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Benchmark: bring vector-add benchmark back

spatial
Christoph Ruegg 9 years ago
parent
0aa3b551a4
commit
12be91e93c
2 changed files with 98 additions and 152 deletions
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  1. 249
      src/Benchmark/LinearAlgebra/DenseVectorAdd.cs
  2. 1
      src/Benchmark/Program.cs

249
src/Benchmark/LinearAlgebra/DenseVectorAdd.cs
View File

@ -1,156 +1,101 @@
//using System;
//using MathNet.Numerics;
//using MathNet.Numerics.LinearAlgebra;
//using MathNet.Numerics.LinearAlgebra.Storage;
//using MathNet.Numerics.Providers.LinearAlgebra;
//using MathNet.Numerics.Providers.LinearAlgebra.Mkl;
//using MathNet.Numerics.Threading;
using BenchmarkDotNet.Attributes;
using BenchmarkDotNet.Configs;
using BenchmarkDotNet.Environments;
using BenchmarkDotNet.Jobs;
using MathNet.Numerics;
using MathNet.Numerics.LinearAlgebra;
using MathNet.Numerics.Providers.Common.Mkl;
using MathNet.Numerics.Providers.LinearAlgebra;
namespace Benchmark.LinearAlgebra
{
//Benchmark(new LinearAlgebra.DenseVectorAdd(10000000,1), 10, "Large (10'000'000) - 10x1 iterations");
//Benchmark(new LinearAlgebra.DenseVectorAdd(100,1000), 100, "Small (100) - 100x1000 iterations");
// public class DenseVectorAdd
// {
// readonly int _rounds;
// readonly Vector<double> _a;
// readonly Vector<double> _b;
// readonly ILinearAlgebraProvider _managed = new ManagedLinearAlgebraProvider();
// readonly ILinearAlgebraProvider _mkl = new MklLinearAlgebraProvider();
// public DenseVectorAdd(int size, int rounds)
// {
// _rounds = rounds;
// _b = Vector<double>.Build.Random(size);
// _a = Vector<double>.Build.Random(size);
// _managed.InitializeVerify();
// Control.LinearAlgebraProvider = _managed;
//#if NATIVE
// _mkl.InitializeVerify();
//#endif
// }
// [BenchSharkTask("AddOperator")]
// public Vector<double> AddOperator()
// {
// var z = _b;
// for (int i = 0; i < _rounds; i++)
// {
// z = _a + z;
// }
// return z;
// }
// [BenchSharkTask("Map2")]
// public Vector<double> Map2()
// {
// var z = _b;
// for (int i = 0; i < _rounds; i++)
// {
// z = _a.Map2((u, v) => u + v, z);
// }
// return z;
// }
// [BenchSharkTask("Loop")]
// public Vector<double> Loop()
// {
// var z = _b;
// for (int i = 0; i < _rounds; i++)
// {
// var aa = ((DenseVectorStorage<double>)_a.Storage).Data;
// var az = ((DenseVectorStorage<double>)z.Storage).Data;
// var ar = new Double[aa.Length];
// for (int k = 0; k < ar.Length; k++)
// {
// ar[k] = aa[k] + az[k];
// }
// z = Vector<double>.Build.Dense(ar);
// }
// return z;
// }
// [BenchSharkTask("ParallelLoop4096")]
// public Vector<double> ParallelLoop4096()
// {
// var z = _b;
// for (int i = 0; i < _rounds; i++)
// {
// var aa = ((DenseVectorStorage<double>)_a.Storage).Data;
// var az = ((DenseVectorStorage<double>)z.Storage).Data;
// var ar = new Double[aa.Length];
// CommonParallel.For(0, ar.Length, 4096, (u, v) =>
// {
// for (int k = u; k < v; k++)
// {
// ar[k] = aa[k] + az[k];
// }
// });
// z = Vector<double>.Build.Dense(ar);
// }
// return z;
// }
// [BenchSharkTask("ParallelLoop32768")]
