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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/lockedpool.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/lockedpool.cpp')
-rw-r--r-- | src/bench/lockedpool.cpp | 31 |
1 files changed, 14 insertions, 17 deletions
diff --git a/src/bench/lockedpool.cpp b/src/bench/lockedpool.cpp index 5d943810df..32b060a15a 100644 --- a/src/bench/lockedpool.cpp +++ b/src/bench/lockedpool.cpp @@ -9,10 +9,9 @@ #include <vector> #define ASIZE 2048 -#define BITER 5000 #define MSIZE 2048 -static void BenchLockedPool(benchmark::State& state) +static void BenchLockedPool(benchmark::Bench& bench) { void *synth_base = reinterpret_cast<void*>(0x08000000); const size_t synth_size = 1024*1024; @@ -22,24 +21,22 @@ static void BenchLockedPool(benchmark::State& state) for (int x=0; x<ASIZE; ++x) addr.push_back(nullptr); uint32_t s = 0x12345678; - while (state.KeepRunning()) { - for (int x=0; x<BITER; ++x) { - int idx = s & (addr.size()-1); - if (s & 0x80000000) { - b.free(addr[idx]); - addr[idx] = nullptr; - } else if(!addr[idx]) { - addr[idx] = b.alloc((s >> 16) & (MSIZE-1)); - } - bool lsb = s & 1; - s >>= 1; - if (lsb) - s ^= 0xf00f00f0; // LFSR period 0xf7ffffe0 + bench.run([&] { + int idx = s & (addr.size() - 1); + if (s & 0x80000000) { + b.free(addr[idx]); + addr[idx] = nullptr; + } else if (!addr[idx]) { + addr[idx] = b.alloc((s >> 16) & (MSIZE - 1)); } - } + bool lsb = s & 1; + s >>= 1; + if (lsb) + s ^= 0xf00f00f0; // LFSR period 0xf7ffffe0 + }); for (void *ptr: addr) b.free(ptr); addr.clear(); } -BENCHMARK(BenchLockedPool, 1300); +BENCHMARK(BenchLockedPool); |