const char * what() const throw() {
return cudaGetErrorString(err_num);
}
int
APPLY_SPECIFIC(conv_fwd)(PyGpuArrayObject *input, PyGpuArrayObject *kerns,
PyGpuArrayObject *om,
cudnnConvolutionDescriptor_t desc,
double alpha, double beta,
PyGpuArrayObject **output,
PyGpuContextObject *c) {
cudnnStatus_t err = CUDNN_STATUS_SUCCESS;
float af = alpha, bf = beta;
void *alpha_p;
void *beta_p;
if (PyGpuArray_DIMS(input)[1] != PyGpuArray_DIMS(kerns)[1]) {
PyErr_SetString(PyExc_ValueError,
"images and kernel must have the same stack size");
return 1;
}
if (c_set_tensorNd(input, APPLY_SPECIFIC(input)) == -1)
return 1;
if (c_set_filter(kerns, APPLY_SPECIFIC(kerns)) == -1)
return 1;
switch (input->ga.typecode) {
case GA_DOUBLE:
alpha_p = (void *)α
beta_p = (void *)β
break;
case GA_FLOAT:
case GA_HALF:
alpha_p = (void *)⁡
beta_p = (void *)&bf;
break;
default:
PyErr_SetString(PyExc_TypeError, "Unsupported type in convolution");
return 1;
}
#ifdef CONV_INPLACE
Py_XDECREF(*output);
*output = om;
Py_INCREF(*output);
#else
if (theano_prep_output(output, PyGpuArray_NDIM(om), PyGpuArray_DIMS(om),
om->ga.typecode, GA_C_ORDER, c) != 0)
return 1;
if (beta != 0.0 && pygpu_move(*output, om))
return 1;
#endif
if (c_set_tensorNd(*output, APPLY_SPECIFIC(output)) == -1)
return 1;
cudnnConvolutionFwdAlgo_t algo = CONV_ALGO;
cuda_enter(c->ctx);
#ifdef CHOOSE_ALGO
/* Static variables are only initialized once so this will not
* reset the previous algo every time */
static int reuse_algo = 0;
static cudnnConvolutionFwdAlgo_t prev_algo = CONV_ALGO;
#ifndef CHOOSE_ONCE
static size_t prev_img_dims[5] = {0};
static size_t prev_kern_dims[5] = {0};
reuse_algo = 1;
for (unsigned int i = 0; i < PyGpuArray_NDIM(input); i++) {
reuse_algo = (reuse_algo &&
PyGpuArray_DIM(input, i) == prev_img_dims[i]);
reuse_algo = (reuse_algo &&
PyGpuArray_DIM(kerns, i) == prev_kern_dims[i]);
}
#endif
if (!reuse_algo) {
#ifdef CHOOSE_TIME
int count;
cudnnConvolutionFwdAlgoPerf_t choice;
err = cudnnFindConvolutionForwardAlgorithm(
APPLY_SPECIFIC(_handle), APPLY_SPECIFIC(input), APPLY_SPECIFIC(kerns),
desc, APPLY_SPECIFIC(output), 1, &count, &choice);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError,
"error selecting convolution algo: %s",
cudnnGetErrorString(err));
cuda_exit(c->ctx);
return 1;
}
algo = choice.algo;
#else
size_t free = 0, total = 0;
cudaError_t err2 = cudaMemGetInfo(&free, &total);
if (err2 != cudaSuccess) {
PyErr_Format(PyExc_RuntimeError, "Error when trying to find the "
"memory information on the GPU: %s\n",
cudaGetErrorString(err2));
cuda_exit(c->ctx);
return 1;
}
err = cudnnGetConvolutionForwardAlgorithm(
APPLY_SPECIFIC(_handle), APPLY_SPECIFIC(input), APPLY_SPECIFIC(kerns),
desc, APPLY_SPECIFIC(output),
CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT, free, &algo);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError,
"error selecting convolution algo: %s",
cudnnGetErrorString(err));
cuda_exit(c->ctx);
return 1;
}
#endif
prev_algo = algo;
} else {
algo = prev_algo;
}
#ifdef CHOOSE_ONCE
reuse_algo = 1;
#else
for (unsigned int i = 0; i < PyGpuArray_NDIM(input); i++) {
prev_img_dims[i] = PyGpuArray_DIM(input, i);
prev_kern_dims[i] = PyGpuArray_DIM(kerns, i);
}
#endif
#endif
/* These two algos are not supported for 3d conv */
if (PyGpuArray_NDIM(input) == 5 &&
(algo == CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM ||
algo == CUDNN_CONVOLUTION_FWD_ALGO_GEMM))
algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;
#if CUDNN_VERSION > 3000
if (algo == CUDNN_CONVOLUTION_FWD_ALGO_FFT) {
int nd;
int pad[2];
int stride[2];
int upscale[2];
cudnnConvolutionMode_t mode;
err = cudnnGetConvolutionNdDescriptor(desc, 2, &nd, pad, stride,
upscale, &mode);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError,
"error getting convolution properties: %s",
cudnnGetErrorString(err));
cuda_exit(c->ctx);
return 1;
}
if (stride[0] != 1 || stride[1] != 1 ||
PyGpuArray_DIM(input, 2) > 1024 || PyGpuArray_DIM(input, 3) > 1024 ||
(PyGpuArray_DIM(kerns, 2) == 1 && PyGpuArray_DIM(kerns, 3) == 1)) {
algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
}
}
#endif
#if CUDNN_VERSION < 3000
/* cuDNN before v3 does not support kernels larger than input even
* if appropriate padding is selected. */
for (unsigned int i = 2; i < PyGpuArray_NDIM(input); i++) {
if (PyGpuArray_DIM(kerns, i) > PyGpuArray_DIM(input, i)) {
PyErr_SetString(PyExc_RuntimeError, "the current version "
"of CuDNN does not support kernels larger than the "
"inputs in any spatial dimension, even if the inputs "
"are padded such that the padded inputs are larger "
"than the kernels. Update your installation of CuDNN "
"to V3 or more recent to solve the issue.");
cuda_exit(c->ctx);
return 1;
}
}
#endif
{
size_t worksize;
gpudata *workspace;
err = cudnnGetConvolutionForwardWorkspaceSize(APPLY_SPECIFIC(_handle),
APPLY_SPECIFIC(input),
APPLY_SPECIFIC(kerns),
desc,
APPLY_SPECIFIC(output),
algo,
&worksize);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError,
"error getting worksize: %s",
cudnnGetErrorString(err));
cuda_exit(c->ctx);
return 1;
}
/*
* This is less than ideal since we need to free it after (which
* introduces a synchronization point. But we don't have a module
* to place a nice get_work_mem() function in.
