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lumiera_/tests/vault/gear/scheduler-stress-test.cpp
Ichthyostega 93729e5667 Scheduler-test: more precise accounting for expected concurrency 
It turns out to be not correct using all the divergence in concurrency
as a form factor, since it is quite common that not all cores can be active
at every level, given the structural constraints as dictated by the load graph.

On the other hand, if the empirical work (non wait-time) concurrency
systematically differs from the simple model used for establishing the schedule,
then this should indeed be considered a form factor and deduced from
the effective stress factor, since it is not a reserve available for speed-up

The solution entertained here is to derive an effective compounded sum
of weights from the calculation used to build the schedule. This compounded
weight sum is typically lower than the plain sum of all node weights, which
is precisely due to the theoretical amount of expense reduction assumed
in the schedule generation. So this gives us a handle at the theoretically
expected expense and through the plain weight sum, we may draw conclusion
about the effective concurrency expected in this schedule.

Taking only this part as base for the empirical deviations yields search results
very close to stressFactor ~1 -- implying that the test setup now
observes what was intended to observe...
2024-02-23 02:02:20 +01:00

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/*
SchedulerStress(Test) - verify scheduler performance characteristics
Copyright (C) Lumiera.org
2023, Hermann Vosseler <Ichthyostega@web.de>
This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License as
published by the Free Software Foundation; either version 2 of
the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
* *****************************************************/
/** @file scheduler-usage-test.cpp
** unit test \ref SchedulerStress_test
*/
#include "lib/test/run.hpp"
#include "test-chain-load.hpp"
#include "stress-test-rig.hpp"
#include "vault/gear/scheduler.hpp"
#include "lib/time/timevalue.hpp"
#include "lib/format-string.hpp"
#include "lib/format-cout.hpp"
#include "lib/test/diagnostic-output.hpp"//////////////////////////TODO work in distress
//#include "lib/format-string.hpp"
#include "lib/test/transiently.hpp"
//#include "lib/test/microbenchmark.hpp"
//#include "lib/util.hpp"
//#include <utility>
//#include <vector>
#include <array>
using test::Test;
//using std::move;
//using util::isSameObject;
namespace vault{
namespace gear {
namespace test {
// using lib::time::FrameRate;
// using lib::time::Offset;
// using lib::time::Time;
using util::_Fmt;
// using std::vector;
using std::array;
namespace { // Test definitions and setup...
}
/***************************************************************************//**
* @test Investigate and verify non-functional characteristics of the Scheduler.
* @see SchedulerActivity_test
* @see SchedulerInvocation_test
* @see SchedulerCommutator_test
* @see stress-test-rig.hpp
*/
class SchedulerStress_test : public Test
{
virtual void
run (Arg)
{
//smokeTest();
// setup_systematicSchedule();
search_breaking_point();
// verify_instrumentation();
// investigateWorkProcessing();
walkingDeadline();
}
/** @test TODO demonstrate sustained operation under load
* - TODO this is a placeholder and works now, but need a better example
* - it should not produce so much overload, rather some stretch of steady-state processing
* @todo WIP 12/23 🔁 define ⟶ implement
*/
void
smokeTest()
{
MARK_TEST_FUN
TestChainLoad testLoad{512};
testLoad.configureShape_chain_loadBursts()
.buildTopology()
// .printTopologyDOT()
;
auto stats = testLoad.computeGraphStatistics();
cout << _Fmt{"Test-Load: Nodes: %d Levels: %d ∅Node/Level: %3.1f Forks: %d Joins: %d"}
% stats.nodes
% stats.levels
% stats.indicators[STAT_NODE].pL
% stats.indicators[STAT_FORK].cnt
% stats.indicators[STAT_JOIN].cnt
<< endl;
// while building the calculation-plan graph
// node hashes were computed, observing dependencies
size_t expectedHash = testLoad.getHash();
// some jobs/nodes are marked with a weight-step
// these can be instructed to spend some CPU time
auto LOAD_BASE = 500us;
testLoad.performGraphSynchronously(LOAD_BASE);
CHECK (testLoad.getHash() == expectedHash);
double referenceTime = testLoad.calcRuntimeReference(LOAD_BASE);
cout << "refTime(singleThr): "<<referenceTime/1000<<"ms"<<endl;
// Perform through Scheduler----------
BlockFlowAlloc bFlow;
EngineObserver watch;
Scheduler scheduler{bFlow, watch};
double performanceTime =
testLoad.setupSchedule(scheduler)
.withLoadTimeBase(LOAD_BASE)
.withJobDeadline(150ms)
.withPlanningStep(200us)
.withChunkSize(20)
.launch_and_wait();
cout << "runTime(Scheduler): "<<performanceTime/1000<<"ms"<<endl;
// invocation through Scheduler has reproduced all node hashes
CHECK (testLoad.getHash() == expectedHash);
}
/** @test build a scheme to adapt the schedule to expected runtime.
