bioem_cuda.cu 26.8 KB
Newer Older
Pilar Cossio's avatar
License  
Pilar Cossio committed
1
/* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
2
3
4
5
   < BioEM software for Bayesian inference of Electron Microscopy images>
   Copyright (C) 2016 Pilar Cossio, David Rohr, Fabio Baruffa, Markus Rampp, 
        Volker Lindenstruth and Gerhard Hummer.
   Max Planck Institute of Biophysics, Frankfurt, Germany.
6

7
   See license statement for terms of distribution.
Pilar Cossio's avatar
License  
Pilar Cossio committed
8
9
10

   ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++*/

11
12
13
14
15
16
17
18
19
20
21
#define BIOEM_GPUCODE

#if defined(_WIN32)
#include <windows.h>
#endif

#include <iostream>
using namespace std;

#include "bioem_cuda_internal.h"
#include "bioem_algorithm.h"
Pilar Cossio's avatar
Pilar Cossio committed
22
//#include "helper_cuda.h"
23

24
25
26
27
28
29
30
#define checkCudaErrors(error) \
{ \
	if ((error) != cudaSuccess) \
	{ \
		printf("CUDA Error %d / %s (%s: %d)\n", error, cudaGetErrorString(error), __FILE__, __LINE__); \
		exit(1); \
	} \
31
32
}

David Rohr's avatar
David Rohr committed
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
static const char *cufftGetErrorStrung(cufftResult error)
{
    switch (error)
    {
        case CUFFT_SUCCESS:
            return "CUFFT_SUCCESS";

        case CUFFT_INVALID_PLAN:
            return "CUFFT_INVALID_PLAN";

        case CUFFT_ALLOC_FAILED:
            return "CUFFT_ALLOC_FAILED";

        case CUFFT_INVALID_TYPE:
            return "CUFFT_INVALID_TYPE";

        case CUFFT_INVALID_VALUE:
            return "CUFFT_INVALID_VALUE";

        case CUFFT_INTERNAL_ERROR:
            return "CUFFT_INTERNAL_ERROR";

        case CUFFT_EXEC_FAILED:
            return "CUFFT_EXEC_FAILED";

        case CUFFT_SETUP_FAILED:
            return "CUFFT_SETUP_FAILED";

        case CUFFT_INVALID_SIZE:
            return "CUFFT_INVALID_SIZE";

        case CUFFT_UNALIGNED_DATA:
            return "CUFFT_UNALIGNED_DATA";
    }
    return "UNKNOWN";
}

Luka Stanisic's avatar
Luka Stanisic committed
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
/* Handing CUDA Driver errors */

#define cuErrorCheck(call) \
  do { \
    CUresult __error__; \
    if ((__error__ = (call)) != CUDA_SUCCESS) { \
      printf("CUDA Driver Error %d / %s (%s %d)\n", __error__, cuGetError(__error__),__FILE__, __LINE__); \
      return __error__; \
    } \
  } while (false)

static const char * cuGetError(CUresult result) {
  switch (result) {
    case CUDA_SUCCESS:                              return "No errors";
    case CUDA_ERROR_INVALID_VALUE:                  return "Invalid value";
    case CUDA_ERROR_OUT_OF_MEMORY:                  return "Out of memory";
    case CUDA_ERROR_NOT_INITIALIZED:                return "Driver not initialized";
    case CUDA_ERROR_DEINITIALIZED:                  return "Driver deinitialized";
    case CUDA_ERROR_PROFILER_DISABLED:              return "Profiler disabled";
    case CUDA_ERROR_PROFILER_NOT_INITIALIZED:       return "Profiler not initialized";
    case CUDA_ERROR_PROFILER_ALREADY_STARTED:       return "Profiler already started";
    case CUDA_ERROR_PROFILER_ALREADY_STOPPED:       return "Profiler already stopped";
    case CUDA_ERROR_NO_DEVICE:                      return "No CUDA-capable device available";
    case CUDA_ERROR_INVALID_DEVICE:                 return "Invalid device";
    case CUDA_ERROR_INVALID_IMAGE:                  return "Invalid kernel image";
    case CUDA_ERROR_INVALID_CONTEXT:                return "Invalid context";
    case CUDA_ERROR_CONTEXT_ALREADY_CURRENT:        return "Context already current";
    case CUDA_ERROR_MAP_FAILED:                     return "Map failed";
    case CUDA_ERROR_UNMAP_FAILED:                   return "Unmap failed";
    case CUDA_ERROR_ARRAY_IS_MAPPED:                return "Array is mapped";
    case CUDA_ERROR_ALREADY_MAPPED:                 return "Already mapped";
    case CUDA_ERROR_NO_BINARY_FOR_GPU:              return "No binary for GPU";
    case CUDA_ERROR_ALREADY_ACQUIRED:               return "Already acquired";
    case CUDA_ERROR_NOT_MAPPED:                     return "Not mapped";
    case CUDA_ERROR_NOT_MAPPED_AS_ARRAY:            return "Not mapped as array";
    case CUDA_ERROR_NOT_MAPPED_AS_POINTER:          return "Not mapped as pointer";
    case CUDA_ERROR_ECC_UNCORRECTABLE:              return "Uncorrectable ECC error";
    case CUDA_ERROR_UNSUPPORTED_LIMIT:              return "Unsupported CUlimit";
    case CUDA_ERROR_CONTEXT_ALREADY_IN_USE:         return "Context already in use";
    case CUDA_ERROR_INVALID_SOURCE:                 return "Invalid source";
    case CUDA_ERROR_FILE_NOT_FOUND:                 return "File not found";
    case CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND: return "Shared object symbol not found";
    case CUDA_ERROR_SHARED_OBJECT_INIT_FAILED:      return "Shared object initialization failed";
    case CUDA_ERROR_OPERATING_SYSTEM:               return "Operating System call failed";
    case CUDA_ERROR_INVALID_HANDLE:                 return "Invalid handle";
    case CUDA_ERROR_NOT_FOUND:                      return "Not found";
    case CUDA_ERROR_NOT_READY:                      return "CUDA not ready";
    case CUDA_ERROR_LAUNCH_FAILED:                  return "Launch failed";
    case CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES:        return "Launch exceeded resources";
    case CUDA_ERROR_LAUNCH_TIMEOUT:                 return "Launch exceeded timeout";
    case CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING:  return "Launch with incompatible texturing";
    case CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED:    return "Peer access already enabled";
    case CUDA_ERROR_PEER_ACCESS_NOT_ENABLED:        return "Peer access not enabled";
    case CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE:         return "Primary context active";
    case CUDA_ERROR_CONTEXT_IS_DESTROYED:           return "Context is destroyed";
    case CUDA_ERROR_ASSERT:                         return "Device assert failed";
    case CUDA_ERROR_TOO_MANY_PEERS:                 return "Too many peers";
    case CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED: return "Host memory already registered";
    case CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED:     return "Host memory not registered";
    case CUDA_ERROR_UNKNOWN:                        return "Unknown error";
    default:                                        return "Unknown error code";
  }
}

