public static void benchmarkLU()
{
int N = Constants.LU_SIZE;
SciMark2.Random R = new SciMark2.Random(Constants.RANDOM_SEED);
int Iterations = 2000;
double[][] A = RandomMatrix(N, N, R);
double[][] lu = new double[N][];
for (int i = 0; i < N; i++)
{
lu[i] = new double[N];
}
int[] pivot = new int[N];
foreach (var iteration in Benchmark.Iterations)
{
using (iteration.StartMeasurement())
{
for (int i = 0; i < Iterations; i++)
{
CopyMatrix(lu, A);
LU.factor(lu, pivot);
}
}
}
validateLU(N, R, lu, A, pivot);
}
public void SetupSOR()
{
Random R = new SciMark2.Random(Constants.RANDOM_SEED);
int N = Constants.SOR_SIZE;
inputSOR = RandomMatrix(N, N, R);
}
public void SetupFFT()
{
Random R = new SciMark2.Random(Constants.RANDOM_SEED);
int N = Constants.FFT_SIZE;
inputFFT = RandomVector(2 * N, R);
}
public static void benchMonteCarlo()
{
SciMark2.Random R = new SciMark2.Random(Constants.RANDOM_SEED);
int Iterations = 40000000;
foreach (var iteration in Benchmark.Iterations)
{
using (iteration.StartMeasurement())
{
MonteCarlo.integrate(Iterations);
}
}
}
public void SetupLU()
{
Random R = new SciMark2.Random(Constants.RANDOM_SEED);
int N = Constants.LU_SIZE;
inputLUA = RandomMatrix(N, N, R);
inputLU = new double[N][];
for (int i = 0; i < N; i++)
{
inputLU[i] = new double[N];
}
inputLUPivot = new int[N];
}
public static void benchSOR()
{
int N = Constants.SOR_SIZE;
SciMark2.Random R = new SciMark2.Random(Constants.RANDOM_SEED);
int Iterations = 20000;
double[][] G = RandomMatrix(N, N, R);
foreach (var iteration in Benchmark.Iterations)
{
using (iteration.StartMeasurement())
{
SOR.execute(1.25, G, Iterations);
}
}
}
public static void validateLU(int N, SciMark2.Random R, double[][] lu, double[][] A, int[] pivot)
{
// verify that LU is correct
double[] b = RandomVector(N, R);
double[] x = NewVectorCopy(b);
LU.solve(lu, pivot, x);
const double EPS = 1.0e-12;
if (normabs(b, matvec(A, x)) / N > EPS)
{
throw new Exception("LU failed to validate");
}
}
public static void benchFFT()
{
SciMark2.Random R = new SciMark2.Random(Constants.RANDOM_SEED);
int N = Constants.FFT_SIZE;
long Iterations = 20000;
double[] x = RandomVector(2 * N, R);
foreach (var iteration in Benchmark.Iterations)
{
using (iteration.StartMeasurement())
{
innerFFT(x, Iterations);
}
}
validateFFT(N, x);
}
public static void benchFFT()
{
SciMark2.Random R = new SciMark2.Random(Constants.RANDOM_SEED);
int N = Constants.FFT_SIZE;
long Iterations = 20000;
double[] x = RandomVector(2 * N, R);
foreach (var iteration in Benchmark.Iterations)
{
using (iteration.StartMeasurement())
{
innerFFT(x, Iterations);
}
}
validateFFT(N, x);
}
public static double integrate(int Num_samples)
{
SciMark2.Random R = new SciMark2.Random(SEED);
int under_curve = 0;
for (int count = 0; count < Num_samples; count++)
{
double x = R.nextDouble();
double y = R.nextDouble();
if (x * x + y * y <= 1.0)
under_curve++;
}
return ((double)under_curve / Num_samples) * 4.0;
}
public static void benchSOR()
{
int N = Constants.SOR_SIZE;
SciMark2.Random R = new SciMark2.Random(Constants.RANDOM_SEED);
int Iterations = 20000;
double[][] G = RandomMatrix(N, N, R);
foreach (var iteration in Benchmark.Iterations)
{
using (iteration.StartMeasurement())
{
SOR.execute(1.25, G, Iterations);
}
}
}
public static void benchSparseMult()
{
int N = Constants.SPARSE_SIZE_M;
int nz = Constants.SPARSE_SIZE_nz;
int Iterations = 100000;
SciMark2.Random R = new SciMark2.Random(Constants.RANDOM_SEED);
double[] x = RandomVector(N, R);
double[] y = new double[N];
int nr = nz / N; // average number of nonzeros per row
int anz = nr * N; // _actual_ number of nonzeros
double[] val = RandomVector(anz, R);
int[] col = new int[anz];
int[] row = new int[N + 1];
row[0] = 0;
for (int r = 0; r < N; r++)
{
// initialize elements for row r
int rowr = row[r];