*/
if (worksize != 0) {
workspace = c->ops->buffer_alloc(c->ctx, worksize, NULL, 0, NULL);
if (workspace == NULL) {
PyErr_SetString(PyExc_RuntimeError,
"Could not allocate working memory");
cuda_exit(c->ctx);
return 1;
}
}
err = cudnnConvolutionForward(
APPLY_SPECIFIC(_handle),
alpha_p,
APPLY_SPECIFIC(input), PyGpuArray_DEV_DATA(input),
APPLY_SPECIFIC(kerns), PyGpuArray_DEV_DATA(kerns),
desc, algo,
worksize == 0 ? NULL : *(void **)workspace, worksize,
beta_p,
APPLY_SPECIFIC(output), PyGpuArray_DEV_DATA(*output));
if (worksize != 0)
c->ops->buffer_release(workspace);
}
cuda_exit(c->ctx);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError, "error doing operation: %s",
cudnnGetErrorString(err));
return 1;
}
return 0;
}
void oskar_cuda_device_info_scan(oskar_CudaDeviceInfo* device, int id)
{
int arch, device_count = 0;
cudaError_t error;
struct cudaDeviceProp device_prop;
size_t total_memory = 0, free_memory = 0;
/* Set CUDA device. */
cudaSetDevice(id);
/* Set default values in case of errors. */
device->name[0] = 0;
device->compute.capability.major = 0;
device->compute.capability.minor = 0;
device->supports_double = 0;
device->global_memory_size = 0;
device->free_memory = 0;
device->num_multiprocessors = 0;
device->num_cores = 0;
device->gpu_clock = 0;
device->memory_clock = 0;
device->memory_bus_width = 0;
device->level_2_cache_size = 0;
device->shared_memory_size = 0;
device->num_registers = 0;
device->warp_size = 0;
device->max_threads_per_block = 0;
device->max_threads_dim[0] = 0;
device->max_threads_dim[1] = 0;
device->max_threads_dim[2] = 0;
device->max_grid_size[0] = 0;
device->max_grid_size[1] = 0;
device->max_grid_size[2] = 0;
/* Get device count. */
error = cudaGetDeviceCount(&device_count);
if (error != cudaSuccess || device_count == 0)
{
fprintf(stderr, "Unable to determine number of CUDA devices: %s\n",
cudaGetErrorString(error));
return;
}
/* Check device ID is within range. */
if (id > device_count - 1)
{
fprintf(stderr, "Error: Device ID out of range.\n");
return;
}
/* Get device properties. */
cudaGetDeviceProperties(&device_prop, id);
strcpy(device->name, device_prop.name);
device->compute.capability.major = device_prop.major;
device->compute.capability.minor = device_prop.minor;
device->supports_double = 0;
if (device_prop.major >= 2 || device_prop.minor >= 3)
device->supports_double = 1;
total_memory = device_prop.totalGlobalMem / 1024;
device->global_memory_size = total_memory;
device->num_multiprocessors = device_prop.multiProcessorCount;
arch = (device_prop.major << 4) + device_prop.minor;
switch (arch)
{
case 0x10:
case 0x11:
case 0x12:
case 0x13:
device->num_cores = 8;
break;
case 0x20:
device->num_cores = 32;
break;
case 0x21:
device->num_cores = 48;
break;
case 0x30:
case 0x32:
case 0x35:
case 0x37:
device->num_cores = 192;
break;
case 0x50:
case 0x52:
case 0x53:
device->num_cores = 128;
break;
case 0x60:
device->num_cores = 64;
break;
case 0x61:
case 0x62:
device->num_cores = 128;
break;
default:
device->num_cores = -1;
break;
}
if (device->num_cores > 0)
device->num_cores *= device->num_multiprocessors;
device->gpu_clock = device_prop.clockRate;
#if CUDART_VERSION >= 4000
device->memory_clock = device_prop.memoryClockRate;
device->memory_bus_width = device_prop.memoryBusWidth;
device->level_2_cache_size = device_prop.l2CacheSize;
#else
device->memory_clock = -1;
device->memory_bus_width = -1;
device->level_2_cache_size = -1;
#endif
/* Get free memory size. */
cudaMemGetInfo(&free_memory, &total_memory);
free_memory /= 1024;
device->free_memory = free_memory;
/* Get block properties. */
device->shared_memory_size = device_prop.sharedMemPerBlock;
device->num_registers = device_prop.regsPerBlock;
device->warp_size = device_prop.warpSize;
device->max_threads_per_block = device_prop.maxThreadsPerBlock;
device->max_threads_dim[0] = device_prop.maxThreadsDim[0];
device->max_threads_dim[1] = device_prop.maxThreadsDim[1];
device->max_threads_dim[2] = device_prop.maxThreadsDim[2];
device->max_grid_size[0] = device_prop.maxGridSize[0];
device->max_grid_size[1] = device_prop.maxGridSize[1];
device->max_grid_size[2] = device_prop.maxGridSize[2];
}
////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////
int
main( int argc, char** argv)
{
pArgc = &argc;
pArgv = argv;
shrQAStart(argc, argv);
shrSetLogFileName ("deviceQuery.txt");
shrLog("%s Starting...\n\n", argv[0]);
shrLog(" CUDA Device Query (Runtime API) version (CUDART static linking)\n\n");
int deviceCount = 0;
cudaError_t error_id = cudaGetDeviceCount(&deviceCount);
if (error_id != cudaSuccess) {
shrLog( "cudaGetDeviceCount returned %d\n-> %s\n", (int)error_id, cudaGetErrorString(error_id) );
shrQAFinishExit(*pArgc, (const char **)pArgv, QA_FAILED);
}
// This function call returns 0 if there are no CUDA capable devices.
