void magma_saxpy(
magma_int_t n,
float alpha,
const float *dx, magma_int_t incx,
float *dy, magma_int_t incy )
{
cublasSaxpy( n, alpha, dx, incx, dy, incy );
}
void cublas_codelet_func_7(void *descr[], void *arg)
{
struct cg_problem *pb = arg;
float *vecr, *vecq;
uint32_t size;
/* get the vector */
vecr = (float *)STARPU_VECTOR_GET_PTR(descr[0]);
vecq = (float *)STARPU_VECTOR_GET_PTR(descr[1]);
size = STARPU_VECTOR_GET_NX(descr[0]);
cublasSaxpy (size, -pb->alpha, vecq, 1, vecr, 1);
}
CAMLprim value spoc_cublasSaxpy (value n, value alpha, value x, value incx, value y, value incy, value dev){
CAMLparam5(n,alpha, x,incx, y);
CAMLxparam2(incy, dev);
CAMLlocal3(dev_vec_array, dev_vec, gi);
int id;
CUdeviceptr d_A;
CUdeviceptr d_B;
GET_VEC(x, d_A);
GET_VEC(y, d_B);
CUBLAS_GET_CONTEXT;
cublasSaxpy(Int_val(n), (float)(Double_val(alpha)), (float*)d_A, Int_val(incx), (float*)d_B, Int_val(incy));
CUBLAS_CHECK_CALL(cublasGetError());
CUDA_RESTORE_CONTEXT;
CAMLreturn(Val_unit);
}
void reshape_layer_updater_cuda::enqueue_backward_data_propagation(
cudaStream_t stream_id,
unsigned int input_index,
cuda_linear_buffer_device::ptr input_errors_buffer,
cuda_linear_buffer_device::const_ptr output_errors_buffer,
const std::vector<cuda_linear_buffer_device::const_ptr>& schema_data,
const std::vector<cuda_linear_buffer_device::const_ptr>& data,
const std::vector<cuda_linear_buffer_device::const_ptr>& data_custom,
const std::vector<cuda_linear_buffer_device::const_ptr>& input_neurons_buffers,
cuda_linear_buffer_device::const_ptr output_neurons_buffer,
const std::vector<cuda_linear_buffer_device::const_ptr>& persistent_working_data,
cuda_linear_buffer_device::ptr temporary_working_fixed_buffer,
cuda_linear_buffer_device::ptr temporary_working_per_entry_buffer,
cuda_linear_buffer_device::const_ptr temporary_fixed_buffer,
cuda_linear_buffer_device::const_ptr temporary_per_entry_buffer,
bool add_update_to_destination,
unsigned int entry_count)
{
unsigned int elem_count = entry_count * output_elem_count_per_entry;
if (add_update_to_destination)
{
cublas_safe_call(cublasSetStream(cuda_config->get_cublas_handle(), stream_id));
float alpha = 1.0F;
cublas_safe_call(cublasSaxpy(
cuda_config->get_cublas_handle(),
elem_count,
&alpha,
*output_errors_buffer,
1,
*input_errors_buffer,
1));
}
else
{
if ((const float *)(*input_errors_buffer) != (const float *)(*output_errors_buffer))
{
cuda_util::copy_buffer(
*cuda_config,
*output_errors_buffer,
*input_errors_buffer,
output_elem_count_per_entry * entry_count,
stream_id);
}
}
}
void cublas_codelet_func_9(void *descr[], void *arg)
{
struct cg_problem *pb = arg;
float *vecd, *vecr;
uint32_t size;
/* get the vector */
vecd = (float *)STARPU_VECTOR_GET_PTR(descr[0]);
vecr = (float *)STARPU_VECTOR_GET_PTR(descr[1]);
size = STARPU_VECTOR_GET_NX(descr[0]);
/* d = beta d */
cublasSscal(size, pb->beta, vecd, 1);
/* d = r + d */
cublasSaxpy (size, 1.0f, vecr, 1, vecd, 1);
}
static vl::Error
axpy(vl::Context & context,
ptrdiff_t n,
type alpha,
type const *x, ptrdiff_t incx,
type *y, ptrdiff_t incy)
{
cublasHandle_t handle ;
cublasStatus_t status ;
status = context.getCudaHelper().getCublasHandle(&handle) ;
if (status != CUBLAS_STATUS_SUCCESS) goto done ;
status = cublasSaxpy(handle,
(int)n,
&alpha,
x, (int)incx,
y, (int)incy) ;
done:
return context.setError
(context.getCudaHelper().catchCublasError(status, "cublasSaxpy"), __func__) ;
}
static void cuda_saxpy(long size, float* y, float alpha, const float* src)
{
// printf("SAXPY %x %x %ld\n", y, src, size);
cublasSaxpy(size, alpha, src, 1, y, 1);
}
void caffe_gpu_axpy<float>(const int N, const float alpha, const float* X,
float* Y) {
CUBLAS_CHECK(cublasSaxpy(Caffe::cublas_handle(), N, &alpha, X, 1, Y, 1));
}
cublasStatus_t cublasXaxpy(int n, const float* alpha, const float* x, int incx, float* y, int incy) {
return cublasSaxpy(g_context->cublasHandle, n, alpha, x, incx, y, incy);
}
int
main()
{
#define N 8
int i;
float x_ref[N], y_ref[N];
float x[N], y[N];
cublasHandle_t h;
float a = 2.0;
for (i = 0; i < N; i++)
{
x[i] = x_ref[i] = 4.0 + i;
y[i] = y_ref[i] = 3.0;
}
saxpy (N, a, x_ref, y_ref);
cublasCreate (&h);
#pragma acc data copyin (x[0:N]) copy (y[0:N])
{
#pragma acc host_data use_device (x, y)
{
cublasSaxpy (h, N, &a, x, 1, y, 1);
}
}
validate_results (N, y, y_ref);
#pragma acc data create (x[0:N]) copyout (y[0:N])
{
#pragma acc kernels
for (i = 0; i < N; i++)
y[i] = 3.0;
#pragma acc host_data use_device (x, y)
{
cublasSaxpy (h, N, &a, x, 1, y, 1);
}
}
cublasDestroy (h);
validate_results (N, y, y_ref);
for (i = 0; i < N; i++)
y[i] = 3.0;
/* There's no need to use host_data here. */
#pragma acc data copyin (x[0:N]) copyin (a) copy (y[0:N])
{
#pragma acc parallel present (x[0:N]) pcopy (y[0:N]) present (a)
saxpy (N, a, x, y);
}
validate_results (N, y, y_ref);
/* Exercise host_data with data transferred with acc enter data. */
for (i = 0; i < N; i++)
y[i] = 3.0;
#pragma acc enter data copyin (x, a, y)
#pragma acc parallel present (x[0:N]) pcopy (y[0:N]) present (a)
{
saxpy (N, a, x, y);
}
#pragma acc exit data delete (x, a) copyout (y)
validate_results (N, y, y_ref);
return 0;
}
//
// Overloaded function for dispatching to
// * CUBLAS backend, and
// * float value-type.