row[r + 1] = rowr + nr;
int step = r / nr;
if (step < 1)
{
step = 1;
}
// take at least unit steps
for (int i = 0; i < nr; i++)
{
col[rowr + i] = i * step;
}
}
foreach (var iteration in Benchmark.Iterations)
{
using (iteration.StartMeasurement())
{
SparseCompRow.matmult(y, val, row, col, x, Iterations);
}
}
}
public static double integrate(int Num_samples)
{
SciMark2.Random R = new SciMark2.Random(SEED);
int under_curve = 0;
for (int count = 0; count < Num_samples; count++)
{
double x = R.nextDouble();
double y = R.nextDouble();
if (x * x + y * y <= 1.0)
{
under_curve++;
}
}
return(((double)under_curve / Num_samples) * 4.0);
}
public void SetupSparseMult()
{
Random R = new SciMark2.Random(Constants.RANDOM_SEED);
int N = Constants.SPARSE_SIZE_M;
int nz = Constants.SPARSE_SIZE_nz;
inputSparseMultX = RandomVector(N, R);
inputSparseMultY = new double[N];
int nr = nz / N; // average number of nonzeros per row
int anz = nr * N; // _actual_ number of nonzeros
inputSparseMultVal = RandomVector(anz, R);
inputSparseMultCol = new int[anz];
inputSparseMultRow = new int[N + 1];
inputSparseMultRow[0] = 0;
for (int r = 0; r < N; r++)
{
// initialize elements for row r
int rowr = inputSparseMultRow[r];
inputSparseMultRow[r + 1] = rowr + nr;
int step = r / nr;
if (step < 1)
{
step = 1;
}
// take at least unit steps
for (int i = 0; i < nr; i++)
{
inputSparseMultCol[rowr + i] = i * step;
}
}
}
/// <summary>
/// Benchmark 5 kernels with individual Mflops.
/// "results[0]" has the average Mflop rate.
/// </summary>
public static int Main(System.String[] args)
{
#if DEBUG
double min_time = Constants.RESOLUTION_TINY;
#else
double min_time = Constants.RESOLUTION_DEFAULT;
#endif
int FFT_size = Constants.FFT_SIZE;
int SOR_size = Constants.SOR_SIZE;
int Sparse_size_M = Constants.SPARSE_SIZE_M;
int Sparse_size_nz = Constants.SPARSE_SIZE_nz;
int LU_size = Constants.LU_SIZE;
// look for runtime options
if (args.Length > 0)
{
if (args[0].ToUpper().Equals("-h") ||
args[0].ToUpper().Equals("-help"))
{
Console.WriteLine("Usage: [-large] [iterations]");
return -1;
}
int current_arg = 0;
if (args[current_arg].ToUpper().Equals("-LARGE"))
{
FFT_size = Constants.LG_FFT_SIZE;
SOR_size = Constants.LG_SOR_SIZE;
Sparse_size_M = Constants.LG_SPARSE_SIZE_M;
Sparse_size_nz = Constants.LG_SPARSE_SIZE_nz;
LU_size = Constants.LG_LU_SIZE;
current_arg++;
}
if (args.Length > current_arg)
min_time = Double.Parse(args[current_arg]);
}
Console.WriteLine("** **");
Console.WriteLine("** SciMark2a Numeric Benchmark, see http://math.nist.gov/scimark **");
Console.WriteLine("** **");
// run the benchmark
double[] res = new double[6];
SciMark2.Random R = new SciMark2.Random(Constants.RANDOM_SEED);
Console.WriteLine("Mininum running time = {0} seconds", min_time);
res[1] = kernel.measureFFT(FFT_size, min_time, R);
res[2] = kernel.measureSOR(SOR_size, min_time, R);
res[3] = kernel.measureMonteCarlo(min_time, R);
res[4] = kernel.measureSparseMatmult(Sparse_size_M, Sparse_size_nz, min_time, R);
res[5] = kernel.measureLU(LU_size, min_time, R);
res[0] = (res[1] + res[2] + res[3] + res[4] + res[5]) / 5;
// print out results
Console.WriteLine();
Console.WriteLine("Composite Score: {0:F2} MFlops", res[0]);
Console.WriteLine("FFT : {0} - ({1})", res[1] == 0.0 ? "ERROR, INVALID NUMERICAL RESULT!" : res[1].ToString("F2"), FFT_size);
Console.WriteLine("SOR : {1:F2} - ({0}x{0})", SOR_size, res[2]);
Console.WriteLine("Monte Carlo : {0:F2}", res[3]);
Console.WriteLine("Sparse MatMult : {2:F2} - (N={0}, nz={1})", Sparse_size_M, Sparse_size_nz, res[4]);
Console.WriteLine("LU : {1} - ({0}x{0})", LU_size, res[1] == 0.0 ? "ERROR, INVALID NUMERICAL RESULT!" : res[5].ToString("F2"));
return 100;
}
public static void benchMonteCarlo()
{
SciMark2.Random R = new SciMark2.Random(Constants.RANDOM_SEED);