* - as in many other tests, use the massively forking load pattern
* - demonstrate how TestChainLoad computes an idealised level expense
* - verify how schedule times are derived from this expense sequence
* @todo WIP 12/23 ✔ define ⟶ ✔ implement
*/
void
setup_systematicSchedule()
{
TestChainLoad testLoad{64};
testLoad.configureShape_chain_loadBursts()
.buildTopology()
// .printTopologyDOT()
// .printTopologyStatistics()
;
auto LOAD_BASE = 500us;
ComputationalLoad cpuLoad;
cpuLoad.timeBase = LOAD_BASE;
cpuLoad.calibrate();
double micros = cpuLoad.invoke();
CHECK (micros < 550);
CHECK (micros > 450);
// build a schedule sequence based on
// summing up weight factors, with example concurrency ≔ 4
uint concurrency = 4;
auto stepFactors = testLoad.levelScheduleSequence(concurrency).effuse();
CHECK (stepFactors.size() == 1+testLoad.topLevel());
CHECK (stepFactors.size() == 27);
// Build-Performance-test-setup--------
BlockFlowAlloc bFlow;
EngineObserver watch;
Scheduler scheduler{bFlow, watch};
auto testSetup =
testLoad.setupSchedule(scheduler)
.withLoadTimeBase(LOAD_BASE)
.withJobDeadline(50ms)
.withUpfrontPlanning();
auto schedule = testSetup.getScheduleSeq().effuse();
CHECK (schedule.size() == testLoad.topLevel() + 2);
CHECK (schedule[ 0] == _uTicks(0ms));
CHECK (schedule[ 1] == _uTicks(1ms));
CHECK (schedule[ 2] == _uTicks(2ms));
// ....
CHECK (schedule[25] == _uTicks(25ms));
CHECK (schedule[26] == _uTicks(26ms));
CHECK (schedule[27] == _uTicks(27ms));
// Adapted Schedule----------
double stressFac = 1.0;
testSetup.withAdaptedSchedule (stressFac, concurrency);
schedule = testSetup.getScheduleSeq().effuse();
CHECK (schedule.size() == testLoad.topLevel() + 2);
CHECK (schedule[ 0] == _uTicks(0ms));
CHECK (schedule[ 1] == _uTicks(0ms));
// verify the numbers in detail....
