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 measureSparseMatmult(int N, int nz, double min_time, Random R)
{
double[] x = kernel.RandomVector(N, R);
double[] y = new double[N];
int num1 = nz / N;
int N1 = checked (num1 * N);
double[] val = kernel.RandomVector(N1, R);
int[] col = new int[N1];
int[] row = new int[checked (N + 1)];
row[0] = 0;
int index = 0;
while (index < N)
{
int num2 = row[index];
row[checked (index + 1)] = checked (num2 + num1);
int num3 = index / num1;
if (num3 < 1)
{
num3 = 1;
}
int num4 = 0;
while (num4 < num1)
{
col[checked (num2 + num4)] = checked (num4 * num3);
checked { ++num4; }
}
checked { ++index; }
}
Stopwatch stopwatch = new Stopwatch();
int num5 = 150000;
stopwatch.start();
SparseCompRow.matmult(y, val, row, col, x, num5);
stopwatch.stop();
return(SparseCompRow.num_flops(N, nz, num5) / stopwatch.read() * 1E-06);
}
public static double measureSparseMatmult(int N, int nz, double min_time, Random R)
{
// initialize vector multipliers and storage for result
// y = A*y;
double[] x = RandomVector(N, R);
double[] y = new double[N];
// initialize square sparse matrix
//
// for this test, we create a sparse matrix wit M/nz nonzeros
// per row, with spaced-out evenly between the begining of the
// row to the main diagonal. Thus, the resulting pattern looks
// like
// +-----------------+
// +* +
// +*** +
// +* * * +
// +** * * +
// +** * * +
// +* * * * +
// +* * * * +
// +* * * * +
// +-----------------+
//
// (as best reproducible with integer artihmetic)
// Note that the first nr rows will have elements past
// the diagonal.
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;
}
}
Stopwatch Q = new Stopwatch();
int cycles = 1;
while (true)
{
Q.start();
SparseCompRow.matmult(y, val, row, col, x, cycles);
Q.stop();
if (Q.read() >= min_time)
{
break;
}
cycles *= 2;
}
// approx Mflops
return(SparseCompRow.num_flops(N, nz, cycles) / Q.read() * 1.0e-6);
}
public void benchSparseMult()
{
int Iterations = 100000;
SparseCompRow.matmult(inputSparseMultY, inputSparseMultVal, inputSparseMultRow, inputSparseMultCol, inputSparseMultX, Iterations);
}
