-
Notifications
You must be signed in to change notification settings - Fork 0
/
Utilities.cu
554 lines (425 loc) · 16.8 KB
/
Utilities.cu
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
#include <stdio.h>
#include <assert.h>
//#include <math.h>
#include "cuda_runtime.h"
#include <cuda.h>
#include <cusolverDn.h>
#include <cublas_v2.h>
#include <cufft.h>
#include "Utilities.cuh"
#define DEBUG
#define PI_R 3.14159265358979323846f
/*******************/
/* iDivUp FUNCTION */
/*******************/
//extern "C" int iDivUp(int a, int b){ return ((a % b) != 0) ? (a / b + 1) : (a / b); }
__host__ __device__ int iDivUp(int a, int b){ return ((a % b) != 0) ? (a / b + 1) : (a / b); }
/********************/
/* CUDA ERROR CHECK */
/********************/
// --- Credit to http://stackoverflow.com/questions/14038589/what-is-the-canonical-way-to-check-for-errors-using-the-cuda-runtime-api
void gpuAssert(cudaError_t code, const char *file, int line, bool abort = true)
{
if (code != cudaSuccess)
{
fprintf(stderr, "GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
if (abort) { exit(code); }
}
}
extern "C" void gpuErrchk(cudaError_t ans) { gpuAssert((ans), __FILE__, __LINE__); }
/**************************/
/* CUSOLVE ERROR CHECKING */
/**************************/
#if (__CUDACC_VER__ >= 70000)
static const char *_cusolverGetErrorEnum(cusolverStatus_t error)
{
switch (error)
{
case CUSOLVER_STATUS_SUCCESS:
return "CUSOLVER_SUCCESS";
case CUSOLVER_STATUS_NOT_INITIALIZED:
return "CUSOLVER_STATUS_NOT_INITIALIZED";
case CUSOLVER_STATUS_ALLOC_FAILED:
return "CUSOLVER_STATUS_ALLOC_FAILED";
case CUSOLVER_STATUS_INVALID_VALUE:
return "CUSOLVER_STATUS_INVALID_VALUE";
case CUSOLVER_STATUS_ARCH_MISMATCH:
return "CUSOLVER_STATUS_ARCH_MISMATCH";
case CUSOLVER_STATUS_EXECUTION_FAILED:
return "CUSOLVER_STATUS_EXECUTION_FAILED";
case CUSOLVER_STATUS_INTERNAL_ERROR:
return "CUSOLVER_STATUS_INTERNAL_ERROR";
case CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED:
return "CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED";
}
return "<unknown>";
}
inline void __cusolveSafeCall(cusolverStatus_t err, const char *file, const int line)
{
if (CUSOLVER_STATUS_SUCCESS != err) {
fprintf(stderr, "CUSOLVE error in file '%s', line %d, error: %s \nterminating!\n", __FILE__, __LINE__, \
_cusolverGetErrorEnum(err)); \
assert(0); \
}
}
extern "C" void cusolveSafeCall(cusolverStatus_t err) { __cusolveSafeCall(err, __FILE__, __LINE__); }
#endif
/*************************/
/* CUBLAS ERROR CHECKING */
/*************************/
static const char *_cublasGetErrorEnum(cublasStatus_t error)
{
switch (error)
{
case CUBLAS_STATUS_SUCCESS:
return "CUBLAS_STATUS_SUCCESS";
case CUBLAS_STATUS_NOT_INITIALIZED:
return "CUBLAS_STATUS_NOT_INITIALIZED";
case CUBLAS_STATUS_ALLOC_FAILED:
return "CUBLAS_STATUS_ALLOC_FAILED";
case CUBLAS_STATUS_INVALID_VALUE:
return "CUBLAS_STATUS_INVALID_VALUE";
case CUBLAS_STATUS_ARCH_MISMATCH:
return "CUBLAS_STATUS_ARCH_MISMATCH";
case CUBLAS_STATUS_MAPPING_ERROR:
return "CUBLAS_STATUS_MAPPING_ERROR";
case CUBLAS_STATUS_EXECUTION_FAILED:
return "CUBLAS_STATUS_EXECUTION_FAILED";
case CUBLAS_STATUS_INTERNAL_ERROR:
return "CUBLAS_STATUS_INTERNAL_ERROR";
case CUBLAS_STATUS_NOT_SUPPORTED:
return "CUBLAS_STATUS_NOT_SUPPORTED";
case CUBLAS_STATUS_LICENSE_ERROR:
return "CUBLAS_STATUS_LICENSE_ERROR";
}
return "<unknown>";
}