134
135
136
137
bioem_cuda::bioem_cuda()
{
	deviceInitialized = 0;
	GPUAlgo = getenv("GPUALGO") == NULL ? 2 : atoi(getenv("GPUALGO"));
138
139
	GPUAsync = getenv("GPUASYNC") == NULL ? 1 : atoi(getenv("GPUASYNC"));
	GPUWorkload = getenv("GPUWORKLOAD") == NULL ? 100 : atoi(getenv("GPUWORKLOAD"));
140
	GPUDualStream = getenv("GPUDUALSTREAM") == NULL ? 1 : atoi(getenv("GPUDUALSTREAM"));
141
142
143
144
145
146
147
}

bioem_cuda::~bioem_cuda()
{
	deviceExit();
}

148
149
150
__global__ void compareRefMap_kernel(const int iOrient, const int iConv,  const myfloat_t amp, const myfloat_t pha, const myfloat_t env, const myfloat_t sumC,
                                                const myfloat_t sumsquareC, const myfloat_t* pMap, bioem_Probability pProb, 
						const bioem_param_device param, const bioem_RefMap_Mod RefMap, const int cent_x, const int cent_y, const int maxRef)
151
152
{
	const int iRefMap = myBlockIdxX * myBlockDimX + myThreadIdxX;
153
	if (iRefMap < maxRef)
154
	{
155
		compareRefMap<0>(iRefMap, iOrient, iConv, amp, pha, env, sumC, sumsquareC, pMap, pProb, param, RefMap, cent_x, cent_y);
156
157
158
	}
}

Pilar Cossio's avatar
Pilar Cossio committed
159
__global__ void compareRefMapShifted_kernel(const int iOrient, const int iConv, const myfloat_t amp, const myfloat_t pha, const myfloat_t env, const myfloat_t sumC, const myfloat_t sumsquareC, const myfloat_t* pMap, bioem_Probability pProb, const bioem_param_device param, const bioem_RefMap_Mod RefMap, const int maxRef)
160
161
{
	const int iRefMap = myBlockIdxX * myBlockDimX + myThreadIdxX;
162
	if (iRefMap < maxRef)
163
	{
164
		compareRefMapShifted<1>(iRefMap, iOrient, iConv, amp, pha, env, sumC, sumsquareC, pMap, pProb, param, RefMap);
165
166
167
	}
}

168
169
170
171
172
173
__global__ void cudaZeroMem(void* ptr, size_t size)
{
	int* myptr = (int*) ptr;
	int mysize = size / sizeof(int);
	int myid = myBlockDimX * myBlockIdxX + myThreadIdxX;
	int mygrid = myBlockDimX * myGridDimX;
174
	for (int i = myid; i < mysize; i += mygrid) myptr[i] = 0;
175
176
}