//
inline void axpy( const int n, const float a, const float* x, const int incx,
float* y, const int incy ) {
cublasSaxpy( n, a, x, incx, y, incy );
}
void CQuadraticPath::cudaSolver(float* A, int* rowindex, int* columns,int N,int nz,float*Bx, float*X)
{
const int max_iter = 10000;
const float tol = 1e-12f;
float r0, r1, alpha, beta;
int *d_col, *d_row;
float *d_val, *d_x;
float *d_r, *d_p, *d_omega;
const float floatone = 1.0;
const float floatzero = 0.0;
float dot, nalpha;
/* Create CUBLAS context */
cublasHandle_t cublasHandle = 0;
cublasStatus_t cublasStatus;
cublasStatus = cublasCreate(&cublasHandle);
checkCudaErrors(cublasStatus);
/* Create CUSPARSE context */
cusparseHandle_t cusparseHandle = 0;
cusparseStatus_t cusparseStatus;
cusparseStatus = cusparseCreate(&cusparseHandle);
checkCudaErrors(cusparseStatus);
/* Description of the A matrix*/
cusparseMatDescr_t descr = 0;
cusparseStatus = cusparseCreateMatDescr(&descr);
checkCudaErrors(cusparseStatus);
/* Define the properties of the matrix */
cusparseSetMatType(descr,CUSPARSE_MATRIX_TYPE_GENERAL);
cusparseSetMatIndexBase(descr,CUSPARSE_INDEX_BASE_ZERO);
/* Allocate required memory */
checkCudaErrors(cudaMalloc((void **)&d_col, nz*sizeof(int)));
checkCudaErrors(cudaMalloc((void **)&d_row, (N+1)*sizeof(int)));
checkCudaErrors(cudaMalloc((void **)&d_val, nz*sizeof(float)));
checkCudaErrors(cudaMalloc((void **)&d_x, N*sizeof(float)));
checkCudaErrors(cudaMalloc((void **)&d_r, N*sizeof(float)));
checkCudaErrors(cudaMalloc((void **)&d_p, N*sizeof(float)));
checkCudaErrors(cudaMalloc((void **)&d_omega, N*sizeof(float)));
cudaMemcpy(d_col, columns, nz*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_row, rowindex, (N+1)*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_val, A, nz*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_x, X, N*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_r, Bx, N*sizeof(float), cudaMemcpyHostToDevice);
/* Conjugate gradient without preconditioning.
------------------------------------------
Follows the description by Golub & Van Loan, "Matrix Computations 3rd ed.", Section 10.2.6 */
int k = 0;
r0 = 0;
cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1);
while (r1 > tol*tol && k <= max_iter)
{
k++;
if (k == 1)
{
cublasScopy(cublasHandle, N, d_r, 1, d_p, 1);
}
else
{
beta = r1/r0;
cublasSscal(cublasHandle, N, &beta, d_p, 1);
cublasSaxpy(cublasHandle, N, &floatone, d_r, 1, d_p, 1) ;
}
cusparseScsrmv(cusparseHandle,CUSPARSE_OPERATION_NON_TRANSPOSE, N, N, nz, &floatone, descr, d_val, d_row, d_col, d_p, &floatzero, d_omega);
cublasSdot(cublasHandle, N, d_p, 1, d_omega, 1, &dot);
alpha = r1/dot;
cublasSaxpy(cublasHandle, N, &alpha, d_p, 1, d_x, 1);
nalpha = -alpha;
cublasSaxpy(cublasHandle, N, &nalpha, d_omega, 1, d_r, 1);
r0 = r1;
cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1);
}
cudaMemcpy(X, d_x, N*sizeof(float), cudaMemcpyDeviceToHost);
cudaFree(d_col);
cudaFree(d_row);
cudaFree(d_val);
cudaFree(d_x);
cudaFree(d_r);
cudaFree(d_p);
cudaFree(d_omega);
}
int
main (int argc, char **argv)
{
cublasStatus_t s;
cublasHandle_t h;
CUcontext pctx;
CUresult r;
int i;
const int N = 256;
float *h_X, *h_Y1, *h_Y2;
float *d_X,*d_Y;
float alpha = 2.0f;
float error_norm;
float ref_norm;
/* Test 4 - OpenACC creates, cuBLAS shares. */
acc_set_device_num (0, acc_device_nvidia);
r = cuCtxGetCurrent (&pctx);
if (r != CUDA_SUCCESS)
{
fprintf (stderr, "cuCtxGetCurrent failed: %d\n", r);
exit (EXIT_FAILURE);
}
h_X = (float *) malloc (N * sizeof (float));
if (h_X == 0)
{
fprintf (stderr, "malloc failed: for h_X\n");
exit (EXIT_FAILURE);
}
h_Y1 = (float *) malloc (N * sizeof (float));
if (h_Y1 == 0)
{
fprintf (stderr, "malloc failed: for h_Y1\n");
exit (EXIT_FAILURE);
}
h_Y2 = (float *) malloc (N * sizeof (float));
if (h_Y2 == 0)
{
fprintf (stderr, "malloc failed: for h_Y2\n");
exit (EXIT_FAILURE);
}
for (i = 0; i < N; i++)
{
h_X[i] = rand () / (float) RAND_MAX;
h_Y2[i] = h_Y1[i] = rand () / (float) RAND_MAX;
}
#pragma acc parallel copyin (h_X[0:N]), copy (h_Y2[0:N]) copy (alpha)
{
int i;
for (i = 0; i < N; i++)
{
h_Y2[i] = alpha * h_X[i] + h_Y2[i];
}
}
r = cuCtxGetCurrent (&pctx);
if (r != CUDA_SUCCESS)
{
fprintf (stderr, "cuCtxGetCurrent failed: %d\n", r);
exit (EXIT_FAILURE);
}
d_X = (float *) acc_copyin (&h_X[0], N * sizeof (float));
if (d_X == NULL)
{
fprintf (stderr, "copyin error h_Y1\n");
exit (EXIT_FAILURE);
}
d_Y = (float *) acc_copyin (&h_Y1[0], N * sizeof (float));
if (d_Y == NULL)
{
fprintf (stderr, "copyin error h_Y1\n");
exit (EXIT_FAILURE);
}
s = cublasCreate (&h);
if (s != CUBLAS_STATUS_SUCCESS)
{
fprintf (stderr, "cublasCreate failed: %d\n", s);
exit (EXIT_FAILURE);
}
context_check (pctx);
s = cublasSaxpy (h, N, &alpha, d_X, 1, d_Y, 1);
if (s != CUBLAS_STATUS_SUCCESS)
{
fprintf (stderr, "cublasSaxpy failed: %d\n", s);
exit (EXIT_FAILURE);
}
context_check (pctx);
acc_memcpy_from_device (&h_Y1[0], d_Y, N * sizeof (float));
context_check (pctx);
error_norm = 0;
ref_norm = 0;
for (i = 0; i < N; ++i)
{
float diff;
diff = h_Y1[i] - h_Y2[i];
error_norm += diff * diff;
ref_norm += h_Y2[i] * h_Y2[i];
}
error_norm = (float) sqrt ((double) error_norm);
ref_norm = (float) sqrt ((double) ref_norm);
if ((fabs (ref_norm) < 1e-7) || ((error_norm / ref_norm) >= 1e-6f))
{
fprintf (stderr, "math error\n");
exit (EXIT_FAILURE);
}
free (h_X);
free (h_Y1);
free (h_Y2);
acc_free (d_X);
acc_free (d_Y);
context_check (pctx);
s = cublasDestroy (h);
if (s != CUBLAS_STATUS_SUCCESS)
{
fprintf (stderr, "cublasDestroy failed: %d\n", s);
exit (EXIT_FAILURE);
}
context_check (pctx);
acc_shutdown (acc_device_nvidia);
r = cuCtxGetCurrent (&pctx);
if (r != CUDA_SUCCESS)
{
fprintf (stderr, "cuCtxGetCurrent failed: %d\n", r);
exit (EXIT_FAILURE);
}
if (pctx)
{
fprintf (stderr, "Unexpected context\n");
exit (EXIT_FAILURE);
}
return EXIT_SUCCESS;
}
int main( int argc, char** argv )
{
TESTING_INIT();
real_Double_t gflops, t1, t2;
float c_neg_one = MAGMA_S_NEG_ONE;
magma_int_t ione = 1;
const char trans[] = { 'N', 'C', 'T' };
const char uplo[] = { 'L', 'U' };
const char diag[] = { 'U', 'N' };
const char side[] = { 'L', 'R' };
float *A, *B, *C, *C2, *LU;
float *dA, *dB, *dC1, *dC2;
float alpha = MAGMA_S_MAKE( 0.5, 0.1 );
float beta = MAGMA_S_MAKE( 0.7, 0.2 );
float dalpha = 0.6;
float dbeta = 0.8;
float work[1], error, total_error;
magma_int_t ISEED[4] = {0,0,0,1};
magma_int_t m, n, k, size, maxn, ld, info;
magma_int_t *piv;
magma_err_t err;
magma_opts opts;
parse_opts( argc, argv, &opts );
printf( "Compares magma wrapper function to cublas function; all diffs should be exactly 0.\n\n" );
total_error = 0.;
for( int i = 0; i < opts.ntest; ++i ) {
m = opts.msize[i];
n = opts.nsize[i];
k = opts.ksize[i];
printf("=========================================================================\n");
printf( "M %d, N %d, K %d\n", (int) m, (int) n, (int) k );
// allocate matrices
// over-allocate so they can be any combination of {m,n,k} x {m,n,k}.