// public Vector<double> ParallelLoop32768()
// {
// var z = _b;
// for (int i = 0; i < _rounds; i++)
// {
// var aa = ((DenseVectorStorage<double>)_a.Storage).Data;
// var az = ((DenseVectorStorage<double>)z.Storage).Data;
// var ar = new Double[aa.Length];
// CommonParallel.For(0, ar.Length, 32768, (u, v) =>
// {
// for (int k = u; k < v; k++)
// {
// ar[k] = aa[k] + az[k];
// }
// });
// z = Vector<double>.Build.Dense(ar);
// }
// return z;
// }
// [BenchSharkTask("ManagedProvider")]
// public Vector<double> ManagedProvider()
// {
// var z = _b;
// for (int i = 0; i < _rounds; i++)
// {
// var aa = ((DenseVectorStorage<double>)_a.Storage).Data;
// var az = ((DenseVectorStorage<double>)z.Storage).Data;
// var ar = new Double[aa.Length];
// _managed.AddArrays(aa, az, ar);
// z = Vector<double>.Build.Dense(ar);
// }
// return z;
// }
//#if NATIVEMKL
// [BenchSharkTask("MklProvider")]
// public Vector<double> MklProvider()
// {
// var z = _b;
// for (int i = 0; i < _rounds; i++)
// {
// var aa = ((DenseVectorStorage<double>)_a.Storage).Data;
// var az = ((DenseVectorStorage<double>)z.Storage).Data;
// var ar = new Double[aa.Length];
// _mkl.AddArrays(aa, az, ar);
// z = Vector<double>.Build.Dense(ar);
// }
// return z;
// }
//#endif
// }
[Config(typeof(Config))]
public class DenseVectorAdd
{
class Config : ManualConfig
{
public Config()
{
Add(
new Job("CLR x64", RunMode.Default, EnvMode.RyuJitX64)
{
Env = { Runtime = Runtime.Clr, Platform = Platform.X64 }
},
new Job("CLR x86", RunMode.Default, EnvMode.LegacyJitX86)
{
Env = { Runtime = Runtime.Clr, Platform = Platform.X86 }
});
#if !NET461
Add(new Job("Core RyuJit x64", RunMode.Default, EnvMode.RyuJitX64)
{
Env = { Runtime = Runtime.Core, Platform = Platform.X64 }
});
#endif
}
}
public enum ProviderId
{
Managed,
NativeMKL,
}
[Params(4, 32, 128, 4096, 524288)]
public int N { get; set; }
[Params(ProviderId.Managed, ProviderId.NativeMKL)]
public ProviderId Provider { get; set; }
//const int Rounds = 1024;
double[] _a;
double[] _b;
Vector<double> _av;
Vector<double> _bv;
[GlobalSetup]
public void Setup()
{
switch (Provider)
{
case ProviderId.Managed:
Control.UseManaged();
break;
case ProviderId.NativeMKL:
Control.UseNativeMKL(MklConsistency.Auto, MklPrecision.Double, MklAccuracy.High);
break;
}
_a = Generate.Normal(N, 2.0, 10.0);
_b = Generate.Normal(N, 200.0, 10.0);
_av = Vector<double>.Build.Dense(_a);
_bv = Vector<double>.Build.Dense(_b);
}
[Benchmark(OperationsPerInvoke = 1, Baseline = true)]
public double[] ForLoop()
{
double[] r = new double[_a.Length];
for (int i = 0; i < r.Length; i++)
{
r[i] = _a[i] + _b[i];
}
return r;
}
[Benchmark(OperationsPerInvoke = 1)]
public double[] ProviderAddArrays()
{
double[] r = new double[_a.Length];
LinearAlgebraControl.Provider.AddArrays(_a, _b, r);
return r;
}
[Benchmark(OperationsPerInvoke = 1)]
public Vector<double> VectorAddOp()
{
return _av + _bv;
}
}
}

1
src/Benchmark/Program.cs
View File

@ -15,6 +15,7 @@ namespace Benchmark
{
typeof(Transforms.FFT),
typeof(LinearAlgebra.DenseMatrixProduct),
typeof(LinearAlgebra.DenseVectorAdd),
});
switcher.Run(args);

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