void plan_fft(FFT_plans *plans, Arrays *arr,
Detector_settings *sett, Command_line_opts *opts)
{
/*
############ FFT Plans ################
*/
//arrlen is maximum of Ninterp and fftpad*nfft
arr->arr_len = (sett->fftpad * sett->nfft > sett->Ninterp ? sett->fftpad * sett->nfft : sett->Ninterp);
CudaSafeCall ( cudaMalloc((void**)&arr->cu_xa, arr->arr_len*sizeof(cufftDoubleComplex)) );
CudaSafeCall ( cudaMalloc((void**)&arr->cu_xb, arr->arr_len*sizeof(cufftDoubleComplex)) );
CudaSafeCall ( cudaMalloc((void**)&arr->cu_xar, arr->arr_len*sizeof(cufftDoubleComplex)) );
CudaSafeCall ( cudaMalloc((void**)&arr->cu_xbr, arr->arr_len*sizeof(cufftDoubleComplex)) );
CudaSafeCall ( cudaMalloc((void**)&arr->cu_xa_f, arr->arr_len*sizeof(COMPLEX_TYPE)) );
CudaSafeCall ( cudaMalloc((void**)&arr->cu_xb_f, arr->arr_len*sizeof(COMPLEX_TYPE)) );
CudaSafeCall ( cudaMalloc((void**)&arr->cu_xar_f, arr->arr_len*sizeof(COMPLEX_TYPE)) );
CudaSafeCall ( cudaMalloc((void**)&arr->cu_xbr_f, arr->arr_len*sizeof(COMPLEX_TYPE)) );
if (opts->fftinterp == INT) { //interbinning
CudaSafeCall ( cudaMalloc((void**)&arr->cu_xa2_f, sett->nfft*sizeof(COMPLEX_TYPE)) );
CudaSafeCall ( cudaMalloc((void**)&arr->cu_xb2_f, sett->nfft*sizeof(COMPLEX_TYPE)) );
}
sett->nfftf = sett->fftpad*sett->nfft;
if (opts->fftinterp == INT) { //interbinning
cufftPlan1d( &(plans->plan),
sett->nfft,
CUFFT_TRANSFORM_TYPE, 1);
} else { //fft & zero padding
cufftPlan1d( &(plans->plan),
sett->nfftf,
CUFFT_TRANSFORM_TYPE, 1);
}
//plans for interpolation with splines
cufftPlan1d(&(plans->pl_int),
sett->nfft,
CUFFT_Z2Z, 1);
cufftPlan1d(&(plans->pl_inv),
sett->Ninterp,
CUFFT_Z2Z, 1);
/*
############ FFT plans end ################
*/
}
/*!
computes the N-point DFT of signal X and stores in Y using CUDA's FFT library
*/
bool cuda_dft(cuComplex *Y, cuComplex *X, float scale, int N) {
size_t bytes = (size_t)N * sizeof(cuComplex);
cuComplex *Y_gpu, *X_gpu;
cudaMalloc((void **)&Y_gpu, bytes);
cudaMalloc((void **)&X_gpu, bytes);
cudaMemcpy(Y_gpu, Y, bytes, cudaMemcpyHostToDevice);
cudaMemcpy(X_gpu, X, bytes, cudaMemcpyHostToDevice);
cufftHandle plan;
cufftPlan1d(&plan, N, CUFFT_C2C, 1);
cufftExecC2C(plan, X_gpu, Y_gpu, CUFFT_FORWARD);
cufftDestroy(plan);
cudaMemcpy(Y, Y_gpu, bytes, cudaMemcpyDeviceToHost);
cudaFree(Y_gpu);
cudaFree(X_gpu);
for (int n = 0; n < N; n++) {
Y[n].x *= scale;
Y[n].y *= scale;
}
return true;
}
extern "C" void cuda_fft(double *d_data, int Lx, int Ny, void *stream)
{
cufftHandle plan;
cufftPlan1d(&plan, Lx, CUFFT_Z2Z, Ny);
cufftSetStream(plan, (cudaStream_t)stream);
cufftExecZ2Z(plan, (cufftDoubleComplex*)d_data, (cufftDoubleComplex*)d_data,CUFFT_FORWARD);
cufftDestroy(plan);
}
extern "C" void cuda_fft(float *d_data, int Lx, int Ny, void *stream)
{
cufftHandle plan;
cufftPlan1d(&plan, Lx, CUFFT_C2C, Ny);
cufftSetStream(plan, (cudaStream_t)stream);
cufftExecC2C(plan, (cufftComplex*)d_data, (cufftComplex*)d_data,CUFFT_FORWARD);
cufftDestroy(plan);
}
void g_fwdfft(QSP_ARG_DECL Data_Obj *dst_dp, Data_Obj *src1_dp)
{
//Variable declarations
int NX = 256;
//int BATCH = 10;
int BATCH = 1;
cufftResult_t status;
//Declare plan for FFT
cufftHandle plan;
//cufftComplex *data;
//cufftComplex *result;
void *data;
void *result;
cudaError_t drv_err;
//Allocate RAM
//cutilSafeCall(cudaMalloc(&data, sizeof(cufftComplex)*NX*BATCH));
//cutilSafeCall(cudaMalloc(&result, sizeof(cufftComplex)*NX*BATCH));
drv_err = cudaMalloc(&data, sizeof(cufftComplex)*NX*BATCH);
if( drv_err != cudaSuccess ){
WARN("error allocating cuda data buffer for fft!?");
return;
}
drv_err = cudaMalloc(&result, sizeof(cufftComplex)*NX*BATCH);
if( drv_err != cudaSuccess ){
WARN("error allocating cuda result buffer for fft!?");
// BUG clean up previous malloc...