maxn = max( max( m, n ), k );
ld = maxn;
size = maxn*maxn;
err = magma_malloc_cpu( (void**) &piv, maxn*sizeof(magma_int_t) ); assert( err == 0 );
err = magma_smalloc_pinned( &A, size ); assert( err == 0 );
err = magma_smalloc_pinned( &B, size ); assert( err == 0 );
err = magma_smalloc_pinned( &C, size ); assert( err == 0 );
err = magma_smalloc_pinned( &C2, size ); assert( err == 0 );
err = magma_smalloc_pinned( &LU, size ); assert( err == 0 );
err = magma_smalloc( &dA, size ); assert( err == 0 );
err = magma_smalloc( &dB, size ); assert( err == 0 );
err = magma_smalloc( &dC1, size ); assert( err == 0 );
err = magma_smalloc( &dC2, size ); assert( err == 0 );
// initialize matrices
size = maxn*maxn;
lapackf77_slarnv( &ione, ISEED, &size, A );
lapackf77_slarnv( &ione, ISEED, &size, B );
lapackf77_slarnv( &ione, ISEED, &size, C );
printf( "========== Level 1 BLAS ==========\n" );
// ----- test SSWAP
// swap 2nd and 3rd columns of dA, then copy to C2 and compare with A
assert( n >= 4 );
magma_ssetmatrix( m, n, A, ld, dA, ld );
magma_ssetmatrix( m, n, A, ld, dB, ld );
magma_sswap( m, dA(0,1), 1, dA(0,2), 1 );
magma_sswap( m, dB(0,1), 1, dB(0,2), 1 );
// check results, storing diff between magma and cuda calls in C2
cublasSaxpy( ld*n, c_neg_one, dA, 1, dB, 1 );
magma_sgetmatrix( m, n, dB, ld, C2, ld );
error = lapackf77_slange( "F", &m, &k, C2, &ld, work );
total_error += error;
printf( "sswap diff %.2g\n", error );
// ----- test ISAMAX
// get argmax of column of A
magma_ssetmatrix( m, k, A, ld, dA, ld );
error = 0;
for( int j = 0; j < k; ++j ) {
magma_int_t i1 = magma_isamax( m, dA(0,j), 1 );
magma_int_t i2 = cublasIsamax( m, dA(0,j), 1 );
assert( i1 == i2 );
error += abs( i1 - i2 );
}
total_error += error;
gflops = (float)m * k / 1e9;
printf( "isamax diff %.2g\n", error );
printf( "\n" );
printf( "========== Level 2 BLAS ==========\n" );
// ----- test SGEMV
// c = alpha*A*b + beta*c, with A m*n; b,c m or n-vectors
// try no-trans/trans
for( int ia = 0; ia < 3; ++ia ) {
magma_ssetmatrix( m, n, A, ld, dA, ld );
magma_ssetvector( maxn, B, 1, dB, 1 );
magma_ssetvector( maxn, C, 1, dC1, 1 );
magma_ssetvector( maxn, C, 1, dC2, 1 );
t1 = magma_sync_wtime( 0 );
magma_sgemv( trans[ia], m, n, alpha, dA, ld, dB, 1, beta, dC1, 1 );
t1 = magma_sync_wtime( 0 ) - t1;
t2 = magma_sync_wtime( 0 );
cublasSgemv( trans[ia], m, n, alpha, dA, ld, dB, 1, beta, dC2, 1 );
t2 = magma_sync_wtime( 0 ) - t2;
// check results, storing diff between magma and cuda call in C2
size = (trans[ia] == 'N' ? m : n);
cublasSaxpy( size, c_neg_one, dC1, 1, dC2, 1 );
magma_sgetvector( size, dC2, 1, C2, 1 );
error = lapackf77_slange( "F", &size, &ione, C2, &ld, work );
total_error += error;
gflops = FLOPS_SGEMV( m, n ) / 1e9;
printf( "sgemv( %c ) diff %.2g, Gflop/s %6.2f, %6.2f\n",
trans[ia], error, gflops/t1, gflops/t2 );
}
printf( "\n" );
// ----- test SSYMV
// c = alpha*A*b + beta*c, with A m*m symmetric; b,c m-vectors
// try upper/lower
for( int iu = 0; iu < 2; ++iu ) {
magma_ssetmatrix( m, m, A, ld, dA, ld );
magma_ssetvector( m, B, 1, dB, 1 );
magma_ssetvector( m, C, 1, dC1, 1 );
magma_ssetvector( m, C, 1, dC2, 1 );
t1 = magma_sync_wtime( 0 );
magma_ssymv( uplo[iu], m, alpha, dA, ld, dB, 1, beta, dC1, 1 );
t1 = magma_sync_wtime( 0 ) - t1;
t2 = magma_sync_wtime( 0 );
cublasSsymv( uplo[iu], m, alpha, dA, ld, dB, 1, beta, dC2, 1 );
t2 = magma_sync_wtime( 0 ) - t2;
// check results, storing diff between magma and cuda call in C2
cublasSaxpy( m, c_neg_one, dC1, 1, dC2, 1 );
magma_sgetvector( m, dC2, 1, C2, 1 );
error = lapackf77_slange( "F", &m, &ione, C2, &ld, work );
total_error += error;
gflops = FLOPS_SSYMV( m ) / 1e9;
printf( "ssymv( %c ) diff %.2g, Gflop/s %6.2f, %6.2f\n",
uplo[iu], error, gflops/t1, gflops/t2 );
}
printf( "\n" );
// ----- test STRSV
// solve A*c = c, with A m*m triangular; c m-vector
// try upper/lower, no-trans/trans, unit/non-unit diag
// Factor A into LU to get well-conditioned triangles, else solve yields garbage.
// Still can give garbage if solves aren't consistent with LU factors,
// e.g., using unit diag for U, so copy lower triangle to upper triangle.
