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 // This file is part of the Acts project.
 //
 // Copyright (C) 2020 CERN for the benefit of the Acts project
 //
 // This Source Code Form is subject to the terms of the Mozilla Public
 // License, v. 2.0. If a copy of the MPL was not distributed with this
 // file, You can obtain one at http://mozilla.org/MPL/2.0/.
 
 #include <boost/test/data/test_case.hpp>
 #include <boost/test/unit_test.hpp>
 
 #include "Acts/Tests/CommonHelpers/BenchmarkTools.hpp"
 
 #include "Acts/Tests/CommonHelpers/FloatComparisons.hpp"
 
 #include <cmath>
 #include <complex>
 #include <iostream>
 #include <sstream>
 #include <tuple>
 
 namespace Acts::Test {
 
 // Basic non-timing tests do not validate the core performance aspects of the
 // benchmark tools, but have the advantage of being runnable on any system.
 BOOST_AUTO_TEST_SUITE(benchmark_tools)
 
 BOOST_AUTO_TEST_CASE(assume_accessed) {
   int x = 42;
   assumeAccessed(x);
   BOOST_CHECK_EQUAL(x, 42);
 }
 
 BOOST_AUTO_TEST_CASE(assume_read) {
   float x = 4.2f;
   assumeRead(x);
   BOOST_CHECK_EQUAL(x, 4.2f);
 
   const std::string y = "LOL";
   assumeRead(x);
   BOOST_CHECK_EQUAL(y, "LOL");
 
   assumeRead(std::make_tuple(1, false, 3.5));
 }
 
 BOOST_AUTO_TEST_CASE(assume_written) {
   std::complex c(1.2, 3.4);
   assumeWritten(c);
   BOOST_CHECK_EQUAL(c, std::complex(1.2, 3.4));
 }
 
 BOOST_AUTO_TEST_CASE(micro_benchmark_result) {
   MicroBenchmarkResult res;
   res.iters_per_run = 42;
   res.run_timings = {
       std::chrono::microseconds(420), std::chrono::microseconds(21),
       std::chrono::milliseconds(4),   std::chrono::microseconds(84),
       std::chrono::microseconds(294), std::chrono::microseconds(378),
       std::chrono::microseconds(126), std::chrono::milliseconds(42)};
 
   CHECK_CLOSE_REL(res.totalTime().count() / 1'000'000., 47.323, 1e-6);
 
   const auto sorted = res.sortedRunTimes();
   BOOST_CHECK_EQUAL(sorted.size(), res.run_timings.size());
   BOOST_CHECK_EQUAL(sorted[0].count(), 21'000.);
   BOOST_CHECK_EQUAL(sorted[1].count(), 84'000.);
   BOOST_CHECK_EQUAL(sorted[2].count(), 126'000.);
   BOOST_CHECK_EQUAL(sorted[3].count(), 294'000.);
   BOOST_CHECK_EQUAL(sorted[4].count(), 378'000.);
   BOOST_CHECK_EQUAL(sorted[5].count(), 420'000.);
   BOOST_CHECK_EQUAL(sorted[6].count(), 4'000'000.);
   BOOST_CHECK_EQUAL(sorted[7].count(), 42'000'000.);
 
   CHECK_CLOSE_REL(res.runTimeMedian().count() / 1000., (294. + 378.) / 2.,
                   1e-6);
 
   const auto [firstq, thirdq] = res.runTimeQuartiles();
   CHECK_CLOSE_REL(firstq.count() / 1000., (84. + 126.) / 2., 1e-6);
   CHECK_CLOSE_REL(thirdq.count() / 1000., (420. + 4000.) / 2., 1e-6);
 
   const auto robustRTStddev = res.runTimeRobustStddev();
   CHECK_CLOSE_REL(robustRTStddev.count(), (thirdq - firstq).count() / 1.349,
                   1e-3);
 
   const auto runTimeError = res.runTimeError();
   CHECK_CLOSE_REL(
       runTimeError.count(),
       1.2533 * robustRTStddev.count() / std::sqrt(res.run_timings.size()),
       1e-3);
 
   CHECK_CLOSE_REL(res.iterTimeAverage().count(),
                   res.runTimeMedian().count() / res.iters_per_run, 1e-6);
 
   CHECK_CLOSE_REL(res.iterTimeError().count(),
                   runTimeError.count() / std::sqrt(res.iters_per_run), 1e-6);
 
   std::ostringstream os;
   os << res;
   BOOST_CHECK_EQUAL(os.str(),
                     "8 runs of 42 iteration(s), 47.3ms total, "
                     "336.0000+/-1355.2296µs per run, "
                     "8000.000+/-209116.462ns per iteration");
 }
 