return;
}
//Create plan for FFT
status = cufftPlan1d(&plan, NX, CUFFT_C2C, BATCH);
if (status != CUFFT_SUCCESS) {
sprintf(ERROR_STRING, "Error in cufftPlan1d: %s\n", getCUFFTError(status));
NWARN(ERROR_STRING);
}
//Run forward fft on data
status = cufftExecC2C(plan, (cufftComplex *)data,
(cufftComplex *)result, CUFFT_FORWARD);
if (status != CUFFT_SUCCESS) {
sprintf(ERROR_STRING, "Error in cufftExecC2C: %s\n", getCUFFTError(status));
NWARN(ERROR_STRING);
}
//Run inverse fft on data
/*status = cufftExecC2C(plan, data, result, CUFFT_INVERSE);
if (status != CUFFT_SUCCESS)
{
sprintf(ERROR_STRING, "Error in cufftExecC2C: %s\n", getCUFFTError(status));
NWARN(ERROR_STRING);
}*/
//Free resources
cufftDestroy(plan);
cudaFree(data);
}
/*
* Function to be called in thread managing host operations and invoking kernels
*/
void* host_thread(void* passing_ptr) {
DataArray* data_arr_ptr = (DataArray*) passing_ptr;
alloc_data_host(data_arr_ptr);
printf("data allocated by host thread\n");
//printf("data filling by host thread\n");
for (uint64_t ii = 0; ii < data_arr_ptr->size; ii++) {
(*(data_arr_ptr->data_r))[ii] = ii;
(*(data_arr_ptr->data_k))[ii] = data_arr_ptr->size-ii;
}
printf("data filled by host thread\n");
// synchronize after allocating memory - streams should be created, mem on device ready for copying
pthread_barrier_wait (&barrier);
printf("1st barier host thread - allocating mem on cpu\n");
// here we can make cufft plan, for example
cufftHandle plan_forward;
cufftPlan1d(&plan_forward, N, CUFFT_Z2Z, 1);
// synchornize after ... - data should be copyied on device
pthread_barrier_wait (&barrier);
printf("2nd barier host thread - \n");
// run some computations
cufftExecZ2Z(plan_forward, *(data_arr_ptr->data_r_dev), *(data_arr_ptr->data_k_dev), CUFFT_FORWARD);
printf("cufft done\n");
// synchornize after computations -
cudaDeviceSynchronize(); // should be used on
pthread_barrier_wait (&barrier);
printf("3rd barier host thread - \n");
// synchornize after computations -
pthread_barrier_wait (&barrier);
printf("4th barier host thread - \n");
printf("data visible in host thread:\n");
/*for (uint64_t ii = 0; ii < (data_arr_ptr->size <= 32) ? data_arr_ptr->size : 32 ; ii++) {
printf("%lu.\t",ii);
printf("%lf + %lfj\t", creal( (*(data_arr_ptr->data_r))[ii] ), cimag( (*(data_arr_ptr->data_r))[ii] ));
printf("%lf + %lfj\n", creal( (*(data_arr_ptr->data_k))[ii] ), cimag( (*(data_arr_ptr->data_k))[ii] ));
}*/
printf("closing host thread\n");
pthread_exit(NULL);
}
void cuda_make_cufft_plan(cufftHandle *plan, int type, size_t n, size_t batch)
{
cufftType cufft_type = CUFFT_C2C;
if (type == 0)
cufft_type = CUFFT_C2C;
else if (type == 1)
cufft_type = CUFFT_R2C;
else if (type == 2)
cufft_type = CUFFT_C2R;
cudaError_t status = cufftPlan1d(plan, n, cufft_type, batch);
check_error(status);
}
void
Fastconv_base<D, T, C>::fconv
(T const* in, T const* kernel, T* out, length_type rows, length_type columns, bool transform_kernel)
{
// convert pointers to types the CUFFT library accepts
typedef cufftComplex ctype;
ctype* d_out = reinterpret_cast<ctype*>(out);
ctype* d_kernel = const_cast<ctype*>(reinterpret_cast<ctype const*>(kernel));
ctype* d_in = const_cast<ctype*>(reinterpret_cast<ctype const*>(in));
cufftHandle plan;
if (transform_kernel)
{
// Create a 1D FFT plan and transform the kernel
cufftPlan1d(&plan, columns, CUFFT_C2C, 1);
cufftExecC2C(plan, d_kernel, d_kernel, CUFFT_FORWARD);
cufftDestroy(plan);
}
