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Library: sharpen criteria for detecting glitches

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A deeper investigation revealed that we can show the result of glitches
for each relevant situation, simply by scrutinising the produced distribution.
Even the 64-bit-Variant shows a skewed distribuion, in spite of all numbers
being within definition range.

So the conclusion is: we can expect tilted results, but in many cases
this might not be an issue, if the result range is properly wrapped / clipped.
Notably this is the case if we just want to inject a randomised sleep into a multithreaded test setup

Build a self-contained test case to document these findings.
This commit is contained in:
Fischlurch 2024-11-16 19:34:37 +01:00
parent a0336685dc
commit 693ba32c8e
2 changed files with 183 additions and 48 deletions
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141
tests/library/random-concurrent-test.cpp
View file

@ -34,6 +34,8 @@
#include "lib/util.hpp"
#include "lib/scoped-collection.hpp"
#include "lib/test/microbenchmark.hpp"
#include "lib/format-string.hpp"
#include "lib/format-cout.hpp"
#include "lib/test/diagnostic-output.hpp"
#include <deque>
@ -43,18 +45,18 @@
//using std::array;
using std::tuple;
using std::deque;
using util::_Fmt;
namespace lib {
namespace test {
namespace {
const uint NUM_THREADS = 8;
const uint NUM_REPEATS = 10;
const uint NUM_INVOKES = 1'000'000;
const uint NUM_THREADS = 8; ///< for concurrent probes
const uint NUM_SAMPLES = 80; ///< overall number measurement runs
const uint NUM_INVOKES = 1'000'000; ///< invocations of the target per measurment
}
/******************************************************************//**
* @test demonstrate simple access to random number generation,
* as well as the setup of controlled random number sequences.
@ -71,52 +73,103 @@ namespace test {
}
template<typename GEN, uint threads>
struct Experiment
: Sync<>
{
deque<tuple<double,uint>> results;
void
recordRun (double err, uint fails)
{
Lock sync(this);
results.emplace_back (err, fails);
}
GEN generator;
Experiment(GEN&& fun)
: generator{move (fun)}
{ }
const uint N = NUM_INVOKES;
const uint REPEATS = NUM_SAMPLES / threads;
using ResVal = typename GEN::result_type;
ResVal expect = (GEN::max() - GEN::min()) / 2;
/* === Measurement Results === */
double percentGlitches{0.0};
double percentTilted {0.0};
bool isFailure {false};
void
perform()
{
auto drawRandom = [&]()
{
uint fail{0};
double avg{0.0};
for (uint i=0; i<N; ++i)
{
auto r = generator();
if (r < GEN::min() or r > GEN::max())
++fail;
avg += 1.0/N * r;//(r % Engine::max());
}
auto error = avg/expect - 1;
recordRun (error, fail);
};
threadBenchmark<threads> (drawRandom, REPEATS);
uint cases{0}, lows{0}, glitches{0};
_Fmt resultLine{"%6.3f ‰ : %d %s"};
for (auto [err,fails] : results)
{
bool isGlitch = fails or fabs(err) >0.003;
cout << resultLine % (err*1000)
% fails
% (fails? "FAIL": isGlitch? " !! ":"") << endl;
++cases;
if (err < 0) ++lows;
if (isGlitch) ++glitches;
}
// assess overall results......
percentGlitches = 100.0 * glitches/cases;
percentTilted = 100.0 * fabs(double(lows)/cases - 0.5)*2;
isFailure = glitches or percentTilted > 30;
cout << _Fmt{"++-------------++ %s\n"
" Glitches: %5.1f %%\n"
" Tilted: %5.1f %%\n"
"++-------------++\n"}
% (isFailure? "FAIL": "(ok)")
% percentGlitches
% percentTilted
<< endl;
}
};
/** @test examine behaviour of PRNG under concurrency stress */
void
investigate_concurrentAccess()
{
struct Results
: deque<tuple<double,uint>>
, Sync<>
{
void
post (double err, uint fails)
{
Lock sync(this);
emplace_back (err, fails);
}
};
Results results;
using Mersenne32 = std::mt19937;
using Mersenne64 = std::mt19937_64;
using Engine = std::mt19937;
// using Engine = std::mt19937_64;
Experiment<Mersenne32,1> single32{Mersenne32(defaultGen.uni())};
Experiment<Mersenne32,NUM_THREADS> concurr32{Mersenne32(defaultGen.uni())};
Experiment<Mersenne64,NUM_THREADS> concurr64{Mersenne64(defaultGen.uni())};
Engine ranGen{defaultGen.u64()};
single32.perform();
concurr32.perform();
concurr64.perform();
const uint N = NUM_INVOKES;
auto expect = (Engine::max() - Engine::min()) / 2;
auto drawRandom = [&]()
{
uint fail{0};
double avg{0.0};
for (uint i=0; i<N; ++i)
{
auto r = ranGen();
if (r < Engine::min() or r > Engine::max())
++fail;
avg += 1.0/N * (r % Engine::max());
}
auto error = avg/expect - 1;
results.post (error, fail);
};
auto [dur,sum] = threadBenchmark<NUM_THREADS> (drawRandom, NUM_REPEATS);
for (auto res : results)
SHOW_EXPR(res);
SHOW_EXPR(sum)
SHOW_EXPR(dur/NUM_INVOKES)
CHECK (not single32.isFailure, "ALARM : single-threaded Mersenne-Twister 32bit produces skewed distribution");
CHECK ( single32.isFailure, "SURPRISE : Mersenne-Twister 32bit encountered NO glitches under concurrent pressure");
CHECK ( single64.isFailure, "SURPRISE : Mersenne-Twister 64bit encountered NO glitches under concurrent pressure");
}
};

90
wiki/thinkPad.ichthyo.mm
View file

@ -58080,11 +58080,11 @@
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@ -58099,6 +58099,88 @@
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</node>
</node>
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<icon BUILTIN="button_ok"/>
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</node>
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</node>
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</node>
</node>
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<icon BUILTIN="idea"/>
</node>
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<head>
</head>
<body>
<p>
schon kleine &#196;nderungen in der Payload-Funktion k&#246;nnen die Inzidenz der Probleme drastisch &#228;ndern; beispielsweise hat das Hinzuf&#252;gen einer Begrenzung per Modulo f&#252;r einen Generator die Inzidenz drastisch erh&#246;ht, f&#252;r einen anderen die Probleme nahezu zum Verschwinden gebracht
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</node>
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</node>
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