inline void __cublasSafeCall(cublasStatus_t err, const char *file, const int line)
{
if (CUBLAS_STATUS_SUCCESS != err) {
fprintf(stderr, "CUBLAS error in file '%s', line %d, error: %s\nterminating!\n", __FILE__, __LINE__, \
_cublasGetErrorEnum(err)); \
assert(0); \
}
}
extern "C" void cublasSafeCall(cublasStatus_t err) { __cublasSafeCall(err, __FILE__, __LINE__); }
/************************/
/* CUFFT ERROR CHECKING */
/************************/
// See http://stackoverflow.com/questions/16267149/cufft-error-handling
static const char *_cudaGetErrorEnum(cufftResult error)
{
switch (error)
{
case CUFFT_SUCCESS:
return "CUFFT_SUCCESS - The cuFFT operation was successful";
case CUFFT_INVALID_PLAN:
return "CUFFT_INVALID_PLAN - cuFFT was passed an invalid plan handle";
case CUFFT_ALLOC_FAILED:
return "CUFFT_ALLOC_FAILED - cuFFT failed to allocate GPU or CPU memory";
case CUFFT_INVALID_TYPE:
return "CUFFT_INVALID_TYPE - No longer used";
case CUFFT_INVALID_VALUE:
return "CUFFT_INVALID_VALUE - User specified an invalid pointer or parameter";
case CUFFT_INTERNAL_ERROR:
return "CUFFT_INTERNAL_ERROR - Driver or internal cuFFT library error";
case CUFFT_EXEC_FAILED:
return "CUFFT_EXEC_FAILED - Failed to execute an FFT on the GPU";
case CUFFT_SETUP_FAILED:
return "CUFFT_SETUP_FAILED - The cuFFT library failed to initialize";
case CUFFT_INVALID_SIZE:
return "CUFFT_INVALID_SIZE - User specified an invalid transform size";
case CUFFT_UNALIGNED_DATA:
return "CUFFT_UNALIGNED_DATA - No longer used";
case CUFFT_INCOMPLETE_PARAMETER_LIST:
return "CUFFT_INCOMPLETE_PARAMETER_LIST - Missing parameters in call";
case CUFFT_INVALID_DEVICE:
return "CUFFT_INVALID_DEVICE - Execution of a plan was on different GPU than plan creation";
case CUFFT_PARSE_ERROR:
return "CUFFT_PARSE_ERROR - Internal plan database error";
case CUFFT_NO_WORKSPACE:
return "CUFFT_NO_WORKSPACE - No workspace has been provided prior to plan execution";
case CUFFT_NOT_IMPLEMENTED:
return "CUFFT_NOT_IMPLEMENTED - Function does not implement functionality for parameters given";
case CUFFT_LICENSE_ERROR:
return "CUFFT_LICENSE_ERROR - Used in previous versions";
case CUFFT_NOT_SUPPORTED:
return "CUFFT_NOT_SUPPORTED - Operation is not supported for parameters given";
}
return "<unknown>";
}
// --- CUFFTSAFECALL
inline void cufftAssert(cufftResult err, const char *file, const int line, bool abort = true)
{
if (CUFFT_SUCCESS != err) {
fprintf(stderr, "CUFFTassert: Error nr. %d - %s %s %d\n", err, _cudaGetErrorEnum(err), __FILE__, __LINE__);
if (abort) exit(err);
}
}
extern "C" void cufftSafeCall(cufftResult err) { cufftAssert(err, __FILE__, __LINE__); }
/***************************/
/* CUSPARSE ERROR CHECKING */
/***************************/
static const char *_cusparseGetErrorEnum(cusparseStatus_t error)
{
switch (error)
{
case CUSPARSE_STATUS_SUCCESS:
return "CUSPARSE_STATUS_SUCCESS";
case CUSPARSE_STATUS_NOT_INITIALIZED:
return "CUSPARSE_STATUS_NOT_INITIALIZED";
case CUSPARSE_STATUS_ALLOC_FAILED:
return "CUSPARSE_STATUS_ALLOC_FAILED";
case CUSPARSE_STATUS_INVALID_VALUE:
return "CUSPARSE_STATUS_INVALID_VALUE";
case CUSPARSE_STATUS_ARCH_MISMATCH:
return "CUSPARSE_STATUS_ARCH_MISMATCH";
case CUSPARSE_STATUS_MAPPING_ERROR:
return "CUSPARSE_STATUS_MAPPING_ERROR";
case CUSPARSE_STATUS_EXECUTION_FAILED:
return "CUSPARSE_STATUS_EXECUTION_FAILED";
case CUSPARSE_STATUS_INTERNAL_ERROR:
return "CUSPARSE_STATUS_INTERNAL_ERROR";
case CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED:
return "CUSPARSE_STATUS_MATRIX_TYPE_NOT_SUPPORTED";
case CUSPARSE_STATUS_ZERO_PIVOT:
return "CUSPARSE_STATUS_ZERO_PIVOT";
}
return "<unknown>";
}
inline void __cusparseSafeCall(cusparseStatus_t err, const char *file, const int line)
{
if (CUSPARSE_STATUS_SUCCESS != err) {
fprintf(stderr, "CUSPARSE error in file '%s', line %d, error %s\nterminating!\n", __FILE__, __LINE__, \
_cusparseGetErrorEnum(err)); \
assert(0); \
}
}
extern "C" void cusparseSafeCall(cusparseStatus_t err) { __cusparseSafeCall(err, __FILE__, __LINE__); }
/************************/
/* REVERSE ARRAY KERNEL */
/************************/
#define BLOCKSIZE_REVERSE 256
// --- Credit to http://www.drdobbs.com/parallel/cuda-supercomputing-for-the-masses-part/208801731?pgno=2
template <class T>
__global__ void reverseArrayKernel(const T * __restrict__ d_in, T * __restrict__ d_out, const int N, const T a)
{
// --- Credit to the simpleTemplates CUDA sample
SharedMemory<T> smem;
T* s_data = smem.getPointer();
const int tid = blockDim.x * blockIdx.x + threadIdx.x;
const int id = threadIdx.x;
const int offset = blockDim.x * (blockIdx.x + 1);
// --- Load one element per thread from device memory and store it *in reversed order* into shared memory
if (tid < N) s_data[BLOCKSIZE_REVERSE - (id + 1)] = a * d_in[tid];
// --- Block until all threads in the block have written their data to shared memory
__syncthreads();
// --- Write the data from shared memory in forward order
if ((N - offset + id) >= 0) d_out[N - offset + id] = s_data[threadIdx.x];
}
/************************/
/* REVERSE ARRAY KERNEL */
/************************/
template <class T>
void reverseArray(const T * __restrict__ d_in, T * __restrict__ d_out, const int N, const T a) {
reverseArrayKernel << <iDivUp(N, BLOCKSIZE_REVERSE), BLOCKSIZE_REVERSE, BLOCKSIZE_REVERSE * sizeof(T) >> >(d_in, d_out, N, a);
#ifdef DEBUG
gpuErrchk(cudaPeekAtLastError());
gpuErrchk(cudaDeviceSynchronize());
#endif
}
template void reverseArray<float>(const float * __restrict__, float * __restrict__, const int, const float);
template void reverseArray<double>(const double * __restrict__, double * __restrict__, const int, const double);
/********************************************************/
/* CARTESIAN TO POLAR COORDINATES TRANSFORMATION KERNEL */
/********************************************************/
#define BLOCKSIZE_CART2POL 256
template <class T>
__global__ void Cartesian2PolarKernel(const T * __restrict__ d_x, const T * __restrict__ d_y, T * __restrict__ d_rho, T * __restrict__ d_theta,
const int N, const T a) {
const int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < N) {
d_rho[tid] = a * hypot(d_x[tid], d_y[tid]);
d_theta[tid] = atan2(d_y[tid], d_x[tid]);
}
}
/*******************************************************/
/* CARTESIAN TO POLAR COORDINATES TRANSFORMATION - GPU */
/*******************************************************/
//template <class T>
//thrust::pair<T *,T *> Cartesian2Polar(const T * __restrict__ d_x, const T * __restrict__ d_y, const int N, const T a) {
//
// T *d_rho; gpuErrchk(cudaMalloc((void**)&d_rho, N * sizeof(T)));
// T *d_theta; gpuErrchk(cudaMalloc((void**)&d_theta, N * sizeof(T)));
//
// Cartesian2PolarKernel<<<iDivUp(N, BLOCKSIZE_CART2POL), BLOCKSIZE_CART2POL>>>(d_x, d_y, d_rho, d_theta, N, a);
//#ifdef DEBUG
// gpuErrchk(cudaPeekAtLastError());
// gpuErrchk(cudaDeviceSynchronize());