// Create a FFTM plan
cufftPlan1d(&plan, columns, CUFFT_C2C, rows);
// transform the data
cufftExecC2C(plan, d_in, d_in, CUFFT_FORWARD);
// convolve with kernel, combine with scaling needed for inverse FFT
typedef typename impl::scalar_of<T>::type scalar_type;
scalar_type scale = 1 / static_cast<scalar_type>(columns);
if (D == 1)
vmmuls_row(kernel, in, out, scale, rows, columns);
else
mmmuls(kernel, in, out, scale, rows, columns);
// inverse transform the signal
cufftExecC2C(plan, d_out, d_out, CUFFT_INVERSE);
cufftDestroy(plan);
}
/*
* Class: jcuda_jcufft_JCufft
* Method: cufftPlan1dNative
* Signature: (Ljcuda/jcufft/JCufftHandle;III)I
*/
JNIEXPORT jint JNICALL Java_jcuda_jcufft_JCufft_cufftPlan1dNative
(JNIEnv *env, jclass cla, jobject handle, jint nx, jint type, jint batch)
{
if (handle == NULL)
{
ThrowByName(env, "java/lang/NullPointerException", "Parameter 'handle' is null for cufftPlan1d");
return JCUFFT_INTERNAL_ERROR;
}
Logger::log(LOG_TRACE, "Creating 1D plan for %d elements of type %d\n", nx, type);
cufftHandle plan = env->GetIntField(handle, cufftHandle_plan);
cufftResult result = cufftPlan1d(&plan, nx, getCufftType(type), batch);
env->SetIntField(handle, cufftHandle_plan, plan);
return result;
}
void
mexFunction( int nlhs, mxArray *plhs[],
int nrhs, const mxArray *prhs[] )
{
if (nrhs!=4)
mexErrMsgTxt("Wrong number of arguments");
cufftHandle plan = (cufftHandle) mxGetScalar(prhs[0]);
int nx = (int) mxGetScalar(prhs[1]);
cufftType_t type = (cufftType_t) ((int) mxGetScalar(prhs[2]));
int batch = (int) mxGetScalar(prhs[3]);
cufftResult status = cufftPlan1d(&plan, nx, type, batch);
plhs[0] = mxCreateDoubleScalar(status);
if (nlhs>1)
plhs[1] = mxCreateDoubleScalar(plan);
}
oskar_FFT* oskar_fft_create(int precision, int location, int num_dim,
int dim_size, int batch_size_1d, int* status)
{
int i;
oskar_FFT* h = (oskar_FFT*) calloc(1, sizeof(oskar_FFT));
#ifndef OSKAR_HAVE_CUDA
if (location == OSKAR_GPU) location = OSKAR_CPU;
#endif
#ifndef OSKAR_HAVE_OPENCL
if (location & OSKAR_CL) location = OSKAR_CPU;
#endif
h->precision = precision;
h->location = location;
h->num_dim = num_dim;
h->dim_size = dim_size;
h->ensure_consistent_norm = 1;
h->num_cells_total = (size_t) dim_size;
for (i = 1; i < num_dim; ++i) h->num_cells_total *= (size_t) dim_size;
if (location == OSKAR_CPU)
{
int len = 4 * dim_size +
2 * (int)(log((double)dim_size) / log(2.0)) + 8;
h->fftpack_wsave = oskar_mem_create(precision, location, len, status);
if (num_dim == 1)
{
(void) batch_size_1d;
*status = OSKAR_ERR_FUNCTION_NOT_AVAILABLE;
}
else if (num_dim == 2)
{
if (precision == OSKAR_DOUBLE)
oskar_fftpack_cfft2i(dim_size, dim_size,
oskar_mem_double(h->fftpack_wsave, status));
else
oskar_fftpack_cfft2i_f(dim_size, dim_size,
oskar_mem_float(h->fftpack_wsave, status));
}
else
*status = OSKAR_ERR_INVALID_ARGUMENT;
h->fftpack_work = oskar_mem_create(precision, location,
2 * h->num_cells_total, status);
}
else if (location == OSKAR_GPU)
{
#ifdef OSKAR_HAVE_CUDA
if (num_dim == 1)
cufftPlan1d(&h->cufft_plan, dim_size,
((precision == OSKAR_DOUBLE) ? CUFFT_Z2Z : CUFFT_C2C),
batch_size_1d);
else if (num_dim == 2)
cufftPlan2d(&h->cufft_plan, dim_size, dim_size,
((precision == OSKAR_DOUBLE) ? CUFFT_Z2Z : CUFFT_C2C));
else
*status = OSKAR_ERR_INVALID_ARGUMENT;
#endif
}
else if (location & OSKAR_CL)
{
#ifdef OSKAR_HAVE_OPENCL
*status = OSKAR_ERR_FUNCTION_NOT_AVAILABLE;
#endif
}
else
*status = OSKAR_ERR_BAD_LOCATION;
return h;
}
/**
Constructor.