// Also used for trsm later.
lapackf77_slacpy( "Full", &maxn, &maxn, A, &ld, LU, &ld );
lapackf77_sgetrf( &maxn, &maxn, LU, &ld, piv, &info );
for( int j = 0; j < maxn; ++j ) {
for( int i = 0; i < j; ++i ) {
*LU(i,j) = *LU(j,i);
}
}
for( int iu = 0; iu < 2; ++iu ) {
for( int it = 0; it < 3; ++it ) {
for( int id = 0; id < 2; ++id ) {
magma_ssetmatrix( m, m, LU, ld, dA, ld );
magma_ssetvector( m, C, 1, dC1, 1 );
magma_ssetvector( m, C, 1, dC2, 1 );
t1 = magma_sync_wtime( 0 );
magma_strsv( uplo[iu], trans[it], diag[id], m, dA, ld, dC1, 1 );
t1 = magma_sync_wtime( 0 ) - t1;
t2 = magma_sync_wtime( 0 );
cublasStrsv( uplo[iu], trans[it], diag[id], m, dA, ld, dC2, 1 );
t2 = magma_sync_wtime( 0 ) - t2;
// check results, storing diff between magma and cuda call in C2
cublasSaxpy( m, c_neg_one, dC1, 1, dC2, 1 );
magma_sgetvector( m, dC2, 1, C2, 1 );
error = lapackf77_slange( "F", &m, &ione, C2, &ld, work );
total_error += error;
gflops = FLOPS_STRSM( MagmaLeft, m, 1 ) / 1e9;
printf( "strsv( %c, %c, %c ) diff %.2g, Gflop/s %6.2f, %6.2f\n",
uplo[iu], trans[it], diag[id], error, gflops/t1, gflops/t2 );
}}}
printf( "\n" );
printf( "========== Level 3 BLAS ==========\n" );
// ----- test SGEMM
// C = alpha*A*B + beta*C, with A m*k or k*m; B k*n or n*k; C m*n
// try combinations of no-trans/trans
for( int ia = 0; ia < 3; ++ia ) {
for( int ib = 0; ib < 3; ++ib ) {
bool nta = (trans[ia] == 'N');
bool ntb = (trans[ib] == 'N');
magma_ssetmatrix( (nta ? m : k), (nta ? m : k), A, ld, dA, ld );
magma_ssetmatrix( (ntb ? k : n), (ntb ? n : k), B, ld, dB, ld );
magma_ssetmatrix( m, n, C, ld, dC1, ld );
magma_ssetmatrix( m, n, C, ld, dC2, ld );
t1 = magma_sync_wtime( 0 );
magma_sgemm( trans[ia], trans[ib], m, n, k, alpha, dA, ld, dB, ld, beta, dC1, ld );
t1 = magma_sync_wtime( 0 ) - t1;
t2 = magma_sync_wtime( 0 );
cublasSgemm( trans[ia], trans[ib], m, n, k, alpha, dA, ld, dB, ld, beta, dC2, ld );
t2 = magma_sync_wtime( 0 ) - t2;
// check results, storing diff between magma and cuda call in C2
cublasSaxpy( ld*n, c_neg_one, dC1, 1, dC2, 1 );
magma_sgetmatrix( m, n, dC2, ld, C2, ld );
error = lapackf77_slange( "F", &m, &n, C2, &ld, work );
total_error += error;
gflops = FLOPS_SGEMM( m, n, k ) / 1e9;
printf( "sgemm( %c, %c ) diff %.2g, Gflop/s %6.2f, %6.2f\n",
trans[ia], trans[ib], error, gflops/t1, gflops/t2 );
}}
printf( "\n" );
// ----- test SSYMM
// C = alpha*A*B + beta*C (left) with A m*m symmetric; B,C m*n; or
// C = alpha*B*A + beta*C (right) with A n*n symmetric; B,C m*n
// try left/right, upper/lower
for( int is = 0; is < 2; ++is ) {
for( int iu = 0; iu < 2; ++iu ) {
magma_ssetmatrix( m, m, A, ld, dA, ld );
magma_ssetmatrix( m, n, B, ld, dB, ld );
magma_ssetmatrix( m, n, C, ld, dC1, ld );
magma_ssetmatrix( m, n, C, ld, dC2, ld );
t1 = magma_sync_wtime( 0 );
magma_ssymm( side[is], uplo[iu], m, n, alpha, dA, ld, dB, ld, beta, dC1, ld );
t1 = magma_sync_wtime( 0 ) - t1;
t2 = magma_sync_wtime( 0 );
cublasSsymm( side[is], uplo[iu], m, n, alpha, dA, ld, dB, ld, beta, dC2, ld );
t2 = magma_sync_wtime( 0 ) - t2;
// check results, storing diff between magma and cuda call in C2
cublasSaxpy( ld*n, c_neg_one, dC1, 1, dC2, 1 );
magma_sgetmatrix( m, n, dC2, ld, C2, ld );
error = lapackf77_slange( "F", &m, &n, C2, &ld, work );
total_error += error;
gflops = FLOPS_SSYMM( side[is], m, n ) / 1e9;
printf( "ssymm( %c, %c ) diff %.2g, Gflop/s %6.2f, %6.2f\n",
side[is], uplo[iu], error, gflops/t1, gflops/t2 );
}}
printf( "\n" );
// ----- test SSYRK
// C = alpha*A*A^H + beta*C (no-trans) with A m*k and C m*m symmetric; or
// C = alpha*A^H*A + beta*C (trans) with A k*m and C m*m symmetric
// try upper/lower, no-trans/trans
for( int iu = 0; iu < 2; ++iu ) {
for( int it = 0; it < 3; ++it ) {
magma_ssetmatrix( n, k, A, ld, dA, ld );
magma_ssetmatrix( n, n, C, ld, dC1, ld );
magma_ssetmatrix( n, n, C, ld, dC2, ld );
t1 = magma_sync_wtime( 0 );
magma_ssyrk( uplo[iu], trans[it], n, k, dalpha, dA, ld, dbeta, dC1, ld );
t1 = magma_sync_wtime( 0 ) - t1;
t2 = magma_sync_wtime( 0 );
cublasSsyrk( uplo[iu], trans[it], n, k, dalpha, dA, ld, dbeta, dC2, ld );
t2 = magma_sync_wtime( 0 ) - t2;
// check results, storing diff between magma and cuda call in C2
cublasSaxpy( ld*n, c_neg_one, dC1, 1, dC2, 1 );
magma_sgetmatrix( n, n, dC2, ld, C2, ld );
error = lapackf77_slange( "F", &n, &n, C2, &ld, work );
total_error += error;
gflops = FLOPS_SSYRK( k, n ) / 1e9;
printf( "ssyrk( %c, %c ) diff %.2g, Gflop/s %6.2f, %6.2f\n",
uplo[iu], trans[it], error, gflops/t1, gflops/t2 );
}}
printf( "\n" );
// ----- test SSYR2K
// C = alpha*A*B^H + ^alpha*B*A^H + beta*C (no-trans) with A,B n*k; C n*n symmetric; or
// C = alpha*A^H*B + ^alpha*B^H*A + beta*C (trans) with A,B k*n; C n*n symmetric
// try upper/lower, no-trans/trans
for( int iu = 0; iu < 2; ++iu ) {
for( int it = 0; it < 3; ++it ) {
bool nt = (trans[it] == 'N');
magma_ssetmatrix( (nt ? n : k), (nt ? n : k), A, ld, dA, ld );
magma_ssetmatrix( n, n, C, ld, dC1, ld );
magma_ssetmatrix( n, n, C, ld, dC2, ld );
t1 = magma_sync_wtime( 0 );
magma_ssyr2k( uplo[iu], trans[it], n, k, alpha, dA, ld, dB, ld, dbeta, dC1, ld );
t1 = magma_sync_wtime( 0 ) - t1;
t2 = magma_sync_wtime( 0 );
cublasSsyr2k( uplo[iu], trans[it], n, k, alpha, dA, ld, dB, ld, dbeta, dC2, ld );
t2 = magma_sync_wtime( 0 ) - t2;
// check results, storing diff between magma and cuda call in C2
cublasSaxpy( ld*n, c_neg_one, dC1, 1, dC2, 1 );
magma_sgetmatrix( n, n, dC2, ld, C2, ld );
error = lapackf77_slange( "F", &n, &n, C2, &ld, work );
total_error += error;
gflops = FLOPS_SSYR2K( k, n ) / 1e9;
printf( "ssyr2k( %c, %c ) diff %.2g, Gflop/s %6.2f, %6.2f\n",
uplo[iu], trans[it], error, gflops/t1, gflops/t2 );
}}
printf( "\n" );
// ----- test STRMM
// C = alpha*A*C (left) with A m*m triangular; C m*n; or
// C = alpha*C*A (right) with A n*n triangular; C m*n
// try left/right, upper/lower, no-trans/trans, unit/non-unit
for( int is = 0; is < 2; ++is ) {
for( int iu = 0; iu < 2; ++iu ) {
for( int it = 0; it < 3; ++it ) {
for( int id = 0; id < 2; ++id ) {
bool left = (side[is] == 'L');
magma_ssetmatrix( (left ? m : n), (left ? m : n), A, ld, dA, ld );
magma_ssetmatrix( m, n, C, ld, dC1, ld );
magma_ssetmatrix( m, n, C, ld, dC2, ld );
t1 = magma_sync_wtime( 0 );
magma_strmm( side[is], uplo[iu], trans[it], diag[id], m, n, alpha, dA, ld, dC1, ld );