//#endif
//
// return thrust::make_pair(d_rho, d_theta);
//}
//
//template thrust::pair<float *, float *> Cartesian2Polar<float> (const float *, const float *, const int, const float);
//template thrust::pair<double *, double *> Cartesian2Polar<double> (const double *, const double *, const int, const double);
/*******************************************************/
/* CARTESIAN TO POLAR COORDINATES TRANSFORMATION - CPU */
/*******************************************************/
//template <class T>
//thrust::pair<T *,T *> h_Cartesian2Polar(const T * __restrict__ h_x, const T * __restrict__ h_y, const int N, const T a) {
//
// T *h_rho = (T *)malloc(N * sizeof(T));
// T *h_theta = (T *)malloc(N * sizeof(T));
//
// for (int i = 0; i < N; i++) {
// h_rho[i] = a * hypot(h_x[i], h_y[i]);
// h_theta[i] = atan2(h_y[i], h_x[i]);
// }
//
// return thrust::make_pair(h_rho, h_theta);
//}
//
//template thrust::pair<float *, float *> h_Cartesian2Polar<float> (const float *, const float *, const int, const float);
//template thrust::pair<double *, double *> h_Cartesian2Polar<double> (const double *, const double *, const int, const double);
/*******************************/
/* COMPUTE L2 NORM OF A VECTOR */
/*******************************/
template<class T>
T h_l2_norm(T *v1, T *v2, const int N) {
T norm = (T)0;
for (int i = 0; i < N; ++i)
{
T d = v1[i] - v2[i];
norm = norm + d * d;
}
return sqrt(norm);
}
template float h_l2_norm<float>(float *, float *, const int);
template double h_l2_norm<double>(double *, double *, const int);
/*******************************/
/* LINEAR COMBINATION FUNCTION */
/*******************************/
void linearCombination(const float * __restrict__ d_coeff, const float * __restrict__ d_basis_functions_real, float * __restrict__ d_linear_combination,
const int N_basis_functions, const int N_sampling_points, const cublasHandle_t handle) {
float alpha = 1.f;
float beta = 0.f;
cublasSafeCall(cublasSgemv(handle, CUBLAS_OP_N, N_sampling_points, N_basis_functions, &alpha, d_basis_functions_real, N_sampling_points,
d_coeff, 1, &beta, d_linear_combination, 1));
}
void linearCombination(const double * __restrict__ d_coeff, const double * __restrict__ d_basis_functions_real, double * __restrict__ d_linear_combination,
const int N_basis_functions, const int N_sampling_points, const cublasHandle_t handle) {
double alpha = 1.;
double beta = 0.;
cublasSafeCall(cublasDgemv(handle, CUBLAS_OP_N, N_sampling_points, N_basis_functions, &alpha, d_basis_functions_real, N_sampling_points,
d_coeff, 1, &beta, d_linear_combination, 1));
}
/******************************/
/* ADD A CONSTANT TO A VECTOR */
/******************************/
#define BLOCKSIZE_VECTORADDCONSTANT 256
template<class T>
__global__ void vectorAddConstantKernel(T * __restrict__ d_in, const T scalar, const int N) {
const int tid = threadIdx.x + blockIdx.x*blockDim.x;
if (tid < N) d_in[tid] += scalar;
}
template<class T>
void vectorAddConstant(T * __restrict__ d_in, const T scalar, const int N) {
vectorAddConstantKernel << <iDivUp(N, BLOCKSIZE_VECTORADDCONSTANT), BLOCKSIZE_VECTORADDCONSTANT >> >(d_in, scalar, N);
}
template void vectorAddConstant<float>(float * __restrict__, const float, const int);
template void vectorAddConstant<double>(double * __restrict__, const double, const int);
/*****************************************/
/* MULTIPLY A VECTOR BY A CONSTANT - GPU */
