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authorMartin Ankerl <martin.ankerl@gmail.com>2020-06-13 09:37:27 +0200
committerMartin Ankerl <martin.ankerl@gmail.com>2020-06-13 12:24:18 +0200
commit78c312c983255e15fc274de2368a2ec13ce81cbf (patch)
tree09c5cec9b0b3f7ef2aa9364057858861c134cf45 /src/bench/prevector.cpp
parent19e919217e6d62e3640525e4149de1a4ae04e74f (diff)
downloadbitcoin-78c312c983255e15fc274de2368a2ec13ce81cbf.tar.xz
Replace current benchmarking framework with nanobench
This replaces the current benchmarking framework with nanobench [1], an MIT licensed single-header benchmarking library, of which I am the autor. This has in my opinion several advantages, especially on Linux: * fast: Running all benchmarks takes ~6 seconds instead of 4m13s on an Intel i7-8700 CPU @ 3.20GHz. * accurate: I ran e.g. the benchmark for SipHash_32b 10 times and calculate standard deviation / mean = coefficient of variation: * 0.57% CV for old benchmarking framework * 0.20% CV for nanobench So the benchmark results with nanobench seem to vary less than with the old framework. * It automatically determines runtime based on clock precision, no need to specify number of evaluations. * measure instructions, cycles, branches, instructions per cycle, branch misses (only Linux, when performance counters are available) * output in markdown table format. * Warn about unstable environment (frequency scaling, turbo, ...) * For better profiling, it is possible to set the environment variable NANOBENCH_ENDLESS to force endless running of a particular benchmark without the need to recompile. This makes it to e.g. run "perf top" and look at hotspots. Here is an example copy & pasted from the terminal output: | ns/byte | byte/s | err% | ins/byte | cyc/byte | IPC | bra/byte | miss% | total | benchmark |--------------------:|--------------------:|--------:|----------------:|----------------:|-------:|---------------:|--------:|----------:|:---------- | 2.52 | 396,529,415.94 | 0.6% | 25.42 | 8.02 | 3.169 | 0.06 | 0.0% | 0.03 | `bench/crypto_hash.cpp RIPEMD160` | 1.87 | 535,161,444.83 | 0.3% | 21.36 | 5.95 | 3.589 | 0.06 | 0.0% | 0.02 | `bench/crypto_hash.cpp SHA1` | 3.22 | 310,344,174.79 | 1.1% | 36.80 | 10.22 | 3.601 | 0.09 | 0.0% | 0.04 | `bench/crypto_hash.cpp SHA256` | 2.01 | 496,375,796.23 | 0.0% | 18.72 | 6.43 | 2.911 | 0.01 | 1.0% | 0.00 | `bench/crypto_hash.cpp SHA256D64_1024` | 7.23 | 138,263,519.35 | 0.1% | 82.66 | 23.11 | 3.577 | 1.63 | 0.1% | 0.00 | `bench/crypto_hash.cpp SHA256_32b` | 3.04 | 328,780,166.40 | 0.3% | 35.82 | 9.69 | 3.696 | 0.03 | 0.0% | 0.03 | `bench/crypto_hash.cpp SHA512` [1] https://github.com/martinus/nanobench * Adds support for asymptotes This adds support to calculate asymptotic complexity of a benchmark. This is similar to #17375, but currently only one asymptote is supported, and I have added support in the benchmark `ComplexMemPool` as an example. Usage is e.g. like this: ``` ./bench_bitcoin -filter=ComplexMemPool -asymptote=25,50,100,200,400,600,800 ``` This runs the benchmark `ComplexMemPool` several times but with different complexityN settings. The benchmark can extract that number and use it accordingly. Here, it's used for `childTxs`. The output is this: | complexityN | ns/op | op/s | err% | ins/op | cyc/op | IPC | total | benchmark |------------:|--------------------:|--------------------:|--------:|----------------:|----------------:|-------:|----------:|:---------- | 25 | 1,064,241.00 | 939.64 | 1.4% | 3,960,279.00 | 2,829,708.00 | 1.400 | 0.01 | `ComplexMemPool` | 50 | 1,579,530.00 | 633.10 | 1.0% | 6,231,810.00 | 4,412,674.00 | 1.412 | 0.02 | `ComplexMemPool` | 100 | 4,022,774.00 | 248.58 | 0.6% | 16,544,406.00 | 11,889,535.00 | 1.392 | 0.04 | `ComplexMemPool` | 200 | 15,390,986.00 | 64.97 | 0.2% | 63,904,254.00 | 47,731,705.00 | 1.339 | 0.17 | `ComplexMemPool` | 400 | 69,394,711.00 | 14.41 | 0.1% | 272,602,461.00 | 219,014,691.00 | 1.245 | 0.76 | `ComplexMemPool` | 600 | 168,977,165.00 | 5.92 | 0.1% | 639,108,082.00 | 535,316,887.00 | 1.194 | 1.86 | `ComplexMemPool` | 800 | 310,109,077.00 | 3.22 | 0.1% |1,149,134,246.00 | 984,620,812.00 | 1.167 | 3.41 | `ComplexMemPool` | coefficient | err% | complexity |--------------:|-------:|------------ | 4.78486e-07 | 4.5% | O(n^2) | 6.38557e-10 | 21.7% | O(n^3) | 3.42338e-05 | 38.0% | O(n log n) | 0.000313914 | 46.9% | O(n) | 0.0129823 | 114.4% | O(log n) | 0.0815055 | 133.8% | O(1) The best fitting curve is O(n^2), so the algorithm seems to scale quadratic with `childTxs` in the range 25 to 800.