The constructor creates a CUFFT plan with the given size.
@param size0 requested size of CUFFT plan
@param batch number of 1D transforms
*/
inline Plan(size_t size0, int batch = 1)
{
CUFFT_CHECK(cufftPlan1d(&plan, size0, CUFFT_C2R, batch));
}
/**
Constructor.
The constructor creates a CUFFT plan with the given size.
@param size requested size of CUFFT plan
@param batch number of 1D transforms
*/
inline Plan(const Size<1> &size, int batch = 1)
{
CUFFT_CHECK(cufftPlan1d(&plan, size[0], CUFFT_C2R, batch));
}
void
Fastconv_base<D, T, ComplexFmt>::fconv
(T const* in, T const* kernel, T* out, length_type rows, length_type columns, bool transform_kernel)
{
size_t kernel_size = (D == 1) ? columns : rows * columns;
// allocate device memory and copy input and kernel over from host
Device_storage<T> dev_out(rows * columns);
Device_storage<T> dev_kernel(kernel_size);
Device_storage<T> dev_in(rows * columns);
// If the kernel is a matrix, it is assumed to be row-major and dense.
// As a result, it can be copied as one contiguous chunk.
cudaMemcpy(
dev_kernel.data(),
kernel,
kernel_size * sizeof(T),
cudaMemcpyHostToDevice);
ASSERT_CUDA_OK();
// Transfer the input (row major, dense)
cudaMemcpy(
dev_in.data(),
in,
rows * columns * sizeof(T),
cudaMemcpyHostToDevice);
ASSERT_CUDA_OK();
// convert pointers to types the CUFFT library accepts
typedef cufftComplex ctype;
ctype* d_out = reinterpret_cast<ctype*>(dev_out.data());
ctype* d_kernel = reinterpret_cast<ctype*>(dev_kernel.data());
ctype* d_in = reinterpret_cast<ctype*>(dev_in.data());
cufftHandle plan;
if (transform_kernel)
{
// Create a 1D FFT plan and transform the kernel
cufftPlan1d(&plan, columns, CUFFT_C2C, 1);
cufftExecC2C(plan, d_kernel, d_kernel, CUFFT_FORWARD);
cufftDestroy(plan);
}
// Create a FFTM plan
cufftPlan1d(&plan, columns, CUFFT_C2C, rows);
// transform the data
cufftExecC2C(plan, d_in, d_in, CUFFT_FORWARD);
// convolve with kernel, combine with scaling needed for inverse FFT
typedef typename impl::Scalar_of<T>::type scalar_type;
scalar_type scale = 1 / static_cast<scalar_type>(columns);
if (D == 1)
vmmuls_row_cc(d_kernel, d_in, d_out, scale, rows, columns);
else
mmmuls_cc(d_kernel, d_in, d_out, scale, rows, columns);
// inverse transform the signal
cufftExecC2C(plan, d_out, d_out, CUFFT_INVERSE);
cufftDestroy(plan);
// Move data back to the host from the output buffer
cudaMemcpy(
out,
dev_out.data(),
rows * columns * sizeof(T),
cudaMemcpyDeviceToHost);
ASSERT_CUDA_OK();
}
int main2(int sockfd)
{
cufftHandle plan;
cufftComplex *devPtr;
cufftReal indata[NX*BATCH];
cufftComplex data[NX*BATCH];
int i,timer,j,k;
char fname[15];
FILE *f;
#define BUFSIZE (21*4096*sizeof(int))
int buffer[BUFSIZE];
int p,nread;
f = fopen("21-4096","rb");
nread=fread(buffer,BUFSIZE,1,f);
printf("nread=%i\n",nread);
fclose(f);
i=0;
for (j=0;j<BATCH;j++) {
for (k=0;k<NX;k++) {
data[j*NX+k].x = buffer[j*NX+k];
data[j*NX+k].y = 0;
}
}
//f=fopen("y.txt","r");
/* source data creation */
//int sockfd = myconnect();
//printf("connected\n");
/* WORKING!!!!!!!!