Pilar Cossio's avatar
Pilar Cossio committed
177
__global__ void compareRefMapLoopShifts_kernel(const int iOrient, const int iConv, const myfloat_t amp, const myfloat_t pha, const myfloat_t env, const myfloat_t sumC, const myfloat_t sumsquareC, const myfloat_t* pMap, bioem_Probability pProb, const bioem_param_device param, const bioem_RefMap RefMap, const int blockoffset, const int nShifts, const int nShiftBits, const int maxRef)
178
179
180
181
182
183
184
185
186
{
	const size_t myid = (myBlockIdxX + blockoffset) * myBlockDimX + myThreadIdxX;
	const int iRefMap = myid >> (nShiftBits << 1);
	const int myRef = myThreadIdxX >> (nShiftBits << 1);
	const int myShiftIdx = (myid >> nShiftBits) & (nShifts - 1);
	const int myShiftIdy = myid & (nShifts - 1);
	const int myShift = myid & (nShifts * nShifts - 1);
	const int cent_x = myShiftIdx * param.GridSpaceCenter - param.maxDisplaceCenter;
	const int cent_y = myShiftIdy * param.GridSpaceCenter - param.maxDisplaceCenter;
187

188
	const bool threadActive = myShiftIdx < nShifts && myShiftIdy < nShifts && iRefMap < maxRef;
189

Pilar Cossio's avatar
Pilar Cossio committed
190
	compareRefMap<2>(iRefMap, iOrient, iConv, amp, pha, env, sumC, sumsquareC, pMap, pProb, param, RefMap, cent_x, cent_y, myShift, nShifts * nShifts, myRef, threadActive);
191
192
}

193
__global__ void multComplexMap(const mycomplex_t* convmap, const mycomplex_t* refmap, mycuComplex_t* out, const int NumberPixelsTotal, const int MapSize, const int NumberMaps, const int Offset)
194
195
{
	if (myBlockIdxX >= NumberMaps) return;
196
	const mycuComplex_t* myin = (mycuComplex_t*) &refmap[(myBlockIdxX + Offset) * MapSize];
197
	const mycuComplex_t* myconv = (mycuComplex_t*) convmap;
198
	mycuComplex_t* myout = &out[myBlockIdxX * MapSize];
199
	for(int i = myThreadIdxX; i < NumberPixelsTotal; i += myBlockDimX)
200
	{
201
202
203
204
205
206
207
		mycuComplex_t val;
		const mycuComplex_t conv = myconv[i];
		const mycuComplex_t in = myin[i];

		val.x = conv.x * in.x + conv.y * in.y;
		val.y = conv.y * in.x - conv.x * in.y;
		myout[i] = val;
208
209
210
	}
}

211
__global__ void cuDoRefMapsFFT(const int iOrient, const int iConv, const myfloat_t amp, const myfloat_t pha, const myfloat_t env, const myfloat_t* lCC, const myfloat_t sumC, const myfloat_t sumsquareC, bioem_Probability pProb, const bioem_param_device param, const bioem_RefMap RefMap, const int maxRef, const int Offset)
212
{
213
	if (myBlockIdxX * myBlockDimX + myThreadIdxX >= maxRef) return;
214
215
	const int iRefMap = myBlockIdxX * myBlockDimX + myThreadIdxX + Offset;
	const myfloat_t* mylCC = &lCC[(myBlockIdxX * myBlockDimX + myThreadIdxX) * param.NumberPixels * param.NumberPixels];
216
	doRefMapFFT(iRefMap, iOrient, iConv, amp, pha, env, mylCC, sumC, sumsquareC, pProb, param, RefMap);
217
218
}

219
220
221
222
223
224
225
226
227
228
229
230
231
template <class T> static inline T divup(T num, T divider) {return((num + divider - 1) / divider);}
static inline bool IsPowerOf2(int x) {return ((x > 0) && ((x & (x - 1)) == 0));}
#if defined(_WIN32)
static inline int ilog2 (int value)
{
	DWORD index;
	_BitScanReverse (&index, value);
	return(value);
}
#else
static inline int ilog2(int value) {return 31 - __builtin_clz(value);}
#endif