_Fmt stepFmt{"lev:%-2d stepFac:%-6.3f schedule:%6.3f"};
auto stepStr = [&](uint i){ return string{stepFmt % i % stepFactors[i>0?i-1:0] % (_raw(schedule[i])/1000.0)}; };
CHECK (stepStr( 0) == "lev:0 stepFac:0.000 schedule: 0.000"_expect);
CHECK (stepStr( 1) == "lev:1 stepFac:0.000 schedule: 0.000"_expect);
CHECK (stepStr( 2) == "lev:2 stepFac:0.000 schedule: 0.000"_expect);
CHECK (stepStr( 3) == "lev:3 stepFac:2.000 schedule: 1.000"_expect);
CHECK (stepStr( 4) == "lev:4 stepFac:2.000 schedule: 1.000"_expect);
CHECK (stepStr( 5) == "lev:5 stepFac:2.000 schedule: 1.000"_expect);
CHECK (stepStr( 6) == "lev:6 stepFac:2.000 schedule: 1.000"_expect);
CHECK (stepStr( 7) == "lev:7 stepFac:3.000 schedule: 1.500"_expect);
CHECK (stepStr( 8) == "lev:8 stepFac:5.000 schedule: 2.500"_expect);
CHECK (stepStr( 9) == "lev:9 stepFac:7.000 schedule: 3.500"_expect);
CHECK (stepStr(10) == "lev:10 stepFac:8.000 schedule: 4.000"_expect);
CHECK (stepStr(11) == "lev:11 stepFac:8.000 schedule: 4.000"_expect);
CHECK (stepStr(12) == "lev:12 stepFac:8.000 schedule: 4.000"_expect);
CHECK (stepStr(13) == "lev:13 stepFac:9.000 schedule: 4.500"_expect);
CHECK (stepStr(14) == "lev:14 stepFac:10.000 schedule: 5.000"_expect);
CHECK (stepStr(15) == "lev:15 stepFac:12.000 schedule: 6.000"_expect);
CHECK (stepStr(16) == "lev:16 stepFac:12.000 schedule: 6.000"_expect);
CHECK (stepStr(17) == "lev:17 stepFac:13.000 schedule: 6.500"_expect);
CHECK (stepStr(18) == "lev:18 stepFac:16.000 schedule: 8.000"_expect);
CHECK (stepStr(19) == "lev:19 stepFac:16.000 schedule: 8.000"_expect);
CHECK (stepStr(20) == "lev:20 stepFac:20.000 schedule:10.000"_expect);
CHECK (stepStr(21) == "lev:21 stepFac:22.500 schedule:11.250"_expect);
CHECK (stepStr(22) == "lev:22 stepFac:24.167 schedule:12.083"_expect);
CHECK (stepStr(23) == "lev:23 stepFac:26.167 schedule:13.083"_expect);
CHECK (stepStr(24) == "lev:24 stepFac:28.167 schedule:14.083"_expect);
CHECK (stepStr(25) == "lev:25 stepFac:30.867 schedule:15.433"_expect);
CHECK (stepStr(26) == "lev:26 stepFac:31.867 schedule:15.933"_expect);
CHECK (stepStr(27) == "lev:27 stepFac:32.867 schedule:16.433"_expect);
// Adapted Schedule with lower stress level and higher concurrency....
stressFac = 0.3;
concurrency = 6;
stepFactors = testLoad.levelScheduleSequence(concurrency).effuse();
testSetup.withAdaptedSchedule (stressFac, concurrency);
schedule = testSetup.getScheduleSeq().effuse();
CHECK (stepStr( 0) == "lev:0 stepFac:0.000 schedule: 0.000"_expect);
CHECK (stepStr( 1) == "lev:1 stepFac:0.000 schedule: 0.000"_expect);
CHECK (stepStr( 2) == "lev:2 stepFac:0.000 schedule: 0.000"_expect);
CHECK (stepStr( 3) == "lev:3 stepFac:2.000 schedule: 3.333"_expect);
CHECK (stepStr( 4) == "lev:4 stepFac:2.000 schedule: 3.333"_expect);
CHECK (stepStr( 5) == "lev:5 stepFac:2.000 schedule: 3.333"_expect);
CHECK (stepStr( 6) == "lev:6 stepFac:2.000 schedule: 3.333"_expect);
CHECK (stepStr( 7) == "lev:7 stepFac:3.000 schedule: 5.000"_expect);
CHECK (stepStr( 8) == "lev:8 stepFac:5.000 schedule: 8.333"_expect);
CHECK (stepStr( 9) == "lev:9 stepFac:7.000 schedule:11.666"_expect);
CHECK (stepStr(10) == "lev:10 stepFac:8.000 schedule:13.333"_expect);
CHECK (stepStr(11) == "lev:11 stepFac:8.000 schedule:13.333"_expect);
CHECK (stepStr(12) == "lev:12 stepFac:8.000 schedule:13.333"_expect);
