Ichthyostega
a0336685dc
Further investigation shows that the ''data type used for computation'' plays a crucial role. The (recommended) 64bit mersenne twister uses the full value range of the working data type, which on a typical 64bit system is also `uint64_t`. In this case, values corrupted by concurrency go unnoticed. This can be **verified empirically** : the distribution of shifts from the theoretical mean value is in the expected low range < 2‰ However, when using the 32bit mersenne engine, the working data type is still uint64_t. In this case a **significant number of glitches** can be shown empricially. When drawing 1 Million values, in 80% of all runs at least one glitch and up to 5 glitches can happen, and the mean values are **significantly skewed**
126 lines
3.7 KiB
C++
126 lines
3.7 KiB
C++
/*
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RandomConcurrent(Test) - investigate concurrent random number generation
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Copyright (C) Lumiera.org
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2024, Hermann Vosseler <Ichthyostega@web.de>
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This program is free software; you can redistribute it and/or
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modify it under the terms of the GNU General Public License as
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published by the Free Software Foundation; either version 2 of
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the License, or (at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program; if not, write to the Free Software
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Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
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* *****************************************************/
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/** @file random-concurrent-test.cpp
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** unit test \ref RandomConcurrent_test
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*/
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#include "lib/test/run.hpp"
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#include "lib/sync-barrier.hpp"
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#include "lib/random.hpp"
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#include "lib/thread.hpp"
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#include "lib/sync.hpp"
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#include "lib/util.hpp"
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#include "lib/scoped-collection.hpp"
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#include "lib/test/microbenchmark.hpp"
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#include "lib/test/diagnostic-output.hpp"
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#include <deque>
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#include <tuple>
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//#include <array>
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//using util::isLimited;
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//using std::array;
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using std::tuple;
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using std::deque;
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namespace lib {
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namespace test {
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namespace {
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const uint NUM_THREADS = 8;
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const uint NUM_REPEATS = 10;
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const uint NUM_INVOKES = 1'000'000;
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}
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/******************************************************************//**
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* @test demonstrate simple access to random number generation,
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* as well as the setup of controlled random number sequences.
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* @see random.hpp
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*/
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class RandomConcurrent_test : public Test
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{
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virtual void
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run (Arg)
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{
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seedRand();
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investigate_concurrentAccess();
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}
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/** @test examine behaviour of PRNG under concurrency stress */
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void
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investigate_concurrentAccess()
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{
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struct Results
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: deque<tuple<double,uint>>
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, Sync<>
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{
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void
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post (double err, uint fails)
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{
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Lock sync(this);
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emplace_back (err, fails);
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}
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};
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Results results;
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using Engine = std::mt19937;
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// using Engine = std::mt19937_64;
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Engine ranGen{defaultGen.u64()};
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const uint N = NUM_INVOKES;
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auto expect = (Engine::max() - Engine::min()) / 2;
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auto drawRandom = [&]()
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{
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uint fail{0};
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double avg{0.0};
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for (uint i=0; i<N; ++i)
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{
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auto r = ranGen();
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if (r < Engine::min() or r > Engine::max())
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++fail;
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avg += 1.0/N * (r % Engine::max());
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}
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auto error = avg/expect - 1;
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results.post (error, fail);
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};
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auto [dur,sum] = threadBenchmark<NUM_THREADS> (drawRandom, NUM_REPEATS);
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for (auto res : results)
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SHOW_EXPR(res);
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SHOW_EXPR(sum)
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SHOW_EXPR(dur/NUM_INVOKES)
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}
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};
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LAUNCHER (RandomConcurrent_test, "unit common");
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}} // namespace lib::test
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