232
int bioem_cuda::compareRefMaps(int iOrient, int iConv, myfloat_t amp, myfloat_t pha, myfloat_t env, const myfloat_t* conv_map, mycomplex_t* localmultFFT, myfloat_t sumC, myfloat_t sumsquareC, const int startMap)
233
{
234
235
236
237
238
	if (startMap)
	{
		cout << "Error startMap not implemented for GPU Code\n";
		exit(1);
	}
Luka Stanisic's avatar
Luka Stanisic committed
239
240
241
242
243
244
245
#ifdef DEBUG_GPU
	float time;
	cudaEvent_t start, stop;
	checkCudaErrors(cudaEventCreate(&start));
	checkCudaErrors(cudaEventCreate(&stop));
	checkCudaErrors(cudaEventRecord(start, 0));
#endif
246
247
248
249
	if (GPUAsync)
	{
		checkCudaErrors(cudaEventSynchronize(cudaEvent[iConv & 1]));
	}
Luka Stanisic's avatar
Luka Stanisic committed
250
251
252
253
254
255
256
#ifdef DEBUG_GPU
	checkCudaErrors(cudaEventRecord(stop, 0));
	checkCudaErrors(cudaEventSynchronize(stop));
	checkCudaErrors(cudaEventElapsedTime(&time, start, stop));
	printf("\t\t\tGPU: time to synch projections %1.6f sec\n", time/1000);
	checkCudaErrors(cudaEventRecord(start, 0));
#endif
257
	if (FFTAlgo)
258
	{
259
		memcpy(&pConvMapFFT_Host[(iConv & 1) * param.FFTMapSize], localmultFFT, param.FFTMapSize * sizeof(mycomplex_t));
260
		checkCudaErrors(cudaMemcpyAsync(&pConvMapFFT[(iConv & 1) * param.FFTMapSize], &pConvMapFFT_Host[(iConv & 1) * param.FFTMapSize], param.FFTMapSize * sizeof(mycomplex_t), cudaMemcpyHostToDevice, cudaStream[GPUAsync ? 2 : 0]));
Luka Stanisic's avatar
Luka Stanisic committed
261
262
263
264
265
266
267
#ifdef DEBUG_GPU
		checkCudaErrors(cudaEventRecord(stop, 0));
		checkCudaErrors(cudaEventSynchronize(stop));
		checkCudaErrors(cudaEventElapsedTime(&time, start, stop));
		printf("\t\t\tGPU: time for memcpy %1.6f sec\n", time/1000);
		checkCudaErrors(cudaEventRecord(start, 0));
#endif
268
269
270
271
272
		if (GPUAsync)
		{
			checkCudaErrors(cudaEventRecord(cudaEvent[2], cudaStream[2]));
			checkCudaErrors(cudaStreamWaitEvent(cudaStream[0], cudaEvent[2], 0));
		}
273
		if (GPUDualStream)
274
		{
275
276
277
278
279
280
			checkCudaErrors(cudaEventRecord(cudaFFTEvent[0], cudaStream[0]));
			checkCudaErrors(cudaStreamWaitEvent(cudaStream[1], cudaFFTEvent[0], 0));
		}
		for (int i = 0, j = 0; i < maxRef; i += CUDA_FFTS_AT_ONCE, j++)
		{
			if (!GPUDualStream) j = 0;
281
			const int num = min(CUDA_FFTS_AT_ONCE, maxRef - i);
282
283
			multComplexMap<<<num, CUDA_THREAD_COUNT, 0, cudaStream[j & 1]>>>(&pConvMapFFT[(iConv & 1) * param.FFTMapSize], pRefMapsFFT, pFFTtmp2[j & 1], param.param_device.NumberPixels * param.param_device.NumberFFTPixels1D, param.FFTMapSize, num, i);
			cufftResult err = mycufftExecC2R(i + CUDA_FFTS_AT_ONCE > maxRef ? plan[1][j & 1] : plan[0][j & 1], pFFTtmp2[j & 1], pFFTtmp[j & 1]);
David Rohr's avatar
David Rohr committed
284
			if (err != CUFFT_SUCCESS)
285
			{
David Rohr's avatar
David Rohr committed
286
				cout << "Error running CUFFT " << cufftGetErrorStrung(err) << "\n";
287
288
				exit(1);
			}
289
			cuDoRefMapsFFT<<<divup(num, CUDA_THREAD_COUNT), CUDA_THREAD_COUNT, 0, cudaStream[j & 1]>>>(iOrient, iConv,  amp, pha, env, pFFTtmp[j & 1], sumC, sumsquareC, pProb_device, param.param_device, *gpumap, num, i);
290
		}
Luka Stanisic's avatar
Luka Stanisic committed
291
		checkCudaErrors(cudaPeekAtLastError());
292
293
294
295
296
		if (GPUDualStream)
		{
			checkCudaErrors(cudaEventRecord(cudaFFTEvent[1], cudaStream[1]));
			checkCudaErrors(cudaStreamWaitEvent(cudaStream[0], cudaFFTEvent[1], 0));
		}
297
298
299
	}
	else
	{
300
		checkCudaErrors(cudaMemcpyAsync(pConvMap_device[iConv & 1], conv_map, sizeof(myfloat_t) * RefMap.refMapSize, cudaMemcpyHostToDevice, cudaStream[0]));
Luka Stanisic's avatar
Luka Stanisic committed
301
302
303
304
305
306
307
#ifdef DEBUG_GPU
		checkCudaErrors(cudaEventRecord(stop, 0));
		checkCudaErrors(cudaEventSynchronize(stop));
		checkCudaErrors(cudaEventElapsedTime(&time, start, stop));
		printf("\t\t\tGPU: time for memcpy %1.6f sec\n", time/1000);
		checkCudaErrors(cudaEventRecord(start, 0) );
#endif
308
		if (GPUAlgo == 2) //Loop over shifts
309
		{
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
			const int nShifts = 2 * param.param_device.maxDisplaceCenter / param.param_device.GridSpaceCenter + 1;
			if (!IsPowerOf2(nShifts))
			{
				cout << "Invalid number of displacements, no power of two\n";
				exit(1);
			}
			if (CUDA_THREAD_COUNT % (nShifts * nShifts))
			{
				cout << "CUDA Thread count (" << CUDA_THREAD_COUNT << ") is no multiple of number of shifts (" << (nShifts * nShifts) << ")\n";
				exit(1);
			}
			if (nShifts > CUDA_MAX_SHIFT_REDUCE)
			{
				cout << "Too many displacements for CUDA reduction\n";
				exit(1);
			}
			const int nShiftBits = ilog2(nShifts);
			size_t totalBlocks = divup((size_t) maxRef * (size_t) nShifts * (size_t) nShifts, (size_t) CUDA_THREAD_COUNT);
			size_t nBlocks = CUDA_BLOCK_COUNT;
329
			for (size_t i = 0; i < totalBlocks; i += nBlocks)
330
			{
Pilar Cossio's avatar
Pilar Cossio committed
331
				compareRefMapLoopShifts_kernel<<<min(nBlocks, totalBlocks - i), CUDA_THREAD_COUNT, (CUDA_THREAD_COUNT * 2 + CUDA_THREAD_COUNT / (nShifts * nShifts) * 4) * sizeof(myfloat_t), cudaStream[0] >>> (iOrient, iConv, amp, pha, env, sumC, sumsquareC, pConvMap_device[iConv & 1], pProb_device, param.param_device, *gpumap, i, nShifts, nShiftBits, maxRef);
332
			}
333
		}
334
		else if (GPUAlgo == 1) //Split shifts in multiple kernels
335
		{
336
			for (int cent_x = -param.param_device.maxDisplaceCenter; cent_x <= param.param_device.maxDisplaceCenter; cent_x = cent_x + param.param_device.GridSpaceCenter)
337
			{
338
				for (int cent_y = -param.param_device.maxDisplaceCenter; cent_y <= param.param_device.maxDisplaceCenter; cent_y = cent_y + param.param_device.GridSpaceCenter)
339
				{
Pilar Cossio's avatar
Pilar Cossio committed
340
					compareRefMap_kernel<<<divup(maxRef, CUDA_THREAD_COUNT), CUDA_THREAD_COUNT, 0, cudaStream[0]>>> (iOrient, iConv, amp, pha, env, sumC, sumsquareC, pConvMap_device[iConv & 1], pProb_device, param.param_device, *pRefMap_device_Mod, cent_x, cent_y, maxRef);
341
342
				}
			}
343
		}
344
		else if (GPUAlgo == 0) //All shifts in one kernel
345
		{
346
			compareRefMapShifted_kernel<<<divup(maxRef, CUDA_THREAD_COUNT), CUDA_THREAD_COUNT, 0, cudaStream[0]>>> (iOrient, iConv, amp, pha, env, sumC, sumsquareC, pConvMap_device[iConv & 1], pProb_device, param.param_device, *pRefMap_device_Mod, maxRef);
347
		}
348
		else
349
		{
350
351
			cout << "Invalid GPU Algorithm selected\n";
			exit(1);
352
		}
353
	}
Luka Stanisic's avatar
Luka Stanisic committed
354
355
356
357
358
359
360
#ifdef DEBUG_GPU
	checkCudaErrors(cudaEventRecord(stop, 0));
	checkCudaErrors(cudaEventSynchronize(stop));
	checkCudaErrors(cudaEventElapsedTime(&time, start, stop));
	printf("\t\t\tGPU: time to run CUDA %1.6f sec\n", time/1000);
	checkCudaErrors(cudaEventRecord(start, 0));
#endif
361
362
	if (GPUWorkload < 100)
	{
363
		bioem::compareRefMaps(iOrient, iConv, amp, pha, env, conv_map, localmultFFT, sumC, sumsquareC, maxRef);
364
	}
Luka Stanisic's avatar
Luka Stanisic committed
365
366
367
368
369
370
#ifdef DEBUG_GPU
	checkCudaErrors(cudaEventRecord(stop, 0));
	checkCudaErrors(cudaEventSynchronize(stop));
	checkCudaErrors(cudaEventElapsedTime(&time, start, stop));
	printf("\t\t\tGPU: time to run OMP %1.6f sec\n", time/1000);
#endif
371
372
	if (GPUAsync)
	{
373
		checkCudaErrors(cudaEventRecord(cudaEvent[iConv & 1], cudaStream[0]));
374
	}
375
376
	else
	{
377
		checkCudaErrors(cudaStreamSynchronize(cudaStream[0]));
378
379
380
381
	}
	return(0);
}