CHECK (stepStr(13) == "lev:13 stepFac:9.000 schedule:15.000"_expect);
CHECK (stepStr(14) == "lev:14 stepFac:10.000 schedule:16.666"_expect);
CHECK (stepStr(15) == "lev:15 stepFac:12.000 schedule:20.000"_expect);
CHECK (stepStr(16) == "lev:16 stepFac:12.000 schedule:20.000"_expect);
CHECK (stepStr(17) == "lev:17 stepFac:13.000 schedule:21.666"_expect);
CHECK (stepStr(18) == "lev:18 stepFac:16.000 schedule:26.666"_expect);
CHECK (stepStr(19) == "lev:19 stepFac:16.000 schedule:26.666"_expect);
CHECK (stepStr(20) == "lev:20 stepFac:18.000 schedule:30.000"_expect); // note: here the higher concurrency allows to process all 5 concurrent nodes at once
CHECK (stepStr(21) == "lev:21 stepFac:20.500 schedule:34.166"_expect);
CHECK (stepStr(22) == "lev:22 stepFac:22.167 schedule:36.944"_expect);
CHECK (stepStr(23) == "lev:23 stepFac:23.167 schedule:38.611"_expect);
CHECK (stepStr(24) == "lev:24 stepFac:24.167 schedule:40.277"_expect);
CHECK (stepStr(25) == "lev:25 stepFac:25.967 schedule:43.277"_expect);
CHECK (stepStr(26) == "lev:26 stepFac:26.967 schedule:44.944"_expect);
CHECK (stepStr(27) == "lev:27 stepFac:27.967 schedule:46.611"_expect);
// perform a Test with this low stress level (0.3)
double runTime = testSetup.launch_and_wait();
double expected = testSetup.getExpectedEndTime();
CHECK (fabs (runTime-expected) < 5000);
} // Scheduler should able to follow the expected schedule
/** @test verify capability for instrumentation of job invocations
* @see IncidenceCount_test
* @todo WIP 2/24 ✔ define ⟶ ✔ implement
*/
void
verify_instrumentation()
{
const size_t NODES = 20;
const size_t CORES = work::Config::COMPUTATION_CAPACITY;
auto LOAD_BASE = 5ms;
TestChainLoad testLoad{NODES};
BlockFlowAlloc bFlow;
EngineObserver watch;
Scheduler scheduler{bFlow, watch};
auto testSetup =
testLoad.setWeight(1)
.setupSchedule(scheduler)
.withLoadTimeBase(LOAD_BASE)
.withJobDeadline(50ms)
.withInstrumentation() // activate an instrumentation bracket around each job invocation
;
double runTime = testSetup.launch_and_wait();
auto stat = testSetup.getInvocationStatistic(); // retrieve observed invocation statistics
CHECK (runTime < stat.activeTime);
CHECK (isLimited (4900, stat.activeTime/NODES, 8000)); // should be close to 5000
CHECK (stat.coveredTime < runTime);
CHECK (NODES == stat.activationCnt); // each node activated once
CHECK (isLimited (CORES/2, stat.avgConcurrency, CORES)); // should ideally come close to hardware concurrency
CHECK (0 == stat.timeAtConc(0));
CHECK (0 == stat.timeAtConc(CORES+1));
CHECK (runTime/2 < stat.timeAtConc(CORES-1)+stat.timeAtConc(CORES));
} // should ideally spend most of the time at highest concurrency levels
/** @test TODO determine the breaking point towards scheduler overload
* - use the integrated StressRig
* - demonstrate how parameters can be tweaked
* - perform a run, leading to a binary search for the breaking point
* @note on my machine, I observe stress factors close below 0.5, due to the fact
* that the ComputationalLoad typically takes 2 times as long in concurrent
* usage compared to its calibration, which is done in a tight loop. This
* is strange and may well be due to some peculiarity of my system. Which
* also implies that this test's behaviour might be difficult to verify,
* other than by qualitative interpretation of the log output on STDOUT.
