp"
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#include "lib/thread.hpp"
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#include "test/microbenchmark-adaptor.hpp"
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#include <chrono>
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namespace test{
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namespace {
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constexpr size_t DEFAULT_RUNS = 10'000'000;
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using CLOCK_SCALE = std::micro; // Results are in µ-sec
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}
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/**
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* Helper to invoke a functor or λ to observe its running time.
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* @param invokeTestLoop the test (complete including loop) invoked once
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* @param repeatCnt number of repetitions to divide the timing measurement
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* @return averaged time for one repetition, in microseconds
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*/
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template<class FUN>
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inline double
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benchmarkTime (FUN const& invokeTestCode, const size_t repeatCnt =1)
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{
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using std::chrono::steady_clock;
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using Dur = std::chrono::duration<double, CLOCK_SCALE>;
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auto start = steady_clock::now();
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invokeTestCode();
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Dur duration = steady_clock::now () - start;
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return duration.count() / repeatCnt;
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};
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/**
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* Benchmark building block to invoke a functor or λ in a tight loop,
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* passing the current loop index and capturing a result checksum value.
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* @return sum of all individual invocation results as checksum
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*/
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template<class FUN>
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inline size_t
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benchmarkLoop (FUN const& testSubject, const size_t repeatCnt = DEFAULT_RUNS)
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{
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// the test subject gets the current loop-index and returns a checksum value
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auto subject4benchmark = microbenchmark::adapted4benchmark (testSubject);
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size_t checksum{0};
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for (size_t i=0; i<repeatCnt; ++i)
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checksum += subject4benchmark(i);
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return checksum;
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}
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/** perform a simple looped microbenchmark.
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* @param testSubject the operation to test as functor or λ
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* @return a pair `(microseconds, checksum)`
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* @warning this setup is only usable under strong optimisation;
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* moreover, the scaffolding without actual operation should also
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* be tested for comparison, to get a feeling for the setup overhead.
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* For very small test subjects (single operations) it is recommended
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* to use a direct loop without any lambdas and building blocks.
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*/
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template<class FUN>
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inline auto
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microBenchmark (FUN const& testSubject, const size_t repeatCnt = DEFAULT_RUNS)
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{
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volatile size_t checksum{0};
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auto invokeTestLoop = [&]{ checksum = benchmarkLoop (testSubject, repeatCnt); };
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double micros = benchmarkTime (invokeTestLoop, repeatCnt);
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return std::make_pair (micros, checksum);
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}
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/** perform a multithreaded microbenchmark.
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* This function fires up a number of threads
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* and invokes the given test subject repeatedly.
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* @tparam number of threads to run in parallel
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* @param subject function to be timed in parallel
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* @param repeatCnt loop-count _within each thread_
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* @return a pair `(microseconds, checksum)` combining the averaged
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* invocation time and a compounded checksum from all threads.
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* @remarks - the subject function will be _copied_ into each thread
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* - so `nThreads` copies of this function will run in parallel
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* - consider locking if this function accesses a shared closure.
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* - if you pass a lambda, it is eligible for inlining followed by
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* loop optimisation -- be sure to include an observable effect,
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* like returning a value tied to the actual computation, to
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* prevent the compiler from optimising it away altogether.
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*/
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template<size_t nThreads, class FUN>
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inline auto
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threadBenchmark(FUN const& subject, const size_t repeatCnt = DEFAULT_RUNS)
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{
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using lib::SyncBarrier;
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using std::chrono::steady_clock;
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using Dur = std::chrono::duration<double, CLOCK_SCALE>;
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// the test subject gets the current loop-index and returns a checksum value
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auto subject4benchmark = microbenchmark::adapted4benchmark (subject);
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using Subject = decltype(subject4benchmark);
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struct Thread
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: lib::ThreadJoinable<>
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{
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Thread(Subject const& subject, size_t loopCnt, SyncBarrier& testStart)
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: ThreadJoinable{"Micro-Benchmark"
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,[this,loopCnt, testSubject=subject, &testStart]
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() mutable // local (mutable) copy of the test-subject-Functor
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{
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testStart.sync(); // block until all threads are ready
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auto start = steady_clock::now();
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for (size_t i=0; i < loopCnt; ++i)
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checksum += testSubject(i);
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duration = steady_clock::now () - start;
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}}
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{ }
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// Note: barrier at begin and join at end both ensure data synchronisation
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Dur duration{}; // measured time within thread
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size_t checksum{0}; // collected checksum
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};
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SyncBarrier testStart{nThreads + 1}; // coordinated start of timing measurement
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lib::ScopedCollection<Thread> threads(nThreads);
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for (size_t n=0; n<nThreads; ++n) // create test threads
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threads.emplace (subject4benchmark, repeatCnt, testStart);
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testStart.sync(); // barrier until all threads are ready
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size_t checksum{0};
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Dur sumDuration{0.0};
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for (auto& thread : threads)
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{
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thread.join(); // block on measurement end (fence)
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sumDuration += thread.duration;
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checksum += thread.checksum;
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}
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double micros = sumDuration.count() / (nThreads * repeatCnt);
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return std::make_tuple (micros, checksum);
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}
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}// namespace test
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#endif /*TESTSUPPORT_MICROBENCHMARK_H*/
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