David Rohr's avatar
David Rohr committed
382
383
384
int bioem_cuda::selectCudaDevice()
{
	int count;
385
	int bestDevice = 0;
David Rohr's avatar
David Rohr committed
386
	cudaDeviceProp deviceProp;
387
388
389
390
391

	/* Initializing CUDA driver API */
	cuErrorCheck(cuInit(0));

	/* Get number of available CUDA devices */
David Rohr's avatar
David Rohr committed
392
393
394
395
396
397
	checkCudaErrors(cudaGetDeviceCount(&count));
	if (count == 0)
	{
		printf("No CUDA device detected\n");
		return(1);
	}
398
399
400
401

	/* Find the best GPU */
	long long int bestDeviceSpeed = -1, deviceSpeed = -1;
	for (int i = 0; i < count; i++)
David Rohr's avatar
David Rohr committed
402
	{
403
404
		cudaGetDeviceProperties(&deviceProp, i);
		deviceSpeed = (long long int) deviceProp.multiProcessorCount * (long long int) deviceProp.clockRate * (long long int) deviceProp.warpSize;
David Rohr's avatar
David Rohr committed
405
406
407
408
409
410
		if (deviceSpeed > bestDeviceSpeed)
		{
			bestDevice = i;
			bestDeviceSpeed = deviceSpeed;
		}
	}
411
412