i=0;
for (j=0;j<BATCH;j++) {
sprintf(fname,"%i.txt",j);
printf("%s\n",fname);
f = fopen(fname,"r");
for (k=0;k<NX;k++) {
fscanf(f,"%i\n",&p);
data[j*NX+k].x = p;
data[j*NX+k].y = 0;
}
fclose(f);
*/
/*
for(i= 0 ; i < NX*BATCH ; i++){
//fscanf(f,"%i\n",&p);
//data[i].x= p;
data[i].x= 1.0f;
//printf("%f\n",data[i].x);
data[i].y = 0.0f;
}
//fclose(f)
*/
//}
/* creates 1D FFT plan */
cufftPlan1d(&plan, NX, CUFFT_C2C, BATCH);
/*
cutCreateTimer(&timer);
cutResetTimer(timer);
cutStartTimer(timer);
*/
/* GPU memory allocation */
cudaMalloc((void**)&devPtr, sizeof(cufftComplex)*NX*BATCH);
/* transfer to GPU memory */
cudaMemcpy(devPtr, data, sizeof(cufftComplex)*NX*BATCH, cudaMemcpyHostToDevice);
/* executes FFT processes */
cufftExecC2C(plan, devPtr, devPtr, CUFFT_FORWARD);
/* executes FFT processes (inverse transformation) */
//cufftExecC2C(plan, devPtr, devPtr, CUFFT_INVERSE);
/* transfer results from GPU memory */
cudaMemcpy(data, devPtr, sizeof(cufftComplex)*NX*BATCH, cudaMemcpyDeviceToHost);
/* deletes CUFFT plan */
cufftDestroy(plan);
/* frees GPU memory */
cudaFree(devPtr);
/*
cudaThreadSynchronize();
cutStopTimer(timer);
printf("%f\n",cutGetTimerValue(timer)/(float)1000);
cutDeleteTimer(timer);
*/
/*
float mag;
for(i = 0 ; i < NX*BATCH ; i++){
//printf("data[%d] %f %f\n", i, data[i].x, data[i].y);
//printf("%f\n", data[i].x);
mag = sqrtf(data[i].x*data[i].x+data[i].y*data[i].y)*2.0/NX;
printf("%f\n",mag);
}
*/
/*
// save as text file
float mag;
i=0;
for (j=0;j<BATCH;j++) {
sprintf(fname,"%i-mag.txt",j);
printf("%s\n",fname);
f = fopen(fname,"w");
for (k=0;k<NX;k++) {
//fscanf(f,"%i\n",&p);
if (k>50)
continue;
i = j*NX+k;
mag = sqrtf(data[i].x*data[i].x+data[i].y*data[i].y)*2.0/NX;
fprintf(f,"%f\n",mag);
}
fclose(f);
}
*/
float mag;
i=0;
float mags[NX];
int magsint[NX*BATCH];
memset(magsint,0,sizeof(int)*NX*BATCH);
int u = 0;
printf("%f %f %f %f\n",data[0].x,data[1].x,data[2].x,data[3].x);
//printf("%i %i %i %i\n",magsint[0],magsint[1],magsint[2],magsint[3]);
// f = fopen("ffts.bin","wb");
for (j=0;j<BATCH;j++) {
// sprintf(fname,"%i-bin.dat",j);
// printf("%s\n",fname);
for (k=0;k<NX;k++) {
//fscanf(f,"%i\n",&p);
if (k>50)
continue;
i = j*NX+k;
mags[k] = sqrtf(data[i].x*data[i].x+data[i].y*data[i].y)*2.0/NX;
magsint[u]=mags[k] ;
u++;
//fprintf(f,"%f\n",mag);
}
//f = fopen(fname,"wb");
// fwrite(magsint,sizeof(int)*50,1,f);
}
int n;
n = write(sockfd,magsint,sizeof(int)*BATCH*50);
printf("%i %i %i %i\n",magsint[0],magsint[1],magsint[2],magsint[3]);
printf("send ok, size: %i\n",n);
//fclose(f);
return 0;
}
int main(int argc, char *argv[]) {
int i;
struct timeval begin, end;
int size;
size_t bytes;
int n = 0, m = 0;
STARPUFFT(plan) plan;
#ifdef STARPU_HAVE_FFTW
_FFTW(plan) fftw_plan;
#endif
#ifdef STARPU_USE_CUDA
cufftHandle cuda_plan;
cudaError_t cures;
#endif
double timing;
if (argc < 2 || argc > 3) {
fprintf(stderr,"need one or two size of vector\n");
exit(EXIT_FAILURE);
}
starpu_init(NULL);
if (argc == 2) {
n = atoi(argv[1]);
/* 1D */
size = n;
} else if (argc == 3) {
n = atoi(argv[1]);
m = atoi(argv[2]);
/* 2D */
size = n * m;
} else {
assert(0);
}
bytes = size * sizeof(STARPUFFT(complex));
STARPUFFT(complex) *in = STARPUFFT(malloc)(size * sizeof(*in));
starpu_srand48(0);
for (i = 0; i < size; i++)
in[i] = starpu_drand48() + I * starpu_drand48();
STARPUFFT(complex) *out = STARPUFFT(malloc)(size * sizeof(*out));
#ifdef STARPU_HAVE_FFTW
STARPUFFT(complex) *out_fftw = STARPUFFT(malloc)(size * sizeof(*out_fftw));
