public void CheckExponentialPerformance()
{
IntelMathKernelLibrary.SetSequential();
var location = StorageLocation.Host;
using (var randomStream = new RandomNumberStream(location, RandomNumberGeneratorType.MRG32K3A, 111))
{
var normalDistribution = new Normal(randomStream, 0, 1);
var a = new NArray(location, 5000);
var b = new NArray(location, 5000);
a.FillRandom(normalDistribution);
var aArray = GetArray(a);
var bArray = GetArray(b);
var watch = new Stopwatch(); watch.Start();
for (int i = 0; i < 1000; ++i)
{
IntelMathKernelLibrary.Exp(aArray, 0, bArray, 0, 5000);
}
var baseline = watch.ElapsedMilliseconds;
Console.WriteLine("5 millions in place Exps");
Console.WriteLine(baseline);
}
}
public void TestRandomNumberGeneration()
{
var location = StorageLocation.Host;
// We create the stream.
// This identifies the stream and stores the state or pointer to the state (in the case
// of IntelMKL, CUDA, etc).
using (var randomStream = new RandomNumberStream(location, RandomNumberGeneratorType.MRG32K3A, 111))
{
// We then create a distribution based on the stream.
// This stores only specific parameters of the distribution (i.e. mean and standard deviation).
// The purpose of the design is to push scaling and offsetting of random numbers to the Provider,
// which can do this efficiently whilst generating.
var normal = new Normal(randomStream, 0, 1);
var a = new NArray(location, 1000);
var b = new NArray(location, 1000);
// When we call FillRandom, we need to use a Provider that is appropriate to the stream.
a.FillRandom(normal);
b.FillRandom(normal);
var v = a * b;
}
}
public void CheckExponentialPerformance()
{
IntelMathKernelLibrary.SetSequential();
var location = StorageLocation.Host;
using (var randomStream = new RandomNumberStream(location, RandomNumberGeneratorType.MRG32K3A, 111))
{
var normalDistribution = new Normal(randomStream, 0, 1);
var a = new NArray(location, 5000);
var b = new NArray(location, 5000);
a.FillRandom(normalDistribution);
var aArray = GetArray(a);
var bArray = GetArray(b);
var watch = new Stopwatch(); watch.Start();
for (int i = 0; i < 1000; ++i)
{
IntelMathKernelLibrary.Exp(aArray, 0, bArray, 0, 5000);
}
var baseline = watch.ElapsedMilliseconds;
Console.WriteLine("5 millions in place Exps");
Console.WriteLine(baseline);
}
}
public void TestRandomNumberGeneration()
{
var location = StorageLocation.Host;
// We create the stream.
// This identifies the stream and stores the state or pointer to the state (in the case
// of IntelMKL, CUDA, etc).
using (var randomStream = new RandomNumberStream(location, RandomNumberGeneratorType.MRG32K3A, 111))
{
// We then create a distribution based on the stream.
// This stores only specific parameters of the distribution (i.e. mean and standard deviation).
// The purpose of the design is to push scaling and offsetting of random numbers to the Provider,
// which can do this efficiently whilst generating.
var normal = new Normal(randomStream, 0, 1);
var a = new NArray(location, 1000);
var b = new NArray(location, 1000);
// When we call FillRandom, we need to use a Provider that is appropriate to the stream.
a.FillRandom(normal);
b.FillRandom(normal);
var v = a * b;
}
}
private NArray CalculateSyntheticReturns(NArray correlationMatrix, int returnsCount)
{
var location = StorageLocation.Host;
var cholesky = NMath.CholeskyDecomposition(correlationMatrix);
var variates = new NArray(location, returnsCount, correlationMatrix.RowCount);
using (var stream = new RandomNumberStream(location))
{
var normal = new Normal(stream, 0, 1);
variates.FillRandom(normal);
}
return(variates * cholesky.Transpose());
}
public void CreateInputs()
{
var location = StorageLocation.Host;
using (var randomStream = new RandomNumberStream(location, RandomNumberGeneratorType.MRG32K3A, 111))
{
var normalDistribution = new Normal(randomStream, 0, 1);
_a = new NArray(location, 5000);
_a.FillRandom(normalDistribution);
_a_array = GetArray(_a);
_b = new NArray(location, 5000);
_b.FillRandom(normalDistribution);
_b_array = GetArray(_b);
_c = new NArray(location, 5000);
_c.FillRandom(normalDistribution);
_c_array = GetArray(_c);
}
}
public void CreateInputs()
{
var location = StorageLocation.Host;
using (var randomStream = new RandomNumberStream(location, RandomNumberGeneratorType.MRG32K3A, 111))
{
var normalDistribution = new Normal(randomStream, 0, 1);
_a = new NArray(location, 5000);
_a.FillRandom(normalDistribution);
_a_array = GetArray(_a);
_b = new NArray(location, 5000);
_b.FillRandom(normalDistribution);
_b_array = GetArray(_b);
_c = new NArray(location, 5000);
_c.FillRandom(normalDistribution);
_c_array = GetArray(_c);
}
}
private NArray CalculateSyntheticReturns(NArray correlationMatrix, int returnsCount)
{