if (deviceCount == 0)
shrLog("There is no device supporting CUDA\n");
else
shrLog("Found %d CUDA Capable device(s)\n", deviceCount);
int dev, driverVersion = 0, runtimeVersion = 0;
for (dev = 0; dev < deviceCount; ++dev) {
cudaDeviceProp deviceProp;
cudaGetDeviceProperties(&deviceProp, dev);
shrLog("\nDevice %d: \"%s\"\n", dev, deviceProp.name);
#if CUDART_VERSION >= 2020
// Console log
cudaDriverGetVersion(&driverVersion);
cudaRuntimeGetVersion(&runtimeVersion);
shrLog(" CUDA Driver Version / Runtime Version %d.%d / %d.%d\n", driverVersion/1000, (driverVersion%100)/10, runtimeVersion/1000, (runtimeVersion%100)/10);
#endif
shrLog(" CUDA Capability Major/Minor version number: %d.%d\n", deviceProp.major, deviceProp.minor);
char msg[256];
sprintf(msg, " Total amount of global memory: %.0f MBytes (%llu bytes)\n",
(float)deviceProp.totalGlobalMem/1048576.0f, (unsigned long long) deviceProp.totalGlobalMem);
shrLog(msg);
#if CUDART_VERSION >= 2000
shrLog(" (%2d) Multiprocessors x (%3d) CUDA Cores/MP: %d CUDA Cores\n",
deviceProp.multiProcessorCount,
ConvertSMVer2Cores(deviceProp.major, deviceProp.minor),
ConvertSMVer2Cores(deviceProp.major, deviceProp.minor) * deviceProp.multiProcessorCount);
#endif
shrLog(" GPU Clock rate: %.0f MHz (%0.2f GHz)\n", deviceProp.clockRate * 1e-3f, deviceProp.clockRate * 1e-6f);
#if CUDART_VERSION >= 4000
// This is not available in the CUDA Runtime API, so we make the necessary calls the driver API to support this for output
int memoryClock;
getCudaAttribute<int>( &memoryClock, CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE, dev );
shrLog(" Memory Clock rate: %.0f Mhz\n", memoryClock * 1e-3f);
int memBusWidth;
getCudaAttribute<int>( &memBusWidth, CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH, dev );
shrLog(" Memory Bus Width: %d-bit\n", memBusWidth);
int L2CacheSize;
getCudaAttribute<int>( &L2CacheSize, CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE, dev );
if (L2CacheSize) {
shrLog(" L2 Cache Size: %d bytes\n", L2CacheSize);
}
shrLog(" Max Texture Dimension Size (x,y,z) 1D=(%d), 2D=(%d,%d), 3D=(%d,%d,%d)\n",
deviceProp.maxTexture1D, deviceProp.maxTexture2D[0], deviceProp.maxTexture2D[1],
deviceProp.maxTexture3D[0], deviceProp.maxTexture3D[1], deviceProp.maxTexture3D[2]);
shrLog(" Max Layered Texture Size (dim) x layers 1D=(%d) x %d, 2D=(%d,%d) x %d\n",
deviceProp.maxTexture1DLayered[0], deviceProp.maxTexture1DLayered[1],
deviceProp.maxTexture2DLayered[0], deviceProp.maxTexture2DLayered[1], deviceProp.maxTexture2DLayered[2]);
#endif
shrLog(" Total amount of constant memory: %u bytes\n", deviceProp.totalConstMem);
shrLog(" Total amount of shared memory per block: %u bytes\n", deviceProp.sharedMemPerBlock);
shrLog(" Total number of registers available per block: %d\n", deviceProp.regsPerBlock);
shrLog(" Warp size: %d\n", deviceProp.warpSize);
shrLog(" Maximum number of threads per multiprocessor: %d\n", deviceProp.maxThreadsPerMultiProcessor);
shrLog(" Maximum number of threads per block: %d\n", deviceProp.maxThreadsPerBlock);
shrLog(" Maximum sizes of each dimension of a block: %d x %d x %d\n",
deviceProp.maxThreadsDim[0],
deviceProp.maxThreadsDim[1],
deviceProp.maxThreadsDim[2]);
shrLog(" Maximum sizes of each dimension of a grid: %d x %d x %d\n",
deviceProp.maxGridSize[0],
deviceProp.maxGridSize[1],
deviceProp.maxGridSize[2]);
shrLog(" Maximum memory pitch: %u bytes\n", deviceProp.memPitch);
shrLog(" Texture alignment: %u bytes\n", deviceProp.textureAlignment);
#if CUDART_VERSION >= 4000
shrLog(" Concurrent copy and execution: %s with %d copy engine(s)\n", (deviceProp.deviceOverlap ? "Yes" : "No"), deviceProp.asyncEngineCount);
#else
shrLog(" Concurrent copy and execution: %s\n", deviceProp.deviceOverlap ? "Yes" : "No");
#endif
#if CUDART_VERSION >= 2020
shrLog(" Run time limit on kernels: %s\n", deviceProp.kernelExecTimeoutEnabled ? "Yes" : "No");
shrLog(" Integrated GPU sharing Host Memory: %s\n", deviceProp.integrated ? "Yes" : "No");
shrLog(" Support host page-locked memory mapping: %s\n", deviceProp.canMapHostMemory ? "Yes" : "No");
#endif
#if CUDART_VERSION >= 3000
shrLog(" Concurrent kernel execution: %s\n", deviceProp.concurrentKernels ? "Yes" : "No");
shrLog(" Alignment requirement for Surfaces: %s\n", deviceProp.surfaceAlignment ? "Yes" : "No");
#endif
#if CUDART_VERSION >= 3010
shrLog(" Device has ECC support enabled: %s\n", deviceProp.ECCEnabled ? "Yes" : "No");
#endif
#if CUDART_VERSION >= 3020
shrLog(" Device is using TCC driver mode: %s\n", deviceProp.tccDriver ? "Yes" : "No");
#endif
#if CUDART_VERSION >= 4000
shrLog(" Device supports Unified Addressing (UVA): %s\n", deviceProp.unifiedAddressing ? "Yes" : "No");
shrLog(" Device PCI Bus ID / PCI location ID: %d / %d\n", deviceProp.pciBusID, deviceProp.pciDeviceID );
#endif
#if CUDART_VERSION >= 2020
const char *sComputeMode[] = {
"Default (multiple host threads can use ::cudaSetDevice() with device simultaneously)",
"Exclusive (only one host thread in one process is able to use ::cudaSetDevice() with this device)",
"Prohibited (no host thread can use ::cudaSetDevice() with this device)",
"Exclusive Process (many threads in one process is able to use ::cudaSetDevice() with this device)",
"Unknown",
NULL
};
shrLog(" Compute Mode:\n");
shrLog(" < %s >\n", sComputeMode[deviceProp.computeMode]);
#endif
}
// csv masterlog info
// *****************************
// exe and CUDA driver name
shrLog("\n");
std::string sProfileString = "deviceQuery, CUDA Driver = CUDART";
char cTemp[10];
// driver version
sProfileString += ", CUDA Driver Version = ";
#ifdef WIN32
sprintf_s(cTemp, 10, "%d.%d", driverVersion/1000, (driverVersion%100)/10);
#else
sprintf(cTemp, "%d.%d", driverVersion/1000, (driverVersion%100)/10);
#endif
sProfileString += cTemp;
// Runtime version
sProfileString += ", CUDA Runtime Version = ";
#ifdef WIN32
sprintf_s(cTemp, 10, "%d.%d", runtimeVersion/1000, (runtimeVersion%100)/10);