#endif
#ifdef STARPU_USE_CUDA
STARPUFFT(complex) *out_cuda = malloc(size * sizeof(*out_cuda));
#endif
if (argc == 2) {
plan = STARPUFFT(plan_dft_1d)(n, SIGN, 0);
#ifdef STARPU_HAVE_FFTW
fftw_plan = _FFTW(plan_dft_1d)(n, in, out_fftw, SIGN, FFTW_ESTIMATE);
#endif
#ifdef STARPU_USE_CUDA
if (cufftPlan1d(&cuda_plan, n, _CUFFT_C2C, 1) != CUFFT_SUCCESS)
printf("erf\n");
#endif
} else if (argc == 3) {
plan = STARPUFFT(plan_dft_2d)(n, m, SIGN, 0);
#ifdef STARPU_HAVE_FFTW
fftw_plan = _FFTW(plan_dft_2d)(n, m, in, out_fftw, SIGN, FFTW_ESTIMATE);
#endif
#ifdef STARPU_USE_CUDA
STARPU_ASSERT(cufftPlan2d(&cuda_plan, n, m, _CUFFT_C2C) == CUFFT_SUCCESS);
#endif
} else {
assert(0);
}
#ifdef STARPU_HAVE_FFTW
gettimeofday(&begin, NULL);
_FFTW(execute)(fftw_plan);
gettimeofday(&end, NULL);
_FFTW(destroy_plan)(fftw_plan);
timing = (double)((end.tv_sec - begin.tv_sec)*1000000 + (end.tv_usec - begin.tv_usec));
printf("FFTW took %2.2f ms (%2.2f MB/s)\n\n", timing/1000, bytes/timing);
#endif
#ifdef STARPU_USE_CUDA
gettimeofday(&begin, NULL);
if (cufftExecC2C(cuda_plan, (cufftComplex*) in, (cufftComplex*) out_cuda, CUFFT_FORWARD) != CUFFT_SUCCESS)
printf("erf2\n");
if ((cures = cudaThreadSynchronize()) != cudaSuccess)
STARPU_CUDA_REPORT_ERROR(cures);
gettimeofday(&end, NULL);
cufftDestroy(cuda_plan);
timing = (double)((end.tv_sec - begin.tv_sec)*1000000 + (end.tv_usec - begin.tv_usec));
printf("CUDA took %2.2f ms (%2.2f MB/s)\n\n", timing/1000, bytes/timing);
#endif
STARPUFFT(execute)(plan, in, out);
STARPUFFT(showstats)(stdout);
STARPUFFT(destroy_plan)(plan);
printf("\n");
#if 0
for (i = 0; i < 16; i++)
printf("(%f,%f) ", cimag(in[i]), creal(in[i]));
printf("\n\n");
for (i = 0; i < 16; i++)
printf("(%f,%f) ", cimag(out[i]), creal(out[i]));
printf("\n\n");
#ifdef STARPU_HAVE_FFTW
for (i = 0; i < 16; i++)
printf("(%f,%f) ", cimag(out_fftw[i]), creal(out_fftw[i]));
printf("\n\n");
#endif
#endif
#ifdef STARPU_HAVE_FFTW
{
double max = 0., tot = 0., norm = 0., normdiff = 0.;
for (i = 0; i < size; i++) {
double diff = cabs(out[i]-out_fftw[i]);
double diff2 = diff * diff;
double size = cabs(out_fftw[i]);
double size2 = size * size;
if (diff > max)
max = diff;
tot += diff;
normdiff += diff2;
norm += size2;
}
fprintf(stderr, "\nmaximum difference %g\n", max);
fprintf(stderr, "average difference %g\n", tot / size);
fprintf(stderr, "difference norm %g\n", sqrt(normdiff));
double relmaxdiff = max / sqrt(norm);
fprintf(stderr, "relative maximum difference %g\n", relmaxdiff);
double relavgdiff = (tot / size) / sqrt(norm);
fprintf(stderr, "relative average difference %g\n", relavgdiff);
if (!strcmp(TYPE, "f") && (relmaxdiff > 1e-8 || relavgdiff > 1e-8))
return EXIT_FAILURE;
if (!strcmp(TYPE, "") && (relmaxdiff > 1e-16 || relavgdiff > 1e-16))
return EXIT_FAILURE;
}
#endif
#ifdef STARPU_USE_CUDA
{
double max = 0., tot = 0., norm = 0., normdiff = 0.;
for (i = 0; i < size; i++) {
double diff = cabs(out_cuda[i]-out_fftw[i]);
double diff2 = diff * diff;
double size = cabs(out_fftw[i]);
double size2 = size * size;
if (diff > max)
max = diff;
tot += diff;
normdiff += diff2;
norm += size2;
}
fprintf(stderr, "\nmaximum difference %g\n", max);
fprintf(stderr, "average difference %g\n", tot / size);
fprintf(stderr, "difference norm %g\n", sqrt(normdiff));
double relmaxdiff = max / sqrt(norm);
fprintf(stderr, "relative maximum difference %g\n", relmaxdiff);
double relavgdiff = (tot / size) / sqrt(norm);
fprintf(stderr, "relative average difference %g\n", relavgdiff);