var location = StorageLocation.Host;
var cholesky = NMath.CholeskyDecomposition(correlationMatrix);
var variates = new NArray(location, returnsCount, correlationMatrix.RowCount);
using (var stream = new RandomNumberStream(location))
{
var normal = new Normal(stream, 0, 1);
variates.FillRandom(normal);
}
return variates * cholesky.Transpose();
}
public void TranscendentalFunctionTest()
{
IntelMathKernelLibrary.SetSequential();
var location = StorageLocation.Host;
using (var randomStream = new RandomNumberStream(location, RandomNumberGeneratorType.MRG32K3A, 111))
{
var normalDistribution = new Normal(randomStream, 0, 1);
var a = new NArray(location, 5000);
a.FillRandom(normalDistribution);
var watch = new Stopwatch(); watch.Start();
var options = new ParallelOptions()
{
MaxDegreeOfParallelism = 1
};
Parallel.For(0, 100, options, (i) =>
{
DoWork(a, normalDistribution);
});
var baseline = watch.ElapsedMilliseconds;
Console.WriteLine("Base-line");
Console.WriteLine(baseline);
watch.Restart();
Parallel.For(0, 100, (i) =>
{
DoWork(a, normalDistribution);
});
var baselineThreaded = watch.ElapsedMilliseconds;
Console.WriteLine("Base-line threaded");
Console.WriteLine(baselineThreaded);
watch.Restart();
Parallel.For(0, 100, options, (i) =>
{
DoWorkInPlace(a, normalDistribution);
});
var oneThread = watch.ElapsedMilliseconds;
Console.WriteLine("In place");
Console.WriteLine(oneThread);
watch.Restart();
Parallel.For(0, 100, (i) =>
{
DoWorkInPlace(a, normalDistribution);
});
var multipleThreads = watch.ElapsedMilliseconds;
Console.WriteLine("In place threaded");
Console.WriteLine(multipleThreads);
Console.WriteLine(oneThread / (double)multipleThreads);
watch.Restart();
Parallel.For(0, 100, options, (i) =>
{
DoWorkDeferred(a, normalDistribution);
});
var deferred = watch.ElapsedMilliseconds;
Console.WriteLine("Deferred");
Console.WriteLine(deferred);
watch.Restart();
Parallel.For(0, 100, (i) =>
{
DoWorkDeferred(a, normalDistribution);
});
var deferredMultipleThreads = watch.ElapsedMilliseconds;
Console.WriteLine("Deferred threaded");
Console.WriteLine(deferredMultipleThreads);
watch.Restart();
}
}
public void VectorFundamentalsTest()
{
var location = StorageLocation.Host;
using (var randomStream = new RandomNumberStream(location, RandomNumberGeneratorType.MRG32K3A, 111))
{
var normalDistribution = new Normal(randomStream, 0, 1);
var a = new NArray(StorageLocation.Host, 5000);
var b = new NArray(StorageLocation.Host, 5000);
var c = new NArray(StorageLocation.Host, 5000);
var d = new NArray(StorageLocation.Host, 5000);
var result = NArray.CreateLike(a);
a.FillRandom(normalDistribution);
b.FillRandom(normalDistribution);
c.FillRandom(normalDistribution);
d.FillRandom(normalDistribution);
var aArray = GetArray(a);
var bArray = GetArray(b);
var cArray = GetArray(c);
var dArray = GetArray(d);
var resultArray = GetArray(result);
Console.WriteLine(); Console.WriteLine("In place vector Exp MKL");
TestHelpers.Timeit(() =>
{
IntelMathKernelLibrary.Exp(aArray, 0, resultArray, 0, result.Length);
}, 20, 50);
Console.WriteLine(); Console.WriteLine("In place vector Exp C sharp");
TestHelpers.Timeit(() =>
{
for (int i = 0; i < 5000; ++i)
{
resultArray[i] = Math.Exp(aArray[i]);
}
}, 20, 50);
Console.WriteLine(); Console.WriteLine("In place vector aX + Y MKL");
TestHelpers.Timeit(() =>
{
IntelMathKernelLibrary.ConstantAddMultiply(aArray, 0, 3, 0, resultArray, 0, 5000);
//IntelMathKernelLibrary.Multiply(bArray, 0, resultArray, 0, resultArray, 0, result.Length);
IntelMathKernelLibrary.Add(bArray, 0, resultArray, 0, resultArray, 0, result.Length);
//IntelMathKernelLibrary.Exp(resultArray, 0, resultArray, 0, result.Length);
// 3 reads and 2 writes
}, 20, 50);
Console.WriteLine(); Console.WriteLine("In place vector aX + Y C sharp");
TestHelpers.Timeit(() =>
{
for (int i = 0; i < 5000; ++i)
{
resultArray[i] = 3 * aArray[i] + bArray[i]; // 2 reads and a write
//resultArray[i] = Math.Exp(3 * aArray[i] + bArray[i]); // 2 reads and a write
}
}, 20, 50);
Console.WriteLine(); Console.WriteLine("Immediate mode; creating storage");
TestHelpers.Timeit(() =>
{
//var result2 = NMath.Exp(3 * a + b);
var result2 = 3 * a + b;
}, 20, 50);
var result3 = NArray.CreateLike(a);
Console.WriteLine(); Console.WriteLine("Deferred mode; storage passed in");
TestHelpers.Timeit(() =>
{
NArray.Evaluate(() =>
{
//return NMath.Exp(3 * a + b);
return(3 * a + b);
}
, new List <NArray>(), Aggregator.ElementwiseAdd, new List <NArray> {
result3
});
}, 20, 50);
return;
Console.WriteLine(); Console.WriteLine("Deferred mode; storage passed in");
TestHelpers.Timeit(() =>
{
NArray.Evaluate(() =>
{
return(3 * a + b);
}
, new List <NArray>(), Aggregator.ElementwiseAdd, new List <NArray> {
result
});
}, 20, 50);
}
}