t1 = magma_sync_wtime( 0 ) - t1;
t2 = magma_sync_wtime( 0 );
cublasStrmm( side[is], uplo[iu], trans[it], diag[id], m, n, alpha, dA, ld, dC2, ld );
t2 = magma_sync_wtime( 0 ) - t2;
// check results, storing diff between magma and cuda call in C2
cublasSaxpy( ld*n, c_neg_one, dC1, 1, dC2, 1 );
magma_sgetmatrix( m, n, dC2, ld, C2, ld );
error = lapackf77_slange( "F", &n, &n, C2, &ld, work );
total_error += error;
gflops = FLOPS_STRMM( side[is], m, n ) / 1e9;
printf( "strmm( %c, %c ) diff %.2g, Gflop/s %6.2f, %6.2f\n",
uplo[iu], trans[it], error, gflops/t1, gflops/t2 );
}}}}
printf( "\n" );
// ----- test STRSM
// solve A*X = alpha*B (left) with A m*m triangular; B m*n; or
// solve X*A = alpha*B (right) with A n*n triangular; B m*n
// try left/right, upper/lower, no-trans/trans, unit/non-unit
for( int is = 0; is < 2; ++is ) {
for( int iu = 0; iu < 2; ++iu ) {
for( int it = 0; it < 3; ++it ) {
for( int id = 0; id < 2; ++id ) {
bool left = (side[is] == 'L');
magma_ssetmatrix( (left ? m : n), (left ? m : n), LU, ld, dA, ld );
magma_ssetmatrix( m, n, C, ld, dC1, ld );
magma_ssetmatrix( m, n, C, ld, dC2, ld );
t1 = magma_sync_wtime( 0 );
magma_strsm( side[is], uplo[iu], trans[it], diag[id], m, n, alpha, dA, ld, dC1, ld );
t1 = magma_sync_wtime( 0 ) - t1;
t2 = magma_sync_wtime( 0 );
cublasStrsm( side[is], uplo[iu], trans[it], diag[id], m, n, alpha, dA, ld, dC2, ld );
t2 = magma_sync_wtime( 0 ) - t2;
// check results, storing diff between magma and cuda call in C2
cublasSaxpy( ld*n, c_neg_one, dC1, 1, dC2, 1 );
magma_sgetmatrix( m, n, dC2, ld, C2, ld );
error = lapackf77_slange( "F", &n, &n, C2, &ld, work );
total_error += error;
gflops = FLOPS_STRSM( side[is], m, n ) / 1e9;
printf( "strsm( %c, %c ) diff %.2g, Gflop/s %6.2f, %6.2f\n",
uplo[iu], trans[it], error, gflops/t1, gflops/t2 );
}}}}
printf( "\n" );
// cleanup
magma_free_cpu( piv );
magma_free_pinned( A );
magma_free_pinned( B );
magma_free_pinned( C );
magma_free_pinned( C2 );
magma_free_pinned( LU );
magma_free( dA );
magma_free( dB );
magma_free( dC1 );
magma_free( dC2 );
}
if ( total_error != 0. ) {
printf( "total error %.2g -- ought to be 0 -- some test failed (see above).\n",
total_error );
}
else {
printf( "all tests passed\n" );
}
TESTING_FINALIZE();
return 0;
}
/* Solve Ax=b using the conjugate gradient method a) without any preconditioning, b) using an Incomplete Cholesky preconditioner and c) using an ILU0 preconditioner. */
int main(int argc, char **argv)
{
const int max_iter = 1000;
int k, M = 0, N = 0, nz = 0, *I = NULL, *J = NULL;
int *d_col, *d_row;
int qatest = 0;
const float tol = 1e-12f;
float *x, *rhs;
float r0, r1, alpha, beta;
float *d_val, *d_x;
float *d_zm1, *d_zm2, *d_rm2;
float *d_r, *d_p, *d_omega, *d_y;
float *val = NULL;
float *d_valsILU0;
float *valsILU0;
float rsum, diff, err = 0.0;
float qaerr1, qaerr2 = 0.0;
float dot, numerator, denominator, nalpha;
const float floatone = 1.0;
const float floatzero = 0.0;
int nErrors = 0;
printf("conjugateGradientPrecond starting...\n");
/* QA testing mode */
if (checkCmdLineFlag(argc, (const char **)argv, "qatest"))
{
qatest = 1;
}
/* This will pick the best possible CUDA capable device */
cudaDeviceProp deviceProp;
int devID = findCudaDevice(argc, (const char **)argv);
printf("GPU selected Device ID = %d \n", devID);
if (devID < 0)
{
printf("Invalid GPU device %d selected, exiting...\n", devID);
exit(EXIT_SUCCESS);
}
checkCudaErrors(cudaGetDeviceProperties(&deviceProp, devID));
/* Statistics about the GPU device */
printf("> GPU device has %d Multi-Processors, SM %d.%d compute capabilities\n\n",
deviceProp.multiProcessorCount, deviceProp.major, deviceProp.minor);
int version = (deviceProp.major * 0x10 + deviceProp.minor);
if (version < 0x11)
{
printf("%s: requires a minimum CUDA compute 1.1 capability\n", sSDKname);
// cudaDeviceReset causes the driver to clean up all state. While
// not mandatory in normal operation, it is good practice. It is also
// needed to ensure correct operation when the application is being
// profiled. Calling cudaDeviceReset causes all profile data to be
// flushed before the application exits
cudaDeviceReset();
exit(EXIT_SUCCESS);
}
/* Generate a random tridiagonal symmetric matrix in CSR (Compressed Sparse Row) format */
M = N = 16384;
nz = 5*N-4*(int)sqrt((double)N);
I = (int *)malloc(sizeof(int)*(N+1)); // csr row pointers for matrix A
J = (int *)malloc(sizeof(int)*nz); // csr column indices for matrix A
val = (float *)malloc(sizeof(float)*nz); // csr values for matrix A
x = (float *)malloc(sizeof(float)*N);
rhs = (float *)malloc(sizeof(float)*N);
for (int i = 0; i < N; i++)
{
rhs[i] = 0.0; // Initialize RHS
x[i] = 0.0; // Initial approximation of solution
}
genLaplace(I, J, val, M, N, nz, rhs);
/* Create CUBLAS context */
cublasHandle_t cublasHandle = 0;
cublasStatus_t cublasStatus;
cublasStatus = cublasCreate(&cublasHandle);
checkCudaErrors(cublasStatus);
/* Create CUSPARSE context */
cusparseHandle_t cusparseHandle = 0;
cusparseStatus_t cusparseStatus;
cusparseStatus = cusparseCreate(&cusparseHandle);
checkCudaErrors(cusparseStatus);
/* Description of the A matrix*/
cusparseMatDescr_t descr = 0;
cusparseStatus = cusparseCreateMatDescr(&descr);
checkCudaErrors(cusparseStatus);
/* Define the properties of the matrix */
cusparseSetMatType(descr,CUSPARSE_MATRIX_TYPE_GENERAL);
cusparseSetMatIndexBase(descr,CUSPARSE_INDEX_BASE_ZERO);
/* Allocate required memory */
checkCudaErrors(cudaMalloc((void **)&d_col, nz*sizeof(int)));
checkCudaErrors(cudaMalloc((void **)&d_row, (N+1)*sizeof(int)));
checkCudaErrors(cudaMalloc((void **)&d_val, nz*sizeof(float)));
checkCudaErrors(cudaMalloc((void **)&d_x, N*sizeof(float)));
checkCudaErrors(cudaMalloc((void **)&d_y, N*sizeof(float)));
checkCudaErrors(cudaMalloc((void **)&d_r, N*sizeof(float)));
checkCudaErrors(cudaMalloc((void **)&d_p, N*sizeof(float)));
checkCudaErrors(cudaMalloc((void **)&d_omega, N*sizeof(float)));
cudaMemcpy(d_col, J, nz*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_row, I, (N+1)*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_val, val, nz*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_x, x, N*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_r, rhs, N*sizeof(float), cudaMemcpyHostToDevice);
/* Conjugate gradient without preconditioning.