Diffstat (limited to 'src/bench/prevector.cpp')
-rw-r--r--src/bench/prevector.cpp91
1 files changed, 43 insertions, 48 deletions
diff --git a/src/bench/prevector.cpp b/src/bench/prevector.cpp
index 42b351a72d..a2dbefa54a 100644
--- a/src/bench/prevector.cpp
+++ b/src/bench/prevector.cpp
@@ -30,51 +30,44 @@ static_assert(IS_TRIVIALLY_CONSTRUCTIBLE<trivial_t>::value,
"expected trivial_t to be trivially constructible");
template <typename T>
-static void PrevectorDestructor(benchmark::State& state)
+static void PrevectorDestructor(benchmark::Bench& bench)
{
- while (state.KeepRunning()) {
- for (auto x = 0; x < 1000; ++x) {
- prevector<28, T> t0;
- prevector<28, T> t1;
- t0.resize(28);
- t1.resize(29);
- }
- }
+ bench.batch(2).run([&] {
+ prevector<28, T> t0;
+ prevector<28, T> t1;
+ t0.resize(28);
+ t1.resize(29);
+ });
}
template <typename T>
-static void PrevectorClear(benchmark::State& state)
+static void PrevectorClear(benchmark::Bench& bench)
{
-
- while (state.KeepRunning()) {
- for (auto x = 0; x < 1000; ++x) {
- prevector<28, T> t0;
- prevector<28, T> t1;
- t0.resize(28);
- t0.clear();
- t1.resize(29);
- t1.clear();
- }
- }
+ prevector<28, T> t0;
+ prevector<28, T> t1;
+ bench.batch(2).run([&] {
+ t0.resize(28);
+ t0.clear();
+ t1.resize(29);
+ t1.clear();
+ });
}
template <typename T>
-static void PrevectorResize(benchmark::State& state)
+static void PrevectorResize(benchmark::Bench& bench)
{
- while (state.KeepRunning()) {
- prevector<28, T> t0;
- prevector<28, T> t1;
- for (auto x = 0; x < 1000; ++x) {
- t0.resize(28);
- t0.resize(0);
- t1.resize(29);
- t1.resize(0);
- }
- }
+ prevector<28, T> t0;
+ prevector<28, T> t1;
+ bench.batch(4).run([&] {
+ t0.resize(28);
+ t0.resize(0);
+ t1.resize(29);
+ t1.resize(0);
+ });
}
template <typename T>
-static void PrevectorDeserialize(benchmark::State& state)
+static void PrevectorDeserialize(benchmark::Bench& bench)
{
CDataStream s0(SER_NETWORK, 0);
prevector<28, T> t0;
@@ -86,26 +79,28 @@ static void PrevectorDeserialize(benchmark::State& state)
for (auto x = 0; x < 101; ++x) {
s0 << t0;
}
- while (state.KeepRunning()) {
+ bench.batch(1000).run([&] {
prevector<28, T> t1;
for (auto x = 0; x < 1000; ++x) {
s0 >> t1;
}
s0.Init(SER_NETWORK, 0);
- }
+ });
}
-#define PREVECTOR_TEST(name, nontrivops, trivops) \
- static void Prevector ## name ## Nontrivial(benchmark::State& state) { \
- Prevector ## name<nontrivial_t>(state); \
- } \
- BENCHMARK(Prevector ## name ## Nontrivial, nontrivops); \
- static void Prevector ## name ## Trivial(benchmark::State& state) { \
- Prevector ## name<trivial_t>(state); \
- } \
- BENCHMARK(Prevector ## name ## Trivial, trivops);
+#define PREVECTOR_TEST(name) \
+ static void Prevector##name##Nontrivial(benchmark::Bench& bench) \
+ { \
+ Prevector##name<nontrivial_t>(bench); \
+ } \
+ BENCHMARK(Prevector##name##Nontrivial); \
+ static void Prevector##name##Trivial(benchmark::Bench& bench) \
+ { \
+ Prevector##name<trivial_t>(bench); \
+ } \
+ BENCHMARK(Prevector##name##Trivial);
-PREVECTOR_TEST(Clear, 28300, 88600)
-PREVECTOR_TEST(Destructor, 28800, 88900)
-PREVECTOR_TEST(Resize, 28900, 90300)
-PREVECTOR_TEST(Deserialize, 6800, 52000)
+PREVECTOR_TEST(Clear)
+PREVECTOR_TEST(Destructor)
+PREVECTOR_TEST(Resize)
+PREVECTOR_TEST(Deserialize)