#else
sprintf(cTemp, "%d.%d", runtimeVersion/1000, (runtimeVersion%100)/10);
#endif
sProfileString += cTemp;
// Device count
sProfileString += ", NumDevs = ";
#ifdef WIN32
sprintf_s(cTemp, 10, "%d", deviceCount);
#else
sprintf(cTemp, "%d", deviceCount);
#endif
sProfileString += cTemp;
// First 2 device names, if any
for (dev = 0; dev < ((deviceCount > 2) ? 2 : deviceCount); ++dev)
{
cudaDeviceProp deviceProp;
cudaGetDeviceProperties(&deviceProp, dev);
sProfileString += ", Device = ";
sProfileString += deviceProp.name;
}
sProfileString += "\n";
shrLogEx(LOGBOTH | MASTER, 0, sProfileString.c_str());
// finish
shrQAFinishExit(argc, (const char **)argv, QA_PASSED);
}
static void _cudaHandleError(cudaError_t err, const char *file, int line) {
if (err != cudaSuccess) {
printf("%s in %s at line %d\n", cudaGetErrorString(err), file, line);
exit(EXIT_FAILURE);
}
}
static inline void ___cudaSafeCall(cudaError_t err, const char *file, const int line, const char *func = "")
{
if (cudaSuccess != err)
error(cudaGetErrorString(err), file, line, func);
}
int
APPLY_SPECIFIC(conv_gw)(PyGpuArrayObject *input, PyGpuArrayObject *output,
PyGpuArrayObject *km,
cudnnConvolutionDescriptor_t desc,
double alpha, double beta, PyGpuArrayObject **kerns,
PyGpuContextObject *c) {
cudnnStatus_t err = CUDNN_STATUS_SUCCESS;
float af = alpha, bf = beta;
void *alpha_p;
void *beta_p;
if (PyGpuArray_DIMS(input)[1] != PyGpuArray_DIMS(km)[1]) {
PyErr_SetString(PyExc_ValueError,
"GpuDnnConv images and kernel must have the same stack size");
return 1;
}
if (c_set_tensorNd(input, APPLY_SPECIFIC(input)) == -1)
return 1;
if (c_set_tensorNd(output, APPLY_SPECIFIC(output)) == -1)
return 1;
switch (input->ga.typecode) {
case GA_DOUBLE:
alpha_p = (void *)α
beta_p = (void *)β
break;
case GA_FLOAT:
case GA_HALF:
alpha_p = (void *)⁡
beta_p = (void *)&bf;
break;
default:
PyErr_SetString(PyExc_TypeError, "Unsupported type in convolution");
return 1;
}
#ifdef CONV_INPLACE
Py_XDECREF(*kerns);
*kerns = km;
Py_INCREF(*kerns);
#else
if (theano_prep_output(kerns, PyGpuArray_NDIM(km), PyGpuArray_DIMS(km),
km->ga.typecode, GA_C_ORDER, c) != 0)
return 1;
if (beta != 0.0 && pygpu_move(*kerns, km))
return 1;
#endif
if (c_set_filter(*kerns, APPLY_SPECIFIC(kerns)) == -1)
return 1;
cudnnConvolutionBwdFilterAlgo_t algo = CONV_ALGO;
cuda_enter(c->ctx);
#ifdef CHOOSE_ALGO
static int reuse_algo = 0;
static cudnnConvolutionBwdFilterAlgo_t prev_algo = CONV_ALGO;
#ifndef CHOOSE_ONCE
static size_t prev_img_dims[5] = {0};
static size_t prev_top_dims[5] = {0};
reuse_algo = 1;
for (unsigned int i = 0; i < PyGpuArray_NDIM(input); i++) {
reuse_algo = (reuse_algo &&
PyGpuArray_DIM(input, i) == prev_img_dims[i]);
reuse_algo = (reuse_algo &&
PyGpuArray_DIM(output, i) == prev_top_dims[i]);
}
#endif
if (!reuse_algo) {
#ifdef CHOOSE_TIME
int count;
cudnnConvolutionBwdFilterAlgoPerf_t choice;
err = cudnnFindConvolutionBackwardFilterAlgorithm(
APPLY_SPECIFIC(_handle), APPLY_SPECIFIC(input), APPLY_SPECIFIC(output), desc,
APPLY_SPECIFIC(kerns), 1, &count, &choice);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError,
"error selecting convolution algo: %s",
cudnnGetErrorString(err));
cuda_exit(c->ctx);
return 1;
}
algo = choice.algo;
#else
size_t free = 0, total = 0;
cudaError_t err2 = cudaMemGetInfo(&free, &total);
if (err2 != cudaSuccess){
cudaGetLastError();
PyErr_Format(PyExc_RuntimeError, "Error when trying to find the memory "
"information on the GPU: %s\n", cudaGetErrorString(err2));
cuda_exit(c->ctx);
return 1;
}
err = cudnnGetConvolutionBackwardFilterAlgorithm(
APPLY_SPECIFIC(_handle), APPLY_SPECIFIC(input), APPLY_SPECIFIC(output),
desc, APPLY_SPECIFIC(kerns),
CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT, free, &algo);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError,
"error selecting convolution algo: %s",
cudnnGetErrorString(err));
cuda_exit(c->ctx);
return 1;
}
#endif
prev_algo = algo;
} else {
algo = prev_algo;
}
#ifdef CHOOSE_ONCE
reuse_algo = 1;
#else
for (unsigned int i = 0; i < PyGpuArray_NDIM(input); i++) {
prev_img_dims[i] = PyGpuArray_DIM(input, i);
prev_top_dims[i] = PyGpuArray_DIM(output, i);
}
#endif
#endif
#if CUDNN_VERSION > 3000
if (algo == CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT) {
int nd;
int pad[2];
int stride[2];
int upscale[2];
cudnnConvolutionMode_t mode;
err = cudnnGetConvolutionNdDescriptor(desc, 2, &nd, pad, stride,
upscale, &mode);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError,
"error getting convolution properties: %s",
cudnnGetErrorString(err));
cuda_exit(c->ctx);
return 1;
}
if (stride[0] != 1 || stride[1] != 1 ||
PyGpuArray_DIM(input, 2) > 1024 || PyGpuArray_DIM(input, 3) > 1024 ||
(PyGpuArray_DIM(*kerns, 2) == 1 && PyGpuArray_DIM(*kerns, 3) == 1)) {
algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0;
}
}
#endif
size_t worksize;
gpudata *workspace;
err = cudnnGetConvolutionBackwardFilterWorkspaceSize(
APPLY_SPECIFIC(_handle), APPLY_SPECIFIC(input), APPLY_SPECIFIC(output), desc,
APPLY_SPECIFIC(kerns), algo, &worksize);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError, "error getting worksize: %s",
cudnnGetErrorString(err));
cuda_exit(c->ctx);
return 1;
}
if (worksize != 0) {
workspace = c->ops->buffer_alloc(c->ctx, worksize, NULL, 0, NULL);
if (workspace == NULL) {
PyErr_SetString(PyExc_RuntimeError, "Could not allocate working memory");
cuda_exit(c->ctx);
return 1;
}
}
cuda_wait(input->ga.data, GPUARRAY_CUDA_WAIT_READ);
cuda_wait(output->ga.data, GPUARRAY_CUDA_WAIT_READ);
cuda_wait((*kerns)->ga.data, GPUARRAY_CUDA_WAIT_WRITE);
err = cudnnConvolutionBackwardFilter_v3(
APPLY_SPECIFIC(_handle),
alpha_p,
APPLY_SPECIFIC(input), PyGpuArray_DEV_DATA(input),
APPLY_SPECIFIC(output), PyGpuArray_DEV_DATA(output),
desc, algo, worksize == 0 ? NULL : *(void **)workspace, worksize,
beta_p,
APPLY_SPECIFIC(kerns), PyGpuArray_DEV_DATA(*kerns));
if (worksize != 0)
c->ops->buffer_release(workspace);