 BOOST_AUTO_TEST_CASE(micro_benchmark) {
   int counter = 0;
   microBenchmark([&] { ++counter; }, 15, 7, std::chrono::milliseconds(0));
   BOOST_CHECK_EQUAL(counter, 15 * 7);
 
   counter = 0;
   microBenchmark(
       [&] {
         ++counter;
         return counter;
       },
       17, 11, std::chrono::milliseconds(0));
   BOOST_CHECK_EQUAL(counter, 17 * 11);
 
   counter = 0;
   int previous = 64;
   std::vector<int> ints{1, 2, 4, 8, 16, 32, 64};
   microBenchmark(
       [&](int input) {
         if (input == 1) {
           BOOST_CHECK_EQUAL(previous, 64);
           counter = 1;
         } else {
           BOOST_CHECK_EQUAL(input, previous * 2);
           counter += input;
         }
         previous = input;
       },
       ints, 123, std::chrono::milliseconds(3));
   BOOST_CHECK_EQUAL(counter, 127);
 
   counter = 0;
   previous = -81;
   std::vector<char> chars{-1, 3, -9, 27, -81};
   microBenchmark(
       [&](int input) {
         if (input == -1) {
           BOOST_CHECK_EQUAL(previous, -81);
           counter = -1;
         } else {
           BOOST_CHECK_EQUAL(input, -previous * 3);
           counter += input;
         }
         previous = input;
         return &previous;
       },
       chars, 456, std::chrono::milliseconds(8));
   BOOST_CHECK_EQUAL(counter, -61);
 }
 
 BOOST_AUTO_TEST_SUITE_END()
 
 // Timing tests are perhaps the most important ones for validation of
 // benchmarking tools, but they cannot be run by default for two reasons:
 // - They take a while to run, and therefore slow down the testing cycle
 // - They require a quiet system to succeed, and will likely fail when invoked
 //   by a parallel run of CTest or when run on a continuous integration VM.
 //
 // If you can ensure both of these preconditions, you can run the test with
 // ./BenchmarkTools --run_test=benchmark_timings
 BOOST_AUTO_TEST_SUITE(benchmark_timings, *boost::unit_test::disabled())
 
 constexpr std::size_t bench_iters = 1'000;
 
 BOOST_AUTO_TEST_CASE(micro_benchmark) {
   using namespace std::literals::chrono_literals;
 
   // For simple microbenchmarking needs, plain use of microBenchmark is enough.
   //
   // For example, here, the microbenchmark loop isn't optimized out even though
   // each iteration does literally nothing. If it were optimized out, the time
   // per iteration would change, since we wouldn't get linear scaling anymore.
   auto nop = [] {};
   const auto nop_x10 = microBenchmark(nop, 10 * bench_iters);
   std::cout << "nop (10x iters): " << nop_x10 << std::endl;
   const auto nop_x100 = microBenchmark(nop, 100 * bench_iters);
   std::cout << "nop (100x iters): " << nop_x100 << std::endl;
   const double nop_x10_iter_ns = nop_x10.iterTimeAverage().count();
   const double nop_x100_iter_ns = nop_x100.iterTimeAverage().count();
   CHECK_CLOSE_REL(nop_x10_iter_ns, nop_x100_iter_ns, 0.1);
 
 // These tests reason about the performance characteristics of _optimized_ code,
 // and should therefore be compiled out of debug/coverage builds.
 #ifdef __OPTIMIZE__
   // The microbenchmarking harness is super low overhead, less than 1
   // nanosecond per iteration on a modern CPU.
   BOOST_CHECK_LT(nop_x100_iter_ns, 1.0);
 
   // With a well-chosen iteration count that keeps per-run times under the OS
   // scheduling quantum (typically 1ms), the noise is also super low.
   BOOST_CHECK_LT(nop_x100.iterTimeError().count(), 0.1);
 
   // You can measure the overhead of any operation as long as it's not
   // _obnoxiously_ amenable to compiler const-propagation or dead code
   // elimination. For example, this sqrt throughput microbenchmark works,
   // because microBenchmark forces the compiler to assume that "x", "y" and "z"
   // are modified on every benchmark iteration...
   const double x = 1.2, y = 3.4, z = 5.6;
   auto sqrt = microBenchmark(
       [&] { return std::sqrt(x * y) + std::sqrt(y * z) + std::sqrt(z * x); },
       bench_iters);
   std::cout << "sqrt (correct): " << sqrt << std::endl;
   BOOST_CHECK_GT(sqrt.iterTimeAverage().count(), 10. * nop_x100_iter_ns);
 