if (!strcmp(TYPE, "f") && (relmaxdiff > 1e-8 || relavgdiff > 1e-8))
return EXIT_FAILURE;
if (!strcmp(TYPE, "") && (relmaxdiff > 1e-16 || relavgdiff > 1e-16))
return EXIT_FAILURE;
}
#endif
STARPUFFT(free)(in);
STARPUFFT(free)(out);
#ifdef STARPU_HAVE_FFTW
STARPUFFT(free)(out_fftw);
#endif
#ifdef STARPU_USE_CUDA
free(out_cuda);
#endif
starpu_shutdown();
return EXIT_SUCCESS;
}
int main(void)
{
//std::cout << "Generating a time series on device "<< tim.get_nsamps() << std::endl;
//DeviceTimeSeries<float> d_tim(8388608);
//d_tim.set_tsamp(0.000064);
TimeSeries<float> tim;
tim.from_file("/lustre/home/ebarr/Soft/peasoup/tmp5.tim");
DeviceTimeSeries<float> d_tim(tim);
unsigned int size = d_tim.get_nsamps();
TimeSeriesFolder folder(size);
//DeviceTimeSeries<float> d_tim_r(fft_size); //<----for resampled data
//TimeDomainResampler resampler;
float* folded_buffer;
cudaError_t error;
cufftResult result;
error = cudaMalloc((void**)&folded_buffer, sizeof(float)*size);
ErrorChecker::check_cuda_error(error);
unsigned nints = 64;
unsigned nbins = 32;
cufftComplex* fft_out;
error = cudaMalloc((void**)&fft_out, sizeof(cufftComplex)*nints*nbins);
cufftHandle plan;
result = cufftPlan1d(&plan,nbins,CUFFT_R2C, nints);
ErrorChecker::check_cufft_error(result);
Stopwatch timer;
FoldedSubints<float> folded_array(nbins,nints);
//folder.fold(d_tim,folded_array,0.007453079228);
std::cout << "made it here" << std::endl;
FoldOptimiser optimiser(nbins,nints);
timer.start();
for (int ii=0;ii<1;ii++){
//FoldedSubints<float> folded_array(nbins,nints);
folder.fold(d_tim,folded_array,0.007453099228);
Utils::dump_device_buffer<float>(folded_array.get_data(),nints*nbins,"original_fold.bin");
optimiser.optimise(folded_array);
}
timer.stop();
/*
float* temp = new float [nints*nbins];
cudaMemcpy(temp,folded_buffer,nints*nbins*sizeof(float),cudaMemcpyDeviceToHost);
ErrorChecker::check_cuda_error();
for (int ii=0;ii<nints*nbins;ii++)
std::cout << temp[ii] << std::endl;
*/
std::cout << "Total execution time (s): " << timer.getTime()<<std::endl;
std::cout << "Average execution time (s): " << timer.getTime()/1000.0 << std::endl;
return 0;
}
cufftResult WINAPI wine_cufftPlan1d( cufftHandle *plan, int nx, cufftType type, int batch ){
WINE_TRACE("\n");
return cufftPlan1d( plan, nx, type, batch );
}
void generate_PL_lines_fft(double PLindex, int nr_lines,int linelength,
icl_buffer *y_buffer, //double *y, // out
int fielddim_z)
{
cufftHandle fftw_plan_transfer_fw, fftw_plan_noise_fw, fftw_plan_transfer_bw;
// FFT preparation
const size_t out_size = sizeof(cufftDoubleComplex)*(linelength/2+1);
const size_t in_size = sizeof(double)*(linelength+1);
// memory allocation
double *noise_in_host = (double*) malloc(in_size); // all these allocs can technically also be done outside of the function, if performance is bad this might be a consideration
double *noise_in_device;
checkCudaErrors(cudaMalloc((void **)&noise_in_device, in_size));
cufftDoubleComplex *noise_out_host = (cufftDoubleComplex*) fftw_malloc(out_size); // check if i need 2 times +1
cufftDoubleComplex *noise_out_device;
checkCudaErrors(cudaMalloc((void **)&noise_out_device, out_size));
double *transfer_in_host = (double*) malloc(in_size);
double *transfer_in_device;
checkCudaErrors(cudaMalloc((void **)&transfer_in_device, in_size));
cufftDoubleComplex *transfer_out_host = (cufftDoubleComplex*) fftw_malloc(out_size);
cufftDoubleComplex *transfer_out_device;
checkCudaErrors(cudaMalloc((void **)&transfer_out_device, out_size));