* @see stress-test-rig.hpp
* @todo WIP 1/24 ✔ define ⟶ ✔ implement
*/
void
search_breaking_point()
{
MARK_TEST_FUN
struct Setup : StressRig
{
usec LOAD_BASE = 500us;
uint CONCURRENCY = 4;
bool showRuns = true;
auto testLoad() { return TestChainLoad<>{64}.configureShape_chain_loadBursts(); }
};
auto [stress,delta,time] = StressRig::with<Setup>().searchBreakingPoint();
CHECK (delta > 2.5);
CHECK (1.15 > stress and stress > 0.9);
}
/** @test TODO
* @todo WIP 1/24 🔁 define ⟶ implement
*/
void
investigateWorkProcessing()
{
MARK_TEST_FUN
TestChainLoad<8> testLoad{256};
testLoad.seedingRule(testLoad.rule().probability(0.6).minVal(2))
.pruningRule(testLoad.rule().probability(0.44))
.setSeed(55)
.buildTopology()
// .printTopologyDOT()
// .printTopologyStatistics()
;
// ////////////////////////////////////////////////////////WIP : Run test directly for investigation of SEGFAULT....
// BlockFlowAlloc bFlow;
// EngineObserver watch;
// Scheduler scheduler{bFlow, watch};
auto LOAD_BASE = 500us;
// auto stressFac = 1.0;
// auto concurrency = 8;
//
ComputationalLoad cpuLoad;
cpuLoad.timeBase = LOAD_BASE;
cpuLoad.calibrate();
//
double loadMicros = cpuLoad.invoke();
// double refTime = testLoad.calcRuntimeReference(LOAD_BASE);
SHOW_EXPR(loadMicros)
//
// auto testSetup =
// testLoad.setupSchedule(scheduler)
// .withLoadTimeBase(LOAD_BASE)
// .withJobDeadline(50ms)
// .withUpfrontPlanning()
// .withAdaptedSchedule (stressFac, concurrency);
// double runTime = testSetup.launch_and_wait();
// double expected = testSetup.getExpectedEndTime();
//SHOW_EXPR(runTime)
//SHOW_EXPR(expected)
//SHOW_EXPR(refTime)
struct Setup : StressRig
{
usec LOAD_BASE = 500us;
usec BASE_EXPENSE = 200us;
double UPPER_STRESS = 12;
//
double FAIL_LIMIT = 1.0; //0.7;
double TRIGGER_SDEV = 1.0; //0.25;
double TRIGGER_DELTA = 2.0; //0.5;
// uint CONCURRENCY = 4;
// bool SCHED_DEPENDS = true;
bool showRuns = true;
auto
testLoad()
{
TestChainLoad<8> testLoad{256};
testLoad.seedingRule(testLoad.rule().probability(0.6).minVal(2))
.pruningRule(testLoad.rule().probability(0.44))
.weightRule(testLoad.value(1))
.setSeed(55);
return testLoad;
}
};
auto [stress,delta,time] = StressRig::with<Setup>().searchBreakingPoint();
SHOW_EXPR(stress)
SHOW_EXPR(delta)
SHOW_EXPR(time)
}
/** @test TODO
* @todo WIP 1/24 🔁 define ⟶ implement
*/
void
walkingDeadline()
{
}
};
/** Register this test class... */
LAUNCHER (SchedulerStress_test, "unit engine");
}}} // namespace vault::gear::test
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