	/* Get user-specified GPU choice */
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
	if (getenv("GPUDEVICE"))
	{
		int device = atoi(getenv("GPUDEVICE"));
		if (device > count)
		{
			printf("Invalid CUDA device specified, max device number is %d\n", count);
			exit(1);
		}
#ifdef WITH_MPI
		if (device == -1)
		{
			device = mpi_rank % count;
		}
#endif
		if (device < 0)
		{
			printf("Negative CUDA device specified: %d, invalid!\n", device);
430
			exit(1);
431
432
433
		}
		bestDevice = device;
	}
David Rohr's avatar
David Rohr committed
434

435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
	/* Set CUDA processes to appropriate devices */
	cudaGetDeviceProperties(&deviceProp, bestDevice);
	if (deviceProp.computeMode == 0)
	{
		checkCudaErrors(cudaSetDevice(bestDevice));
	}
	else
	{
		if (DebugOutput >= 1)
		{
			printf("CUDA device %d is not set in DEFAULT mode, make sure that CUDA processes are pinned as planned!\n", bestDevice);
			printf("Pinning process %d to CUDA device %d\n", mpi_rank, bestDevice);
		}
		checkCudaErrors(cudaSetDevice(bestDevice));
		/* This synchronization is needed in order to detect bogus silent errors from cudaSetDevice call */
		checkCudaErrors(cudaDeviceSynchronize());
	}
David Rohr's avatar
David Rohr committed
452

453
	/* Debugging information about CUDA devices used by the current process */
David Rohr's avatar
David Rohr committed
454
	if (DebugOutput >= 3)
David Rohr's avatar
David Rohr committed
455
	{
David Rohr's avatar
David Rohr committed
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
		printf("Using CUDA Device %s with Properties:\n", deviceProp.name);
		printf("totalGlobalMem = %lld\n", (unsigned long long int) deviceProp.totalGlobalMem);
		printf("sharedMemPerBlock = %lld\n", (unsigned long long int) deviceProp.sharedMemPerBlock);
		printf("regsPerBlock = %d\n", deviceProp.regsPerBlock);
		printf("warpSize = %d\n", deviceProp.warpSize);
		printf("memPitch = %lld\n", (unsigned long long int) deviceProp.memPitch);
		printf("maxThreadsPerBlock = %d\n", deviceProp.maxThreadsPerBlock);
		printf("maxThreadsDim = %d %d %d\n", deviceProp.maxThreadsDim[0], deviceProp.maxThreadsDim[1], deviceProp.maxThreadsDim[2]);
		printf("maxGridSize = %d %d %d\n", deviceProp.maxGridSize[0], deviceProp.maxGridSize[1], deviceProp.maxGridSize[2]);
		printf("totalConstMem = %lld\n", (unsigned long long int) deviceProp.totalConstMem);
		printf("major = %d\n", deviceProp.major);
		printf("minor = %d\n", deviceProp.minor);
		printf("clockRate = %d\n", deviceProp.clockRate);
		printf("memoryClockRate = %d\n", deviceProp.memoryClockRate);
		printf("multiProcessorCount = %d\n", deviceProp.multiProcessorCount);
		printf("textureAlignment = %lld\n", (unsigned long long int) deviceProp.textureAlignment);
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
		printf("computeMode = %d\n", deviceProp.computeMode);
#if CUDA_VERSION > 3010
		size_t free, total;
#else
		unsigned int free, total;
#endif
		if (deviceProp.computeMode == 0)
		{
			CUdevice tmpDevice;
			cuErrorCheck(cuDeviceGet(&tmpDevice, bestDevice));
			CUcontext tmpContext;
			cuErrorCheck(cuCtxCreate(&tmpContext, 0, tmpDevice));
			cuErrorCheck(cuMemGetInfo(&free, &total));
			cuErrorCheck(cuCtxDestroy(tmpContext));
		}
		else
		{
			cuErrorCheck(cuMemGetInfo(&free, &total));
		}
		printf("free memory = %lld; total memory = %lld\n", free, total);
David Rohr's avatar
David Rohr committed
492
	}
493

David Rohr's avatar
David Rohr committed
494
495
	if (DebugOutput >= 1)
	{
David Rohr's avatar
David Rohr committed
496
		printf("BioEM for CUDA initialized (MPI Rank %d), %d GPUs found, using GPU %d\n", mpi_rank, count, bestDevice);
David Rohr's avatar
David Rohr committed
497
	}
498

David Rohr's avatar
David Rohr committed
499
500
501
	return(0);
}

502
503
504
int bioem_cuda::deviceInit()
{
	deviceExit();
David Rohr's avatar
David Rohr committed
505
	
506
	selectCudaDevice();
507

508
509
	if (FFTAlgo) GPUAlgo = 2;