------------------------------------------
Follows the description by Golub & Van Loan, "Matrix Computations 3rd ed.", Section 10.2.6 */
printf("Convergence of conjugate gradient without preconditioning: \n");
k = 0;
r0 = 0;
cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1);
while (r1 > tol*tol && k <= max_iter)
{
k++;
if (k == 1)
{
cublasScopy(cublasHandle, N, d_r, 1, d_p, 1);
}
else
{
beta = r1/r0;
cublasSscal(cublasHandle, N, &beta, d_p, 1);
cublasSaxpy(cublasHandle, N, &floatone, d_r, 1, d_p, 1) ;
}
cusparseScsrmv(cusparseHandle,CUSPARSE_OPERATION_NON_TRANSPOSE, N, N, nz, &floatone, descr, d_val, d_row, d_col, d_p, &floatzero, d_omega);
cublasSdot(cublasHandle, N, d_p, 1, d_omega, 1, &dot);
alpha = r1/dot;
cublasSaxpy(cublasHandle, N, &alpha, d_p, 1, d_x, 1);
nalpha = -alpha;
cublasSaxpy(cublasHandle, N, &nalpha, d_omega, 1, d_r, 1);
r0 = r1;
cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1);
}
printf(" iteration = %3d, residual = %e \n", k, sqrt(r1));
cudaMemcpy(x, d_x, N*sizeof(float), cudaMemcpyDeviceToHost);
/* check result */
err = 0.0;
for (int i = 0; i < N; i++)
{
rsum = 0.0;
for (int j = I[i]; j < I[i+1]; j++)
{
rsum += val[j]*x[J[j]];
}
diff = fabs(rsum - rhs[i]);
if (diff > err)
{
err = diff;
}
}
printf(" Convergence Test: %s \n", (k <= max_iter) ? "OK" : "FAIL");
nErrors += (k > max_iter) ? 1 : 0;
qaerr1 = err;
if (0)
{
// output result in matlab-style array
int n=(int)sqrt((double)N);
printf("a = [ ");
for (int iy=0; iy<n; iy++)
{
for (int ix=0; ix<n; ix++)
{
printf(" %f ", x[iy*n+ix]);
}
if (iy == n-1)
{
printf(" ]");
}
printf("\n");
}
}
/* Preconditioned Conjugate Gradient using ILU.
--------------------------------------------
Follows the description by Golub & Van Loan, "Matrix Computations 3rd ed.", Algorithm 10.3.1 */
printf("\nConvergence of conjugate gradient using incomplete LU preconditioning: \n");
int nzILU0 = 2*N-1;
valsILU0 = (float *) malloc(nz*sizeof(float));
checkCudaErrors(cudaMalloc((void **)&d_valsILU0, nz*sizeof(float)));
checkCudaErrors(cudaMalloc((void **)&d_zm1, (N)*sizeof(float)));
checkCudaErrors(cudaMalloc((void **)&d_zm2, (N)*sizeof(float)));
checkCudaErrors(cudaMalloc((void **)&d_rm2, (N)*sizeof(float)));
/* create the analysis info object for the A matrix */
cusparseSolveAnalysisInfo_t infoA = 0;
cusparseStatus = cusparseCreateSolveAnalysisInfo(&infoA);
checkCudaErrors(cusparseStatus);
/* Perform the analysis for the Non-Transpose case */
cusparseStatus = cusparseScsrsv_analysis(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE,
N, nz, descr, d_val, d_row, d_col, infoA);
checkCudaErrors(cusparseStatus);
/* Copy A data to ILU0 vals as input*/
cudaMemcpy(d_valsILU0, d_val, nz*sizeof(float), cudaMemcpyDeviceToDevice);
/* generate the Incomplete LU factor H for the matrix A using cudsparseScsrilu0 */
cusparseStatus = cusparseScsrilu0(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, N, descr, d_valsILU0, d_row, d_col, infoA);
checkCudaErrors(cusparseStatus);
/* Create info objects for the ILU0 preconditioner */
cusparseSolveAnalysisInfo_t info_u;
cusparseCreateSolveAnalysisInfo(&info_u);
cusparseMatDescr_t descrL = 0;
cusparseStatus = cusparseCreateMatDescr(&descrL);
cusparseSetMatType(descrL,CUSPARSE_MATRIX_TYPE_GENERAL);
cusparseSetMatIndexBase(descrL,CUSPARSE_INDEX_BASE_ZERO);
cusparseSetMatFillMode(descrL, CUSPARSE_FILL_MODE_LOWER);
cusparseSetMatDiagType(descrL, CUSPARSE_DIAG_TYPE_UNIT);
cusparseMatDescr_t descrU = 0;
cusparseStatus = cusparseCreateMatDescr(&descrU);
cusparseSetMatType(descrU,CUSPARSE_MATRIX_TYPE_GENERAL);
cusparseSetMatIndexBase(descrU,CUSPARSE_INDEX_BASE_ZERO);
cusparseSetMatFillMode(descrU, CUSPARSE_FILL_MODE_UPPER);
cusparseSetMatDiagType(descrU, CUSPARSE_DIAG_TYPE_NON_UNIT);
cusparseStatus = cusparseScsrsv_analysis(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, N, nz, descrU, d_val, d_row, d_col, info_u);
/* reset the initial guess of the solution to zero */
for (int i = 0; i < N; i++)
{
x[i] = 0.0;
}
checkCudaErrors(cudaMemcpy(d_r, rhs, N*sizeof(float), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_x, x, N*sizeof(float), cudaMemcpyHostToDevice));
k = 0;
cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1);
while (r1 > tol*tol && k <= max_iter)
{
// Forward Solve, we can re-use infoA since the sparsity pattern of A matches that of L
cusparseStatus = cusparseScsrsv_solve(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, N, &floatone, descrL,
d_valsILU0, d_row, d_col, infoA, d_r, d_y);
checkCudaErrors(cusparseStatus);
// Back Substitution
cusparseStatus = cusparseScsrsv_solve(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, N, &floatone, descrU,
d_valsILU0, d_row, d_col, info_u, d_y, d_zm1);
checkCudaErrors(cusparseStatus);
k++;
if (k == 1)
{
cublasScopy(cublasHandle, N, d_zm1, 1, d_p, 1);
}
else
{
cublasSdot(cublasHandle, N, d_r, 1, d_zm1, 1, &numerator);
cublasSdot(cublasHandle, N, d_rm2, 1, d_zm2, 1, &denominator);
beta = numerator/denominator;
cublasSscal(cublasHandle, N, &beta, d_p, 1);
cublasSaxpy(cublasHandle, N, &floatone, d_zm1, 1, d_p, 1) ;
}
cusparseScsrmv(cusparseHandle,CUSPARSE_OPERATION_NON_TRANSPOSE, N, N, nzILU0, &floatone, descrU, d_val, d_row, d_col, d_p, &floatzero, d_omega);
cublasSdot(cublasHandle, N, d_r, 1, d_zm1, 1, &numerator);
cublasSdot(cublasHandle, N, d_p, 1, d_omega, 1, &denominator);
alpha = numerator / denominator;