// plans
cufftPlan1d(&fftw_plan_transfer_fw, linelength, CUFFT_D2Z, 1);
cufftPlan1d(&fftw_plan_noise_fw, linelength, CUFFT_D2Z, 1);
cufftPlan1d(&fftw_plan_transfer_bw, linelength, CUFFT_Z2D, 1);
// XXX todo here we can potentially cufftPlanMany, to performa a batch of many plan1d fft
/*
fftw_plan_transfer_fw = fftw_plan_dft_r2c_1d(linelength, transfer_in, transfer_out,FFTW_MEASURE);
fftw_plan_noise_fw = fftw_plan_dft_r2c_1d(linelength, noise_in, noise_out,FFTW_MEASURE);
fftw_plan_transfer_bw = fftw_plan_dft_c2r_1d(linelength, transfer_out, transfer_in,FFTW_MEASURE);
*/
for(int v=0; v<nr_lines; v++){
for(int i=0; i<linelength; i++){
noise_in_host[i] = rand_01();
transfer_in_host[i] = rand_01();
}
Box_Mueller(linelength, noise_in_host, transfer_in_host);
for(int i=0; i<linelength; i++){
noise_in_host[i] = 2*noise_in_host[i]-1; // around 0
noise_in_host[i] = 5*noise_in_host[i]; //changes the deviation, which values need to be put will be investigated
}
transfer_in_host[0]=1.0;
for(int i=1; i<linelength; i++){
transfer_in_host[i] = (transfer_in_host[i-1]/(i))*(i-1-(PLindex/2.0));
}
/// (a) moving transfer_in and noise_in to the device
cudaMemcpy(noise_in_device, noise_in_host, in_size, cudaMemcpyDeviceToHost);
cudaMemcpy(transfer_in_device, transfer_in_host, in_size, cudaMemcpyDeviceToHost);
//fftw_execute(fftw_plan_noise_fw);
//fftw_execute(fftw_plan_transfer_fw);
cufftExecD2Z(fftw_plan_noise_fw, transfer_in_device, transfer_out_device);
cufftExecD2Z(fftw_plan_noise_fw, noise_in_device, noise_out_device);
/// (b) moving back transfer_out and noise out
cudaMemcpy(transfer_out_host, transfer_out_device, out_size, cudaMemcpyHostToDevice);
cudaMemcpy(noise_out_host, noise_out_device, out_size, cudaMemcpyHostToDevice);
for(int i=0; i<0.5*linelength+1; i++){
double temp = (transfer_out_host[i].x*noise_out_host[i].x+transfer_out_host[i].y*noise_out_host[i].y) / linelength;
transfer_out_host[i].y = (transfer_out_host[i].x*noise_out_host[i].y-transfer_out_host[i].y*noise_out_host[i].x) / linelength;
transfer_out_host[i].x = temp;
}
/// (c) moving to the device transfer_out
cudaMemcpy(transfer_out_device, transfer_out_host, out_size, cudaMemcpyDeviceToHost);
// fftw_execute(fftw_plan_transfer_bw);
cufftExecZ2D(fftw_plan_transfer_bw, transfer_out_device, transfer_in_device);
//// (d) moving back transfer_in
cudaMemcpy(transfer_in_host, transfer_in_device, in_size, cudaMemcpyHostToDevice);
for(int i=0; i<linelength; i++){
transfer_in_host[i] = transfer_in_host[i]/sqrt((double) linelength);
}
// xxx reduce max/avg
double average=0;
for(int i=0; i<linelength; i++){
average = average + transfer_in_host[i];
}
average = average/linelength;
double *y_host = (double*)malloc(sizeof(cl_double) * linelength * nr_lines + 1);
size_t index = 0;
for(int i=0; i<linelength; i++){
y_host[index]=(transfer_in_host[i]-average);
index++;
}
/// (e) write y_buf
//icl_local_device* ldev = &local_devices[y_buffer->device->device_id];
icl_local_buffer* lbuf = (icl_local_buffer*)(y_buffer->buffer_add);
cudaMemcpy((void*)lbuf->mem, transfer_out_host, out_size, cudaMemcpyDeviceToHost);
}
free(transfer_in_host);
free(transfer_out_host);
free(noise_out_host);
free(noise_in_host);
cudaFree(transfer_in_device);
cudaFree(transfer_out_device);
cudaFree(noise_out_device);
cudaFree(noise_in_device);
cufftDestroy(fftw_plan_transfer_fw);
cufftDestroy(fftw_plan_transfer_bw);
cufftDestroy(fftw_plan_noise_fw);
// fftw_cleanup();
}