cuda_record(input->ga.data, GPUARRAY_CUDA_WAIT_READ);
cuda_record(output->ga.data, GPUARRAY_CUDA_WAIT_READ);
cuda_record((*kerns)->ga.data, GPUARRAY_CUDA_WAIT_WRITE);
cuda_exit(c->ctx);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError, "error doing operation: %s",
cudnnGetErrorString(err));
return 1;
}
return 0;
}
void CudaSynchronizedMemory<T>::pullFromDevice(size_t nElements)
{
ASRL_ASSERT_GT_DBG(m_size,0, "The array is empty");
if(nElements > m_size)
nElements = m_size;
cudaError_t err = (cudaMemcpy((void*) m_host, (void *)m_device, nElements*sizeof(T), cudaMemcpyDeviceToHost));
ASRL_ASSERT_EQ(err, cudaSuccess, "Unable to copy " << typeid(T).name() << " array of size " << m_size << " from device (" << err << "): " << cudaGetErrorString(err));
}
void CudaSynchronizedMemory<T>::pullFromDeviceAsync(cudaStream_t stream, size_t nElements)
{
ASRL_ASSERT_GT(m_size,0, "The array is empty");
ASRL_ASSERT(m_pageLocked, "Asynchronous transfer is only valid for page-locked host memory");
if(nElements > m_size)
nElements = m_size;
cudaError_t err = (cudaMemcpyAsync((void*) m_host, (void *)m_device, nElements*sizeof(T), cudaMemcpyDeviceToHost, stream));
ASRL_ASSERT_EQ(err,cudaSuccess, "Unable to copy " << typeid(T).name() << " array of size " << m_size << " from device. Stream " << stream << ": (" << err << "): " << cudaGetErrorString(err));
}
void compute_process(int agents_total, int nreps, int world_width, int world_height)
{
int np, pid;
MPI_Comm_rank(MPI_COMM_WORLD, &pid);
MPI_Comm_size(MPI_COMM_WORLD, &np);
int server_process = np - 1;
MPI_Status status;
/* create a type for struct agent */
const int nitems=5;
int blocklengths[5] = {1,1,1,1,1};
MPI_Datatype types[5] = {MPI_INT, MPI_INT, MPI_INT, MPI_FLOAT, MPI_FLOAT};
MPI_Datatype mpi_agent_type;
MPI_Aint offsets[5];
offsets[0] = offsetof(agent, id);
offsets[1] = offsetof(agent, x);
offsets[2] = offsetof(agent, y);
offsets[3] = offsetof(agent, z);
offsets[4] = offsetof(agent, w);
MPI_Type_create_struct(nitems, blocklengths, offsets, types, &mpi_agent_type);
MPI_Type_commit(&mpi_agent_type);
unsigned int num_bytes = agents_total * sizeof(float4);
unsigned int num_halo_points = RADIO * world_width;
unsigned int num_halo_bytes = num_halo_points * sizeof(short int);
//unsigned int world_node_height = (world_height / (np-1)) + (RADIO * 2);
//if(pid == 0 or pid == np - 2)
// world_node_height -= RADIO;
size_t size_world = world_width * world_height * sizeof(short int);
short int *h_world = (short int *)malloc(size_world);
*h_world = 0;
short int *d_world;
for(int j = 0; j < world_width * world_height; j++)
{
h_world[j] = 0;
}
/* alloc host memory */
agent *h_agents_in = (agent *)malloc(num_bytes);
//agent *d_agents_in;
float4 *h_agents_pos;
float4 *d_agents_pos;
//MPI_Recv(rcv_address, num_points, MPI_FLOAT, server_process, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
MPI_Recv(h_agents_in, agents_total, mpi_agent_type, server_process, 0, MPI_COMM_WORLD, &status);
//Iniatialize world
for( int i = 0; i < agents_total; i++)
{
h_world[(world_width * (h_agents_in[i].y - 1) ) + h_agents_in[i].x] = (h_agents_in[i].x!=0?1:0);
//if(h_world[(world_width * (h_agents_in[i].y - 1) ) + h_agents_in[i].x] == 1)
//printf("world x: %d, y: %d\n", h_agents_in[i].x, h_agents_in[i].y);
h_agents_pos[i].x = h_agents_in[i].x;
h_agents_pos[i].y = h_agents_in[i].y;
h_agents_pos[i].z = h_agents_in[i].z;
h_agents_pos[i].w = h_agents_in[i].w;
}
/***
if(pid ==1)
{
int k=0;
for(int j = 0; j < world_width * world_height; j++)
{
if ( j%96 == 0 and j>0)
{
k++;
printf("%d row: %d\n", h_world[j], k);
}
else
printf("%d ", h_world[j]);
}
}
***/
// Error code to check return values for CUDA calls
cudaError_t err = cudaSuccess;
// Allocate the device pointer
err = cudaMalloc((void **)&d_world, size_world);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to allocate device pointer (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
err = cudaMemcpy(d_world, h_world, size_world, cudaMemcpyHostToDevice);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to copy pointer from host to device (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
//http://cuda-programming.blogspot.com.es/2013/02/cuda-array-in-cuda-how-to-use-cuda.html
//http://stackoverflow.com/questions/17924705/structure-of-arrays-vs-array-of-structures-in-cuda
// Allocate the device pointer
err = cudaMalloc((void **)&d_agents_pos, num_bytes);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to allocate device pointer (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
err = cudaMemcpy(d_agents_pos, h_agents_pos, num_bytes, cudaMemcpyHostToDevice);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to copy pointer from host to device (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
launch_kernel(d_agents_pos, d_world, world_width, world_height );
MPI_Barrier( MPI_COMM_WORLD);
#ifdef DEBUG
// printf("pid: %d\n", pid);
// display_data(h_agents_in, agents_total );
#endif
MPI_Send(h_agents_in, agents_total, mpi_agent_type, server_process, DATA_COLLECT, MPI_COMM_WORLD);
/* Release resources */
free(h_agents_in);
/*
free(h_output);
cudaFreeHost(h_left_boundary); cudaFreeHost(h_right_boundary);
cudaFreeHost(h_left_halo); cudaFreeHost(h_right_halo);
cudaFree(d_input); cudaFree(d_output);
*/
}
void GridGpu::gridding()
{
cudaError_t status = kernelCall();
if (status != cudaSuccess)
qWarning() << cudaGetErrorString(status);
}
bool
CudaGLVertexBuffer::allocate() {
int size = _numElements * _numVertices * sizeof(float);
glGenBuffers(1, &_vbo);
#if defined(GL_EXT_direct_state_access)
if (glNamedBufferDataEXT) {
glNamedBufferDataEXT(_vbo, size, 0, GL_DYNAMIC_DRAW);
} else {
#else
{
#endif
glBindBuffer(GL_ARRAY_BUFFER, _vbo);
glBufferData(GL_ARRAY_BUFFER, size, 0, GL_DYNAMIC_DRAW);
glBindBuffer(GL_ARRAY_BUFFER, 0);
}
// register vbo as cuda resource
cudaError_t err = cudaGraphicsGLRegisterBuffer(
&_cudaResource, _vbo, cudaGraphicsMapFlagsWriteDiscard);