cublasSaxpy(cublasHandle, N, &alpha, d_p, 1, d_x, 1);
cublasScopy(cublasHandle, N, d_r, 1, d_rm2, 1);
cublasScopy(cublasHandle, N, d_zm1, 1, d_zm2, 1);
nalpha = -alpha;
cublasSaxpy(cublasHandle, N, &nalpha, d_omega, 1, d_r, 1);
cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1);
}
printf(" iteration = %3d, residual = %e \n", k, sqrt(r1));
cudaMemcpy(x, d_x, N*sizeof(float), cudaMemcpyDeviceToHost);
/* check result */
err = 0.0;
for (int i = 0; i < N; i++)
{
rsum = 0.0;
for (int j = I[i]; j < I[i+1]; j++)
{
rsum += val[j]*x[J[j]];
}
diff = fabs(rsum - rhs[i]);
if (diff > err)
{
err = diff;
}
}
printf(" Convergence Test: %s \n", (k <= max_iter) ? "OK" : "FAIL");
nErrors += (k > max_iter) ? 1 : 0;
qaerr2 = err;
/* Destroy parameters */
cusparseDestroySolveAnalysisInfo(infoA);
cusparseDestroySolveAnalysisInfo(info_u);
/* Destroy contexts */
cusparseDestroy(cusparseHandle);
cublasDestroy(cublasHandle);
/* Free device memory */
free(I);
free(J);
free(val);
free(x);
free(rhs);
free(valsILU0);
cudaFree(d_col);
cudaFree(d_row);
cudaFree(d_val);
cudaFree(d_x);
cudaFree(d_y);
cudaFree(d_r);
cudaFree(d_p);
cudaFree(d_omega);
cudaFree(d_valsILU0);
cudaFree(d_zm1);
cudaFree(d_zm2);
cudaFree(d_rm2);
// cudaDeviceReset causes the driver to clean up all state. While
// not mandatory in normal operation, it is good practice. It is also
// needed to ensure correct operation when the application is being
// profiled. Calling cudaDeviceReset causes all profile data to be
// flushed before the application exits
cudaDeviceReset();
printf(" Test Summary:\n");
printf(" Counted total of %d errors\n", nErrors);
printf(" qaerr1 = %f qaerr2 = %f\n\n", fabs(qaerr1), fabs(qaerr2));
exit((nErrors == 0 &&fabs(qaerr1)<1e-5 && fabs(qaerr2) < 1e-5 ? EXIT_SUCCESS : EXIT_FAILURE));
}
int main(int argc, char **argv)
{
int N = 0, nz = 0, *I = NULL, *J = NULL;
float *val = NULL;
const float tol = 1e-5f;
const int max_iter = 10000;
float *x;
float *rhs;
float a, b, na, r0, r1;
float dot;
float *r, *p, *Ax;
int k;
float alpha, beta, alpham1;
printf("Starting [%s]...\n", sSDKname);
// This will pick the best possible CUDA capable device
cudaDeviceProp deviceProp;
int devID = findCudaDevice(argc, (const char **)argv);
checkCudaErrors(cudaGetDeviceProperties(&deviceProp, devID));
#if defined(__APPLE__) || defined(MACOSX)
fprintf(stderr, "Unified Memory not currently supported on OS X\n");
cudaDeviceReset();
exit(EXIT_WAIVED);
#endif
if (sizeof(void *) != 8)
{
fprintf(stderr, "Unified Memory requires compiling for a 64-bit system.\n");
cudaDeviceReset();
exit(EXIT_WAIVED);
}
if (((deviceProp.major << 4) + deviceProp.minor) < 0x30)
{
fprintf(stderr, "%s requires Compute Capability of SM 3.0 or higher to run.\nexiting...\n", argv[0]);
cudaDeviceReset();
exit(EXIT_WAIVED);
}
// Statistics about the GPU device
printf("> GPU device has %d Multi-Processors, SM %d.%d compute capabilities\n\n",
deviceProp.multiProcessorCount, deviceProp.major, deviceProp.minor);
/* Generate a random tridiagonal symmetric matrix in CSR format */
N = 1048576;
nz = (N-2)*3 + 4;
cudaMallocManaged((void **)&I, sizeof(int)*(N+1));
cudaMallocManaged((void **)&J, sizeof(int)*nz);
cudaMallocManaged((void **)&val, sizeof(float)*nz);
genTridiag(I, J, val, N, nz);
cudaMallocManaged((void **)&x, sizeof(float)*N);
cudaMallocManaged((void **)&rhs, sizeof(float)*N);
for (int i = 0; i < N; i++)
{
rhs[i] = 1.0;
x[i] = 0.0;
}
/* Get handle to the CUBLAS context */
cublasHandle_t cublasHandle = 0;
cublasStatus_t cublasStatus;
cublasStatus = cublasCreate(&cublasHandle);
checkCudaErrors(cublasStatus);
/* Get handle to the CUSPARSE context */
cusparseHandle_t cusparseHandle = 0;
cusparseStatus_t cusparseStatus;
cusparseStatus = cusparseCreate(&cusparseHandle);
checkCudaErrors(cusparseStatus);
cusparseMatDescr_t descr = 0;
cusparseStatus = cusparseCreateMatDescr(&descr);
checkCudaErrors(cusparseStatus);
cusparseSetMatType(descr,CUSPARSE_MATRIX_TYPE_GENERAL);
cusparseSetMatIndexBase(descr,CUSPARSE_INDEX_BASE_ZERO);
// temp memory for CG
checkCudaErrors(cudaMallocManaged((void **)&r, N*sizeof(float)));
checkCudaErrors(cudaMallocManaged((void **)&p, N*sizeof(float)));
checkCudaErrors(cudaMallocManaged((void **)&Ax, N*sizeof(float)));
cudaDeviceSynchronize();
for (int i=0; i < N; i++)
{
r[i] = rhs[i];
}
alpha = 1.0;
alpham1 = -1.0;
beta = 0.0;
r0 = 0.;
cusparseScsrmv(cusparseHandle,CUSPARSE_OPERATION_NON_TRANSPOSE, N, N, nz, &alpha, descr, val, I, J, x, &beta, Ax);
cublasSaxpy(cublasHandle, N, &alpham1, Ax, 1, r, 1);
cublasStatus = cublasSdot(cublasHandle, N, r, 1, r, 1, &r1);
k = 1;
while (r1 > tol*tol && k <= max_iter)
{
if (k > 1)
{
b = r1 / r0;
cublasStatus = cublasSscal(cublasHandle, N, &b, p, 1);
cublasStatus = cublasSaxpy(cublasHandle, N, &alpha, r, 1, p, 1);
}
else
{
cublasStatus = cublasScopy(cublasHandle, N, r, 1, p, 1);
}
cusparseScsrmv(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, N, N, nz, &alpha, descr, val, I, J, p, &beta, Ax);
cublasStatus = cublasSdot(cublasHandle, N, p, 1, Ax, 1, &dot);
a = r1 / dot;
cublasStatus = cublasSaxpy(cublasHandle, N, &a, p, 1, x, 1);
na = -a;
cublasStatus = cublasSaxpy(cublasHandle, N, &na, Ax, 1, r, 1);
r0 = r1;
cublasStatus = cublasSdot(cublasHandle, N, r, 1, r, 1, &r1);
cudaThreadSynchronize();
printf("iteration = %3d, residual = %e\n", k, sqrt(r1));
k++;
}
printf("Final residual: %e\n",sqrt(r1));