/*****************************************/
#define BLOCKSIZE_VECTORMULCONSTANT 256
template<class T>
__global__ void vectorMulConstantKernel(T * __restrict__ d_in, const T scalar, const int N) {
const int tid = threadIdx.x + blockIdx.x*blockDim.x;
if (tid < N) d_in[tid] *= scalar;
}
template<class T>
void vectorMulConstant(T * __restrict__ d_in, const T scalar, const int N) {
vectorMulConstantKernel << <iDivUp(N, BLOCKSIZE_VECTORMULCONSTANT), BLOCKSIZE_VECTORMULCONSTANT >> >(d_in, scalar, N);
}
template void vectorMulConstant<float>(float * __restrict__, const float, const int);
template void vectorMulConstant<double>(double * __restrict__, const double, const int);
/*****************************************/
/* MULTIPLY A VECTOR BY A CONSTANT - CPU */
/*****************************************/
template<class T>
void h_vectorMulConstant(T * __restrict__ h_in, const T scalar, const int N) {
for (int i = 0; i < N; i++) h_in[i] *= scalar;
}
template void h_vectorMulConstant<float>(float * __restrict__, const float, const int);
template void h_vectorMulConstant<double>(double * __restrict__, const double, const int);
/*****************************************************/
/* FUSED MULTIPLY ADD OPERATIONS FOR HOST AND DEVICE */
/*****************************************************/
template<class T>
__host__ __device__ T fma2(T x, T y, T z) { return x * y + z; }
template float fma2<float >(float, float, float);
template double fma2<double>(double, double, double);
/*******************/
/* MODULO FUNCTION */
/*******************/
__device__ int modulo(int val, int _mod)
{
int P;
if (val > 0) { (!(_mod & (_mod - 1)) ? P = val&(_mod - 1) : P = val % (_mod)); return P; }
else
{
(!(_mod & (_mod - 1)) ? P = (-val)&(_mod - 1) : P = (-val) % (_mod));
if (P > 0) return _mod - P;
else return 0;
}
}
/***************************************/
/* ATOMIC ADDITION FUNCTION ON DOUBLES */
/***************************************/
#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 600
#else
__device__ double atomicAdd(double* address, double val)
{
unsigned long long int* address_as_ull =
(unsigned long long int*)address;
register unsigned long long int old = *address_as_ull, assumed;
do {
assumed = old;
old = atomicCAS(address_as_ull, assumed,
__double_as_longlong(val +
__longlong_as_double(assumed)));
} while (assumed != old);
return __longlong_as_double(old);
}
#endif
/*********************************/
/* ATOMIC MIN FUNCTION ON FLOATS */
/*********************************/
__device__ float atomicMin(float* address, float val)
{
int* address_as_i = (int*)address;
int old = *address_as_i, assumed;
do {
assumed = old;
old = ::atomicCAS(address_as_i, assumed,
__float_as_int(::fminf(val, __int_as_float(assumed))));
} while (assumed != old);
return __int_as_float(old);
}
/*********************/
/* DEGREE TO RADIANS */
/*********************/
double deg2rad(double deg) { return deg*PI_R / 180; }
/*********************/
/* CUDA MEMORY USAGE */
/*********************/
void cudaMemoryUsage() {
size_t free_byte;
size_t total_byte;
gpuErrchk(cudaMemGetInfo(&free_byte, &total_byte));
double free_db = (double)free_byte;
double total_db = (double)total_byte;
double used_db = total_db - free_db;
printf("GPU memory: used = %f, free = %f MB, total available = %f MB\n", used_db / 1024.0 / 1024.0, free_db / 1024.0 / 1024.0, total_db / 1024.0 / 1024.0);
}