if (err != cudaSuccess) return false;
return true;
}
void
CudaGLVertexBuffer::map() {
if (_devicePtr) return;
size_t num_bytes;
void *ptr;
cudaError_t err = cudaGraphicsMapResources(1, &_cudaResource, 0);
if (err != cudaSuccess)
Far::Error(Far::FAR_RUNTIME_ERROR,
"CudaGLVertexBuffer::map failed.\n%s\n",
cudaGetErrorString(err));
err = cudaGraphicsResourceGetMappedPointer(&ptr, &num_bytes, _cudaResource);
if (err != cudaSuccess)
Far::Error(Far::FAR_RUNTIME_ERROR,
"CudaGLVertexBuffer::map failed.\n%s\n",
cudaGetErrorString(err));
_devicePtr = ptr;
}
void
CudaGLVertexBuffer::unmap() {
if (_devicePtr == NULL) return;
cudaError_t err = cudaGraphicsUnmapResources(1, &_cudaResource, 0);
if (err != cudaSuccess)
Far::Error(Far::FAR_RUNTIME_ERROR,
"CudaGLVertexBuffer::unmap failed.\n%s\n",
cudaGetErrorString(err));
_devicePtr = NULL;
}
} // end namespace Osd
void testCuda(int m, int n, int nnz, std::vector<int>& rows, std::vector<int>& cols,
std::vector<double>& values, double* matB){
double tol=1e-9;
double start, stop, time_to_build, time_to_solve;
int cudaDevice = 0;
checkCudaErrors(cudaSetDevice(cudaDevice));
cudaDeviceProp prop;
cudaGetDeviceProperties(&prop, cudaDevice);
printf("Device Number: %d\n", cudaDevice);
printf(" Device name: %s\n", prop.name);
checkCudaErrors(cudaDeviceReset());
size_t mem_tot = 0;
size_t mem_free = 0;
cudaMemGetInfo(&mem_free, & mem_tot);
printf("\nFree memory: %d", mem_free);
MatSparse matA;
matA.setSize(m, n);
std::vector<int> I, J;
std::vector<double> V;
for (int k = 0; k < nnz; k++){
double _val = values[k];
int i = rows[k];
int j = cols[k];
if (fabs(_val) > tol){
I.push_back(i-1);
J.push_back(j-1);
V.push_back(_val);
}
}
start = second();
matA.fromTruples(I, J, V);
stop = second();
time_to_build = stop - start;
std::cerr << "Time to Build in GPU (second): " << time_to_build << std::endl;
// ******************************** GPU SOLVER ******************************** //
// --- Initialize cuSPARSE
cusolverSpHandle_t cusolver_handle = NULL; checkCudaErrors(cusolverSpCreate(&cusolver_handle));
cusparseHandle_t cusparse_handle = NULL; checkCudaErrors(cusparseCreate(&cusparse_handle));
cudaStream_t cudaStream = NULL; checkCudaErrors(cudaStreamCreate(&cudaStream));
checkCudaErrors(cusolverSpSetStream(cusolver_handle, cudaStream));
checkCudaErrors(cusparseSetStream(cusparse_handle, cudaStream));
cusparseMatDescr_t descrA; checkCudaErrors(cusparseCreateMatDescr(&descrA));
checkCudaErrors(cusparseSetMatType (descrA, CUSPARSE_MATRIX_TYPE_GENERAL));
checkCudaErrors(cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ZERO));
printf("\nAlloc GPU memory...\n");
double *d_A; checkCudaErrors(cudaMalloc(&d_A, nnz * sizeof(double)));
int *d_A_RowIndices; checkCudaErrors(cudaMalloc(&d_A_RowIndices, (m + 1) * sizeof(int)));
int *d_A_ColIndices; checkCudaErrors(cudaMalloc(&d_A_ColIndices, nnz * sizeof(int)));
double *d_x; checkCudaErrors(cudaMalloc(&d_x, m * sizeof(double)));
double *d_b; checkCudaErrors(cudaMalloc(&d_b, m * sizeof(double)));
printf("\nError: %s", cudaGetErrorString(cudaGetLastError()));
printf("\nCopying data...\n");
checkCudaErrors(cudaMemcpy(d_A, matA.valuesPtr(), nnz * sizeof(double), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_A_RowIndices, matA.RowPtr(), (m + 1) * sizeof(int), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_A_ColIndices, matA.ColIdxPtr(), nnz * sizeof(int), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpy(d_b, matB, m * sizeof(double), cudaMemcpyHostToDevice));
double *h_x = (double *)malloc(m * sizeof(double));
printf("\nError: %s", cudaGetErrorString(cudaGetLastError()));
cudaMemGetInfo(&mem_free, &mem_tot);
printf("\nFree memory: %d", mem_free);
int reorder = 0;
int singularity = 0;
start = second();
//checkCudaErrors(cusolverSpDcsrlsvluHost(cusolver_handle, Nrows, nnz, descrA, sparse.Values(),
// sparse.RowPtr(), sparse.ColIdx(), mB.values, tol, reorder, h_x, &singularity));
checkCudaErrors(cusolverSpDcsrlsvqr(cusolver_handle, m, nnz, descrA, d_A, d_A_RowIndices,
d_A_ColIndices, d_b, tol, reorder, d_x, &singularity));
checkCudaErrors(cudaDeviceSynchronize());
stop = second();
time_to_solve = stop - start;
checkCudaErrors(cudaMemcpy(h_x, d_x, m * sizeof(double), cudaMemcpyDeviceToHost));
// for (int k=0; k<mA.getNumRows(); k++) solution[k] = h_x[k];
checkCudaErrors(cusparseDestroy(cusparse_handle));
checkCudaErrors(cusolverSpDestroy(cusolver_handle));
checkCudaErrors(cudaStreamDestroy(cudaStream));
checkCudaErrors(cudaFree(d_b));
checkCudaErrors(cudaFree(d_x));
checkCudaErrors(cudaFree(d_A));
checkCudaErrors(cudaFree(d_A_RowIndices));
checkCudaErrors(cudaFree(d_A_ColIndices));
free(h_x);
std::cerr << "Time to Build in GPU (second): " << time_to_build << std::endl;
std::cerr << "Time to Solve in GPU (second): " << time_to_solve << std::endl;
std::cerr << "done!";
// ****************************************************************************** //
}
const char* WINAPI wine_cudaGetErrorString(cudaError_t error) {
WINE_TRACE("\n");
return cudaGetErrorString(error);
}
static void cuda_error(int line, cudaError_t code)
{
const char *err_str = cudaGetErrorString(code);
error("cuda error: %d %s \n", line, err_str);
}
////////////////////////////////////////////////////////////////////////////////
//! Run the Cuda part of the computation
////////////////////////////////////////////////////////////////////////////////
void refreshData(struct cudaGraphicsResource **vbo_resource)
{
int np;
MPI_Comm_size(MPI_COMM_WORLD, &np);
MPI_Status status;
int num_comp_nodes = np -1;
unsigned int num_bytes = sizeof(sAgents);
sAgents h_agents_in, h_agents_out[num_comp_nodes];
size_t size_agents = agents_total * sizeof(float4);
// map OpenGL buffer object for writing from CUDA
float4 *d_agents;
// Error code to check return values for CUDA calls
cudaError_t err = cudaSuccess;
checkCudaErrors(cudaGraphicsMapResources(1, vbo_resource, 0));