fprintf(stdout,"&&&& uvm_cg test %s\n", (sqrt(r1) < tol) ? "PASSED" : "FAILED");
float rsum, diff, err = 0.0;
for (int i = 0; i < N; i++)
{
rsum = 0.0;
for (int j = I[i]; j < I[i+1]; j++)
{
rsum += val[j]*x[J[j]];
}
diff = fabs(rsum - rhs[i]);
if (diff > err)
{
err = diff;
}
}
cusparseDestroy(cusparseHandle);
cublasDestroy(cublasHandle);
cudaFree(I);
cudaFree(J);
cudaFree(val);
cudaFree(x);
cudaFree(r);
cudaFree(p);
cudaFree(Ax);
cudaDeviceReset();
printf("Test Summary: Error amount = %f, result = %s\n", err, (k <= max_iter) ? "SUCCESS" : "FAILURE");
exit((k <= max_iter) ? EXIT_SUCCESS : EXIT_FAILURE);
}
int main(int argc, char **argv)
{
int M = 0, N = 0, nz = 0, *I = NULL, *J = NULL;
float *val = NULL;
const float tol = 1e-5f;
const int max_iter = 10000;
float *x;
float *rhs;
float a, b, na, r0, r1;
int *d_col, *d_row;
float *d_val, *d_x, dot;
float *d_r, *d_p, *d_Ax;
int k;
float alpha, beta, alpham1;
shrQAStart(argc, argv);
// This will pick the best possible CUDA capable device
cudaDeviceProp deviceProp;
int devID = findCudaDevice(argc, (const char **)argv);
if (devID < 0) {
printf("exiting...\n");
shrQAFinishExit(argc, (const char **)argv, QA_PASSED);
exit(0);
}
checkCudaErrors( cudaGetDeviceProperties(&deviceProp, devID) );
// Statistics about the GPU device
printf("> GPU device has %d Multi-Processors, SM %d.%d compute capabilities\n\n",
deviceProp.multiProcessorCount, deviceProp.major, deviceProp.minor);
int version = (deviceProp.major * 0x10 + deviceProp.minor);
if(version < 0x11)
{
printf("%s: requires a minimum CUDA compute 1.1 capability\n", sSDKname);
cudaDeviceReset();
shrQAFinishExit(argc, (const char **)argv, QA_PASSED);
}
/* Generate a random tridiagonal symmetric matrix in CSR format */
M = N = 1048576;
nz = (N-2)*3 + 4;
I = (int*)malloc(sizeof(int)*(N+1));
J = (int*)malloc(sizeof(int)*nz);
val = (float*)malloc(sizeof(float)*nz);
genTridiag(I, J, val, N, nz);
x = (float*)malloc(sizeof(float)*N);
rhs = (float*)malloc(sizeof(float)*N);
for (int i = 0; i < N; i++) {
rhs[i] = 1.0;
x[i] = 0.0;
}
/* Get handle to the CUBLAS context */
cublasHandle_t cublasHandle = 0;
cublasStatus_t cublasStatus;
cublasStatus = cublasCreate(&cublasHandle);
if ( checkCublasStatus (cublasStatus, "!!!! CUBLAS initialization error\n") ) return EXIT_FAILURE;
/* Get handle to the CUSPARSE context */
cusparseHandle_t cusparseHandle = 0;
cusparseStatus_t cusparseStatus;
cusparseStatus = cusparseCreate(&cusparseHandle);
if ( checkCusparseStatus (cusparseStatus, "!!!! CUSPARSE initialization error\n") ) return EXIT_FAILURE;
cusparseMatDescr_t descr = 0;
cusparseStatus = cusparseCreateMatDescr(&descr);
if ( checkCusparseStatus (cusparseStatus, "!!!! CUSPARSE cusparseCreateMatDescr error\n") ) return EXIT_FAILURE;
cusparseSetMatType(descr,CUSPARSE_MATRIX_TYPE_GENERAL);
cusparseSetMatIndexBase(descr,CUSPARSE_INDEX_BASE_ZERO);
checkCudaErrors( cudaMalloc((void**)&d_col, nz*sizeof(int)) );
checkCudaErrors( cudaMalloc((void**)&d_row, (N+1)*sizeof(int)) );
checkCudaErrors( cudaMalloc((void**)&d_val, nz*sizeof(float)) );
checkCudaErrors( cudaMalloc((void**)&d_x, N*sizeof(float)) );
checkCudaErrors( cudaMalloc((void**)&d_r, N*sizeof(float)) );
checkCudaErrors( cudaMalloc((void**)&d_p, N*sizeof(float)) );
checkCudaErrors( cudaMalloc((void**)&d_Ax, N*sizeof(float)) );
cudaMemcpy(d_col, J, nz*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_row, I, (N+1)*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_val, val, nz*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_x, x, N*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_r, rhs, N*sizeof(float), cudaMemcpyHostToDevice);
alpha = 1.0;
alpham1 = -1.0;
beta = 0.0;
r0 = 0.;
cusparseScsrmv(cusparseHandle,CUSPARSE_OPERATION_NON_TRANSPOSE, N, N, nz, &alpha, descr, d_val, d_row, d_col, d_x, &beta, d_Ax);
cublasSaxpy(cublasHandle, N, &alpham1, d_Ax, 1, d_r, 1);
cublasStatus = cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1);
k = 1;
while (r1 > tol*tol && k <= max_iter) {
if (k > 1) {
b = r1 / r0;
cublasStatus = cublasSscal(cublasHandle, N, &b, d_p, 1);
cublasStatus = cublasSaxpy(cublasHandle, N, &alpha, d_r, 1, d_p, 1);
} else {
cublasStatus = cublasScopy(cublasHandle, N, d_r, 1, d_p, 1);
}
cusparseScsrmv(cusparseHandle, CUSPARSE_OPERATION_NON_TRANSPOSE, N, N, nz, &alpha, descr, d_val, d_row, d_col, d_p, &beta, d_Ax);
cublasStatus = cublasSdot(cublasHandle, N, d_p, 1, d_Ax, 1, &dot);
a = r1 / dot;
cublasStatus = cublasSaxpy(cublasHandle, N, &a, d_p, 1, d_x, 1);
na = -a;
cublasStatus = cublasSaxpy(cublasHandle, N, &na, d_Ax, 1, d_r, 1);
r0 = r1;
cublasStatus = cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1);
cudaThreadSynchronize();
printf("iteration = %3d, residual = %e\n", k, sqrt(r1));
k++;
}
cudaMemcpy(x, d_x, N*sizeof(float), cudaMemcpyDeviceToHost);
float rsum, diff, err = 0.0;
for (int i = 0; i < N; i++) {
rsum = 0.0;
for (int j = I[i]; j < I[i+1]; j++) {
rsum += val[j]*x[J[j]];
}
diff = fabs(rsum - rhs[i]);
if (diff > err) err = diff;
}
cusparseDestroy(cusparseHandle);
cublasDestroy(cublasHandle);
free(I);
free(J);
free(val);
free(x);
free(rhs);
cudaFree(d_col);
cudaFree(d_row);
cudaFree(d_val);
cudaFree(d_x);
cudaFree(d_r);
cudaFree(d_p);
cudaFree(d_Ax);
cudaDeviceReset();
printf("Test Summary: Error amount = %f\n", err);
shrQAFinishExit(argc, (const char **)argv, (k <= max_iter) ? QA_PASSED : QA_FAILED );
}