int Iterations = 40000000;
foreach (var iteration in Benchmark.Iterations)
{
using (iteration.StartMeasurement())
{
MonteCarlo.integrate(Iterations);
}
}
}
public static void benchSparseMult()
{
int N = Constants.SPARSE_SIZE_M;
int nz = Constants.SPARSE_SIZE_nz;
int Iterations = 100000;
SciMark2.Random R = new SciMark2.Random(Constants.RANDOM_SEED);
double[] x = RandomVector(N, R);
double[] y = new double[N];
int nr = nz / N; // average number of nonzeros per row
int anz = nr * N; // _actual_ number of nonzeros
double[] val = RandomVector(anz, R);
int[] col = new int[anz];
int[] row = new int[N + 1];
row[0] = 0;
for (int r = 0; r < N; r++)
{
// initialize elements for row r
int rowr = row[r];
row[r + 1] = rowr + nr;
int step = r / nr;
if (step < 1)
step = 1;
// take at least unit steps
for (int i = 0; i < nr; i++)
col[rowr + i] = i * step;
}
foreach (var iteration in Benchmark.Iterations)
{
using (iteration.StartMeasurement())
{
SparseCompRow.matmult(y, val, row, col, x, Iterations);
}
}
}
public static void benchmarkLU()
{
int N = Constants.LU_SIZE;
SciMark2.Random R = new SciMark2.Random(Constants.RANDOM_SEED);
int Iterations = 2000;
double[][] A = RandomMatrix(N, N, R);
double[][] lu = new double[N][];
for (int i = 0; i < N; i++)
{
lu[i] = new double[N];
}
int[] pivot = new int[N];
foreach (var iteration in Benchmark.Iterations)
{
using (iteration.StartMeasurement())
{
for (int i = 0; i < Iterations; i++)
{
CopyMatrix(lu, A);
LU.factor(lu, pivot);
}
}
}
validateLU(N, R, lu, A, pivot);
}
/// <summary>
/// Benchmark 5 kernels with individual Mflops.
/// "results[0]" has the average Mflop rate.
/// </summary>
public static int Main(System.String[] args)
{
#if DEBUG
double min_time = Constants.RESOLUTION_TINY;
#else
double min_time = Constants.RESOLUTION_DEFAULT;
#endif
int FFT_size = Constants.FFT_SIZE;
int SOR_size = Constants.SOR_SIZE;
int Sparse_size_M = Constants.SPARSE_SIZE_M;
int Sparse_size_nz = Constants.SPARSE_SIZE_nz;
int LU_size = Constants.LU_SIZE;
// look for runtime options
if (args.Length > 0)
{
if (args[0].ToUpper().Equals("-h") ||
args[0].ToUpper().Equals("-help"))
{
Console.WriteLine("Usage: [-large] [iterations]");
return(-1);
}
int current_arg = 0;
if (args[current_arg].ToUpper().Equals("-LARGE"))
{
FFT_size = Constants.LG_FFT_SIZE;
SOR_size = Constants.LG_SOR_SIZE;
Sparse_size_M = Constants.LG_SPARSE_SIZE_M;
Sparse_size_nz = Constants.LG_SPARSE_SIZE_nz;
LU_size = Constants.LG_LU_SIZE;
current_arg++;
}
if (args.Length > current_arg)
{
min_time = Double.Parse(args[current_arg]);
}
}
Console.WriteLine("** **");
Console.WriteLine("** SciMark2a Numeric Benchmark, see http://math.nist.gov/scimark **");
Console.WriteLine("** **");
// run the benchmark
double[] res = new double[6];
SciMark2.Random R = new SciMark2.Random(Constants.RANDOM_SEED);
Console.WriteLine("Mininum running time = {0} seconds", min_time);
res[1] = kernel.measureFFT(FFT_size, min_time, R);
res[2] = kernel.measureSOR(SOR_size, min_time, R);
res[3] = kernel.measureMonteCarlo(min_time, R);
res[4] = kernel.measureSparseMatmult(Sparse_size_M, Sparse_size_nz, min_time, R);
res[5] = kernel.measureLU(LU_size, min_time, R);
res[0] = (res[1] + res[2] + res[3] + res[4] + res[5]) / 5;
// print out results
Console.WriteLine();
Console.WriteLine("Composite Score: {0:F2} MFlops", res[0]);
Console.WriteLine("FFT : {0} - ({1})", res[1] == 0.0 ? "ERROR, INVALID NUMERICAL RESULT!" : res[1].ToString("F2"), FFT_size);
Console.WriteLine("SOR : {1:F2} - ({0}x{0})", SOR_size, res[2]);
Console.WriteLine("Monte Carlo : {0:F2}", res[3]);
Console.WriteLine("Sparse MatMult : {2:F2} - (N={0}, nz={1})", Sparse_size_M, Sparse_size_nz, res[4]);
Console.WriteLine("LU : {1} - ({0}x{0})", LU_size, res[1] == 0.0 ? "ERROR, INVALID NUMERICAL RESULT!" : res[5].ToString("F2"));
return(100);
}