size_t num_agents_bytes;
checkCudaErrors(cudaGraphicsResourceGetMappedPointer((void **)&d_agents, &num_agents_bytes,
*vbo_resource));
//for(int process = 0; process < num_comp_nodes; process++)
//while(1)
//{
/* Wait for nodes to compute */
MPI_Barrier(MPI_COMM_WORLD);
for(int process = 0; process < num_comp_nodes; process++)
{
MPI_Recv(&h_agents_out[process], num_bytes, MPI_BYTE, process, DATA_COLLECT, MPI_COMM_WORLD, &status);
for( int i = 0; i < agents_total; i++)
{
if( h_agents_out[process].ids[i].y == 1 )
{
h_agents_in.pos[i] = h_agents_out[process].pos[i];
h_agents_in.ids[i] = h_agents_out[process].ids[i];
h_agents_in.pos[i].z = 0;
h_agents_in.pos[i].w = 1;
}
}
}
#ifdef DEBUG
printf("Final Data:\n");
display_data(h_agents_in);
#endif
/***
//pasar los valores al vbo
glBindBuffer(GL_ARRAY_BUFFER, vbo);
unsigned int size = agent_width * agent_height * 4 * sizeof(float);
glBufferSubData(GL_ARRAY_BUFFER, 0, size, &h_agents_in.pos);
glBindBuffer(GL_ARRAY_BUFFER, 0);
***/
// Copy the host pointer memory to the device memory
printf("Copy pointer from the host memory to the CUDA device\n");
err = cudaMemcpy(d_agents, h_agents_in.pos, size_agents, cudaMemcpyHostToDevice);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to copy pointer from host to device (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
//glutPostRedisplay();
printf("sale de refreshData:\n");
//}
// unmap buffer object
checkCudaErrors(cudaGraphicsUnmapResources(1, vbo_resource, 0));
/* release resources */
//free(&h_agents_in);
//free(&h_agents_out);
}
int main(int argc, char **argv) {
printf("Computing Game Of Life On %d x %d Board.\n", DIM_X, DIM_Y);
int *host_current, *host_future, *host_future_naive, *host_future_cached;
int *gpu_current, *gpu_future;
clock_t start, stop;
cudaMallocHost((void**) &host_current, DIM_X * DIM_Y * sizeof(int));
cudaMallocHost((void**) &host_future, DIM_X * DIM_Y * sizeof(int));
cudaMallocHost((void**) &host_future_naive, DIM_X * DIM_Y * sizeof(int));
cudaMallocHost((void**) &host_future_cached, DIM_X * DIM_Y * sizeof(int));
assert(cudaGetLastError() == cudaSuccess);
cudaMalloc((void**) &gpu_current, DIM_X * DIM_Y * sizeof(int));
cudaMalloc((void**) &gpu_future, DIM_X * DIM_Y * sizeof(int));
printf("%s\n", cudaGetErrorString(cudaGetLastError()));
assert(cudaGetLastError() == cudaSuccess);
fill_board(host_current, 40);
add_glider(host_current);
cudaMemcpy(gpu_current, host_current, DIM_X * DIM_Y * sizeof(int), cudaMemcpyHostToDevice);
// print_board(host_current);
float time_naive, time_cached, time_cpu;
for(int i = 1; i < STEPS; i++) {
printf("=========\n");
start = clock();
naive_game_of_life_wrapper(gpu_current, gpu_future);
cudaMemcpy(host_future_naive, gpu_future, DIM_X * DIM_Y * sizeof(int), cudaMemcpyDeviceToHost);
stop = clock();
time_naive = (float)(stop - start)/CLOCKS_PER_SEC;
printf("Time for Naive GPU To Compute Next Phase: %.5f s\n", time_naive);
start = clock();
cached_game_of_life_wrapper(gpu_current, gpu_future);
cudaMemcpy(host_future_cached, gpu_future, DIM_X * DIM_Y * sizeof(int), cudaMemcpyDeviceToHost);
stop = clock();
time_cached = (float)(stop - start)/CLOCKS_PER_SEC;
printf("Time for Cached GPU To Compute Next Phase: %.5f s\n", time_cached);
start = clock();
update_board(host_current, host_future);
stop = clock();
time_cpu = (float)(stop - start)/CLOCKS_PER_SEC;
printf("Time for CPU To Compute Next Phase: %.5f s\n", time_cpu);
printf("speedup for naive = %.2f; speedup for cached = %.2f; speedup for cached over naive = %.2f\n", time_cpu/time_naive, time_cpu/time_cached, time_naive/time_cached);
check_boards(host_future, host_future_naive);
check_boards(host_future, host_future_cached);
int *temp;
temp = host_current;
host_current = host_future;
host_future = temp;
temp = gpu_current;
gpu_current = gpu_future;
gpu_future = temp;
}
cudaFree(host_future);
cudaFree(host_future_naive);
cudaFree(host_future_cached);
cudaFree(host_current);
cudaFree(gpu_current);
cudaFree(gpu_future);
return 0;
}
void compute_process()
{
int np, pid;
MPI_Comm_rank(MPI_COMM_WORLD, &pid);
MPI_Comm_size(MPI_COMM_WORLD, &np);
int server_process = np - 1;
MPI_Status status;
int num_comp_nodes = np -1;
unsigned int num_bytes = sizeof(sAgents);
unsigned int num_halo_points = RADIO * world_width;
unsigned int num_halo_bytes = num_halo_points * sizeof(int);
size_t size_world = world_width * world_height * sizeof(int);
int *h_world = (int *)malloc(size_world);
int *d_world;
int left_neighbor = (pid > 0) ? (pid - 1) : MPI_PROC_NULL;
int right_neighbor = (pid < np -2) ? (pid + 1) : MPI_PROC_NULL;
for(int j = 0; j < world_width * world_height; j++)
{
h_world[j] = 0;
}
sAgents h_agents_in, h_agents_left_node, h_agents_right_node;
float4 h_agents_pos[agents_total], h_agents_ids[agents_total];
float4 *d_agents_pos, *d_agents_ids;
unsigned int num_bytes_agents = agents_total * sizeof(float4);
int world_height_node = world_height / num_comp_nodes;
// Error code to check return values for CUDA calls
cudaError_t err = cudaSuccess;
// Allocate the device pointer
err = cudaMalloc((void **)&d_world, size_world);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to allocate device pointer (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
err = cudaMalloc((void **)&d_agents_pos, num_bytes_agents);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to allocate device pointer (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
err = cudaMalloc((void **)&d_agents_ids, num_bytes_agents);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to allocate device pointer (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
MPI_Recv(&h_agents_in, num_bytes, MPI_BYTE, server_process, 0, MPI_COMM_WORLD, &status);
for(int i = 0; i < agents_total; i++)
{
//identify the active agents according to the y coordinate and set the busy cells in the world
if( ( round(h_agents_in.pos[i].y) >= (pid * world_height_node) ) and ( round(h_agents_in.pos[i].y) < ( (pid + 1) * world_height_node ) ) )