510
511
512
513
514
	gpumap = new bioem_RefMap;
	memcpy(gpumap, &RefMap, sizeof(bioem_RefMap));
	if (FFTAlgo == 0)
	{
		checkCudaErrors(cudaMalloc(&maps, sizeof(myfloat_t) * RefMap.ntotRefMap * RefMap.refMapSize));
515
516
517
518
519
520
521
522
523
524
525
526
527

		if (GPUAlgo == 0 || GPUAlgo == 1)
		{
			pRefMap_device_Mod = (bioem_RefMap_Mod*) gpumap;
			bioem_RefMap_Mod* RefMapGPU = new bioem_RefMap_Mod;
			RefMapGPU->init(RefMap);
			checkCudaErrors(cudaMemcpy(maps, RefMapGPU->maps, sizeof(myfloat_t) * RefMap.ntotRefMap * RefMap.refMapSize, cudaMemcpyHostToDevice));
			delete RefMapGPU;
		}
		else
		{
			checkCudaErrors(cudaMemcpy(maps, RefMap.maps, sizeof(myfloat_t) * RefMap.ntotRefMap * RefMap.refMapSize, cudaMemcpyHostToDevice));
		}
528
529
530
531
532
533
534
535
536
	}
	checkCudaErrors(cudaMalloc(&sum, sizeof(myfloat_t) * RefMap.ntotRefMap));
	checkCudaErrors(cudaMemcpy(sum, RefMap.sum_RefMap, sizeof(myfloat_t) * RefMap.ntotRefMap, cudaMemcpyHostToDevice));
	checkCudaErrors(cudaMalloc(&sumsquare, sizeof(myfloat_t) * RefMap.ntotRefMap));
	checkCudaErrors(cudaMemcpy(sumsquare, RefMap.sumsquare_RefMap, sizeof(myfloat_t) * RefMap.ntotRefMap, cudaMemcpyHostToDevice));
	gpumap->maps = maps;
	gpumap->sum_RefMap = sum;
	gpumap->sumsquare_RefMap = sumsquare;

537
	checkCudaErrors(cudaMalloc(&pProb_memory, pProb_device.get_size(RefMap.ntotRefMap, param.nTotGridAngles, param.nTotCC, param.param_device.writeAngles, param.param_device.writeCC)));
538

539
	for (int i = 0; i < 2; i++)
540
	{
541
		checkCudaErrors(cudaStreamCreate(&cudaStream[i]));
542
		checkCudaErrors(cudaEventCreate(&cudaEvent[i]));
543
		checkCudaErrors(cudaEventCreate(&cudaFFTEvent[i]));
544
		checkCudaErrors(cudaMalloc(&pConvMap_device[i], sizeof(myfloat_t) * RefMap.refMapSize));
545
	}
546
547
548
549
550
	if (GPUAsync)
	{
		checkCudaErrors(cudaStreamCreate(&cudaStream[2]));
		checkCudaErrors(cudaEventCreate(&cudaEvent[2]));
	}
551

552
553
	if (FFTAlgo)
	{
554
		checkCudaErrors(cudaMalloc(&pRefMapsFFT, RefMap.ntotRefMap * param.FFTMapSize * sizeof(mycomplex_t)));
555
556
557
558
		checkCudaErrors(cudaMalloc(&pFFTtmp2[0], CUDA_FFTS_AT_ONCE * param.FFTMapSize * 2 * sizeof(mycomplex_t)));
		checkCudaErrors(cudaMalloc(&pFFTtmp[0], CUDA_FFTS_AT_ONCE * param.param_device.NumberPixels * param.param_device.NumberPixels * 2 * sizeof(myfloat_t)));
		pFFTtmp2[1] = pFFTtmp2[0] + CUDA_FFTS_AT_ONCE * param.FFTMapSize;
		pFFTtmp[1] = pFFTtmp[0] + CUDA_FFTS_AT_ONCE * param.param_device.NumberPixels * param.param_device.NumberPixels;
559
		checkCudaErrors(cudaMalloc(&pConvMapFFT, param.FFTMapSize * sizeof(mycomplex_t) * 2));
560
		checkCudaErrors(cudaHostAlloc(&pConvMapFFT_Host, param.FFTMapSize * sizeof(mycomplex_t) * 2, 0));
561
		checkCudaErrors(cudaMemcpy(pRefMapsFFT, RefMap.RefMapsFFT, RefMap.ntotRefMap * param.FFTMapSize * sizeof(mycomplex_t), cudaMemcpyHostToDevice));
562
563
	}

564
565
566
567
568
569
570
	deviceInitialized = 1;
	return(0);
}

int bioem_cuda::deviceExit()
{
	if (deviceInitialized == 0) return(0);
571

572

David Rohr's avatar
David Rohr committed
573
	cudaFree(pProb_memory);
574
575
	cudaFree(sum);
	cudaFree(sumsquare);
576
	for (int i = 0; i < 2; i++)
577
	{
578
		cudaStreamDestroy(cudaStream[i]);
579
		cudaEventDestroy(cudaEvent[i]);
580
		cudaEventDestroy(cudaFFTEvent[i]);
581
		cudaFree(pConvMap_device[i]);
582
	}
583
584
585
586
	if (FFTAlgo)
	{
		cudaFree(pRefMapsFFT);
		cudaFree(pConvMapFFT);
587
		cudaFreeHost(pConvMapFFT_Host);
588
589
		cudaFree(pFFTtmp[0]);
		cudaFree(pFFTtmp2[0]);
590
	}
591
592
593
594
595
596
597
598
	else
	{
		cudaFree(maps);
	}
	if (GPUAlgo == 0 || GPUAlgo == 1)
	{
		cudaFree(pRefMap_device_Mod);
	}
599
600
601
602
603
604
	if (GPUAsync)
	{
		cudaStreamDestroy(cudaStream[2]);
		cudaEventDestroy(cudaEvent[2]);
	}