   // ...but this variant doesn't work, because the compiler can trivially
   // precompute the square root when optimizing the inner lambda...
   const auto sqrt_constprop = microBenchmark(
       [] {
         return std::sqrt(1.2 * 3.4) + std::sqrt(3.4 * 5.6) +
                std::sqrt(5.6 * 1.2);
       },
       bench_iters * 20);
   std::cout << "sqrt (constprop'd): " << sqrt_constprop << std::endl;
   BOOST_CHECK_LT(sqrt_constprop.iterTimeAverage().count(),
                  sqrt.iterTimeAverage().count() / 5.);
 
   // ...and this one doesn't work either, because the compiler can trivially
   // infer that the result of the computation is unused and stop computing it.
   //
   // The lower tolerance of this test is needed because current GCC doesn't
   // optimize _everything_ out in its default configuration, as sqrt could still
   // have side-effects like setting the errno thread-local variable...
   const auto sqrt_deadcode = microBenchmark(
       [&] { (void)(std::sqrt(x * y) + std::sqrt(y * z) + std::sqrt(z * x)); },
       bench_iters * 10);
   std::cout << "sqrt (deadcode'd): " << sqrt_deadcode << std::endl;
   BOOST_CHECK_LT(sqrt_deadcode.iterTimeAverage().count(),
                  sqrt.iterTimeAverage().count() / 3.);
 #endif
 }
 
 // These tests reason about the performance characteristics of _optimized_ code,
 // and should therefore be compiled out of debug/coverage builds.
 #ifdef __OPTIMIZE__
 BOOST_AUTO_TEST_CASE(assume_read) {
   // You can use assumeRead when you want the compiler to assume that the result
   // of some computation has been read and therefore the computation shouldn't
   // be optimized out. This is what microBenchmark implicitly does to the value
   // returned by the benchmark iteration function, if any.
   //
   // For example, these two computations are almost equivalent. Notice that
   // assumeRead can be used on temporaries.
   const double x = 1.2, y = 3.4, z = 5.6;
   const auto tuple_return = microBenchmark(
       [&] {
         return std::make_tuple(
             std::sqrt(x * y), std::complex(std::sqrt(y * z), std::sqrt(z * x)));
       },
       bench_iters);
   std::cout << "tuple return: " << tuple_return << std::endl;
   const auto assumeread = microBenchmark(
       [&] {
         assumeRead(std::sqrt(x * y));
         assumeRead(std::complex(std::sqrt(y * z), std::sqrt(z * x)));
       },
       bench_iters);
   std::cout << "assumeRead: " << assumeread << std::endl;
   const double tuple_return_iter_ns = tuple_return.iterTimeAverage().count();
   const double assumeRead_iter_ns = assumeread.iterTimeAverage().count();
   CHECK_CLOSE_REL(tuple_return_iter_ns, assumeRead_iter_ns, 1e-2);
 }
 #endif
 
 BOOST_AUTO_TEST_CASE(assume_written) {
   // You can use assumeWritten when you want the compiler to assume that some
   // variables have been written to, and every dependent computation must
   // therefore be recomputed. This is what microBenchmark implicitly does to
   // every variable captured by the benchmark iteration lambda.
   //
   // Since assumeWritten operates on variables in memory, it cannot be used on
   // temporaries, but only on mutable variables.
   double x = 1.2, y = 3.4, z = 5.6;
   auto sqrt_sum = microBenchmark(
       [&] { return std::sqrt(x * y) + std::sqrt(y * z) + std::sqrt(z * x); },
       bench_iters);
   std::cout << "sqrt sum: " << sqrt_sum << std::endl;
   auto sqrt_2sums = microBenchmark(
       [&] {
         double tmp = std::sqrt(x * y) + std::sqrt(y * z) + std::sqrt(z * x);
         assumeWritten(x);
         assumeWritten(y);
         assumeWritten(z);
         return tmp + std::sqrt(x * y) + std::sqrt(y * z) + std::sqrt(z * x);
       },
       bench_iters);
   std::cout << "2x(sqrt sum): " << sqrt_2sums << std::endl;
   const double sqrt_sum_iter_ns = sqrt_sum.iterTimeAverage().count();
   const double sqrt_2sums_iter_ns = sqrt_2sums.iterTimeAverage().count();
   CHECK_CLOSE_REL(2. * sqrt_sum_iter_ns, sqrt_2sums_iter_ns, 1e-2);
 }
 
 BOOST_AUTO_TEST_SUITE_END()
 
 }  // namespace Acts::Test

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