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author | Martin Ankerl <martin.ankerl@gmail.com> | 2020-06-13 09:37:27 +0200 |
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committer | Martin Ankerl <martin.ankerl@gmail.com> | 2020-06-13 12:24:18 +0200 |
commit | 78c312c983255e15fc274de2368a2ec13ce81cbf (patch) | |
tree | 09c5cec9b0b3f7ef2aa9364057858861c134cf45 /src/bench/mempool_stress.cpp | |
parent | 19e919217e6d62e3640525e4149de1a4ae04e74f (diff) | |
download | bitcoin-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/mempool_stress.cpp')
-rw-r--r-- | src/bench/mempool_stress.cpp | 15 |
1 files changed, 10 insertions, 5 deletions
diff --git a/src/bench/mempool_stress.cpp b/src/bench/mempool_stress.cpp index 38d8632318..89233e390c 100644 --- a/src/bench/mempool_stress.cpp +++ b/src/bench/mempool_stress.cpp @@ -26,8 +26,13 @@ struct Available { Available(CTransactionRef& ref, size_t tx_count) : ref(ref), tx_count(tx_count){} }; -static void ComplexMemPool(benchmark::State& state) +static void ComplexMemPool(benchmark::Bench& bench) { + int childTxs = 800; + if (bench.complexityN() > 1) { + childTxs = static_cast<int>(bench.complexityN()); + } + FastRandomContext det_rand{true}; std::vector<Available> available_coins; std::vector<CTransactionRef> ordered_coins; @@ -46,7 +51,7 @@ static void ComplexMemPool(benchmark::State& state) ordered_coins.emplace_back(MakeTransactionRef(tx)); available_coins.emplace_back(ordered_coins.back(), tx_counter++); } - for (auto x = 0; x < 800 && !available_coins.empty(); ++x) { + for (auto x = 0; x < childTxs && !available_coins.empty(); ++x) { CMutableTransaction tx = CMutableTransaction(); size_t n_ancestors = det_rand.randrange(10)+1; for (size_t ancestor = 0; ancestor < n_ancestors && !available_coins.empty(); ++ancestor){ @@ -77,13 +82,13 @@ static void ComplexMemPool(benchmark::State& state) TestingSetup test_setup; CTxMemPool pool; LOCK2(cs_main, pool.cs); - while (state.KeepRunning()) { + bench.run([&]() NO_THREAD_SAFETY_ANALYSIS { for (auto& tx : ordered_coins) { AddTx(tx, pool); } pool.TrimToSize(pool.DynamicMemoryUsage() * 3 / 4); pool.TrimToSize(GetVirtualTransactionSize(*ordered_coins.front())); - } + }); } -BENCHMARK(ComplexMemPool, 1); +BENCHMARK(ComplexMemPool); |