605
	delete gpumap;
606
	cudaThreadExit();
607

608
609
610
611
612
613
	deviceInitialized = 0;
	return(0);
}

int bioem_cuda::deviceStartRun()
{
David Rohr's avatar
David Rohr committed
614
615
616
617
618
619
620
	if (GPUWorkload >= 100)
	{
		maxRef = RefMap.ntotRefMap;
		pProb_host = &pProb;
	}
	else
	{
621
		maxRef = RefMap.ntotRefMap == 1 ? (size_t) RefMap.ntotRefMap : (size_t) RefMap.ntotRefMap * (size_t) GPUWorkload / 100;
David Rohr's avatar
David Rohr committed
622
		pProb_host = new bioem_Probability;
623
		pProb_host->init(maxRef, param.nTotGridAngles, param.nTotCC, *this);
David Rohr's avatar
David Rohr committed
624
625
		pProb_host->copyFrom(&pProb, *this);
	}
626

David Rohr's avatar
David Rohr committed
627
628
629
	pProb_device = *pProb_host;
	pProb_device.ptr = pProb_memory;
	pProb_device.set_pointers();
630
	checkCudaErrors(cudaMemcpyAsync(pProb_device.ptr, pProb_host->ptr, pProb_host->get_size(maxRef, param.nTotGridAngles, param.nTotCC, param.param_device.writeAngles, param.param_device.writeCC), cudaMemcpyHostToDevice, cudaStream[0]));
631
632
633

	if (FFTAlgo)
	{
634
		for (int j = 0;j < 2;j++)
635
		{
636
			for (int i = 0; i < 2; i++)
637
			{
638
				if (i && maxRef % CUDA_FFTS_AT_ONCE == 0) continue;
639
640
641
642
643
644
				int n[2] = {param.param_device.NumberPixels, param.param_device.NumberPixels};
				if (cufftPlanMany(&plan[i][j], 2, n, NULL, 1, param.FFTMapSize, NULL, 1, 0, MY_CUFFT_C2R, i ? (maxRef % CUDA_FFTS_AT_ONCE) : CUDA_FFTS_AT_ONCE) != CUFFT_SUCCESS)
				{
					cout << "Error planning CUFFT\n";
					exit(1);
				}
645
			        if (cufftSetCompatibilityMode(plan[i][j], CUFFT_COMPATIBILITY_FFTW_PADDING) != CUFFT_SUCCESS)
646
647
648
649
650
651
652
653
654
				{
					cout << "Error planning CUFFT compatibility\n";
					exit(1);
				}
				if (cufftSetStream(plan[i][j], cudaStream[j]) != CUFFT_SUCCESS)
				{
					cout << "Error setting CUFFT stream\n";
					exit(1);
				}
655
			}
656
			if (!GPUDualStream) break;
657
658
		}
	}
659
660
661
662
663
	return(0);
}

int bioem_cuda::deviceFinishRun()
{
664
	if (GPUAsync) cudaStreamSynchronize(cudaStream[0]);
665
	checkCudaErrors(cudaMemcpyAsync(pProb_host->ptr, pProb_device.ptr, pProb_host->get_size(maxRef, param.nTotGridAngles, param.nTotCC, param.param_device.writeAngles, param.param_device.writeCC), cudaMemcpyDeviceToHost, cudaStream[0]));
666

667
668
	if (FFTAlgo)
	{
669
670
		for (int j = 0;j < 2;j++)
		{
671
672
673
674
675
			for (int i = 0; i < 2; i++)
			{
				if (i && maxRef % CUDA_FFTS_AT_ONCE == 0) continue;
				cufftDestroy(plan[i][j]);
			}
676
677
			if (!GPUDualStream) break;
		}
678
	}
David Rohr's avatar
David Rohr committed
679
680
681
682
	cudaThreadSynchronize();
	if (GPUWorkload < 100)
	{
		pProb.copyFrom(pProb_host, *this);
683
		free_device_host(pProb_host->ptr);
David Rohr's avatar
David Rohr committed
684
685
		delete[] pProb_host;
	}
686

687
688
689
	return(0);
}

690
691
692
693
694
695
696
697
698
699
700
701
void* bioem_cuda::malloc_device_host(size_t size)
{
	void* ptr;
	checkCudaErrors(cudaHostAlloc(&ptr, size, 0));
	return(ptr);
}

void bioem_cuda::free_device_host(void* ptr)
{
	cudaFreeHost(ptr);
}

702
703
bioem* bioem_cuda_create()
{
David Rohr's avatar
David Rohr committed
704
705
706
707
708
709
710
711
712
	int count;
	
	if (cudaGetDeviceCount(&count) != cudaSuccess) count = 0;
	if (count == 0)
	{
		printf("No CUDA device available, using fallback to CPU version\n");
		return new bioem;
	}

713
714
	return new bioem_cuda;
}