{
h_agents_in.ids[i].y = 1;
h_world[(int)round( (world_width * (h_agents_in.pos[i].y - 1) ) + h_agents_in.pos[i].x )] = h_agents_in.ids[i].x;
}
//Copy the data to a local arrays
h_agents_pos[i] = h_agents_in.pos[i];
h_agents_ids[i] = h_agents_in.ids[i];
}
err = cudaMemcpy(d_world, h_world, size_world, cudaMemcpyHostToDevice);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to copy pointer from host to device (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
//for(int it = 0; it < nreps ; it++)
while(1)
{
int it=4;
err = cudaMemcpy(d_agents_pos, h_agents_pos, num_bytes_agents, cudaMemcpyHostToDevice);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to copy pointer from host to device (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
err = cudaMemcpy(d_agents_ids, h_agents_ids, num_bytes_agents, cudaMemcpyHostToDevice);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to copy pointer from host to device (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
launch_kernel(d_agents_pos, d_agents_ids, d_world, world_width, world_height, agent_width, agent_height, world_height_node, pid );
cudaMemcpy(h_agents_pos, d_agents_pos, num_bytes_agents, cudaMemcpyDeviceToHost);
cudaMemcpy(h_agents_ids, d_agents_ids, num_bytes_agents, cudaMemcpyDeviceToHost);
//copy the data to the struct
for( int i = 0; i < agents_total; i++)
{
h_agents_in.pos[i] = h_agents_pos[i];
h_agents_in.ids[i] = h_agents_ids[i];
}
MPI_Barrier(MPI_COMM_WORLD);
MPI_Send(&h_agents_in, num_bytes, MPI_BYTE, server_process, DATA_COLLECT, MPI_COMM_WORLD);
#ifdef DEBUG
//printf("pid: %d\n", pid);
//display_data(h_agents_in);
#endif
// send data to left, get data from right
MPI_Sendrecv(&h_agents_in, num_bytes, MPI_BYTE, left_neighbor, it, &h_agents_right_node, num_bytes, MPI_BYTE, right_neighbor, it, MPI_COMM_WORLD, &status);
// send data to right, get data from left
MPI_Sendrecv(&h_agents_in, num_bytes, MPI_BYTE, right_neighbor, it, &h_agents_left_node, num_bytes, MPI_BYTE, left_neighbor, it, MPI_COMM_WORLD, &status);
for( int i = 0; i < agents_total; i++)
{
if(pid != np-2)
{
if(h_agents_right_node.ids[i].y == 2)
{
h_agents_in.pos[i] = h_agents_right_node.pos[i];
h_agents_pos[i] = h_agents_right_node.pos[i];
h_agents_in.ids[i].y = 1;
h_agents_ids[i].y = 1;
}
}
if(pid != 0)
{
if(h_agents_left_node.ids[i].y == 3)
{
h_agents_in.pos[i] = h_agents_left_node.pos[i];
h_agents_pos[i] = h_agents_left_node.pos[i];
h_agents_in.ids[i].y = 1;
h_agents_ids[i].y = 1;
}
}
}
/***
if(pid == 1)
{
printf("pid: %d\n", pid);
display_data(h_agents_in);
display_data(h_agents_right_node);
display_data(h_agents_left_node);
}
***/
}
/* Release resources */
// free(h_agents_in);
/*
free(h_output);
cudaFreeHost(h_left_boundary); cudaFreeHost(h_right_boundary);
cudaFreeHost(h_left_halo); cudaFreeHost(h_right_halo);
cudaFree(d_input); cudaFree(d_output);
*/
}
int main(int argc, char *argv[])
{
// needed to work correctly with piped benchmarkrunner
setlinebuf(stdout);
setlinebuf(stdin);
int n_indices = 1;
int n_dimensions = 1;
char inBuf[200]; // ridiculously large input buffer.
bool isFirst = true;
do {
// Allocate memory for the arrays
int *h_indices = 0;
double *h_outputGPU = 0;
try
{
h_indices = new int [n_indices * n_dimensions];
h_outputGPU = new double [n_indices * n_dimensions];
}
catch (std::exception e)
{
std::cerr << "Caught exception: " << e.what() << std::endl;
std::cerr << "Unable to allocate CPU memory (try running with fewer vectors/dimensions)" << std::endl;
return -1;
}
int *d_indices;
double *d_output;
try
{
cudaError_t cudaResult;
cudaResult = cudaMalloc((void **)&d_indices, n_dimensions * n_indices * sizeof(int));
if (cudaResult != cudaSuccess)
{
throw std::runtime_error(cudaGetErrorString(cudaResult));
}
}
catch (std::runtime_error e)
{
std::cerr << "Caught exception: " << e.what() << std::endl;
std::cerr << "Unable to allocate GPU memory (try running with fewer vectors/dimensions)" << std::endl;
return -1;
}
// Initialize the indices (done on the host)
for(int i = 0; i < n_indices; i++) {
h_indices[i] = i;
}
// Copy the indices to the device
cudaMemcpy(d_indices, h_indices, n_dimensions * n_indices * sizeof(int), cudaMemcpyHostToDevice);
cudaDeviceSynchronize();
// Execute the QRNG on the device
int n_vec;
sobol_nikola_unsimplified(n_indices, d_indices, n_indices, &d_output, &n_vec);
cudaDeviceSynchronize();
cudaMemcpy(h_outputGPU, d_output, n_indices * n_dimensions * sizeof(double), cudaMemcpyDeviceToHost);
// Cleanup and terminate
delete h_indices;
cudaFree(d_indices);
cudaFree(d_output);
if(!isFirst) {
printf("RESULT ");
for(int i = 0; i < std::min(n_indices,10); i++)
printf("%f ", h_outputGPU[i]);
printf("\n");
}
else {
printf("OK\n");
isFirst = false;
}
delete h_outputGPU;
fgets(inBuf, 200, stdin);
if (sscanf(inBuf, "%u", &n_indices) == 0)
{
// if input is not a number, it has to be "EXIT"
if (strncmp("EXIT",inBuf,4)==0)
{
printf("OK\n");
break;
}
else
{
printf("ERROR. Bad input: %s\n", inBuf);
break;
}
}
} while (true);
cudaDeviceReset();
return -1;
}
void check_error(cudaError_t ret) const {
if( ret != cudaSuccess ) {
throw std::runtime_error(cudaGetErrorString(ret));
}
}
//--------------------------------------------------------------------------
// CUDA init
//--------------------------------------------------------------------------
bool CUDAContext::configInit( )
{
#ifdef EQUALIZER_USE_CUDA
cudaDeviceProp props;
uint32_t device = getPipe()->getDevice();
// Setup the CUDA device
if( device == LB_UNDEFINED_UINT32 )
{
device = _getFastestDeviceID();
LBWARN << "No CUDA device, using the fastest device: " << device
<< std::endl;
}
int device_count = 0;
cudaGetDeviceCount( &device_count );
LBINFO << "CUDA devices found: " << device_count << std::endl;
LBASSERT( static_cast< uint32_t >( device_count ) > device );
if( static_cast< uint32_t >( device_count ) <= device )
{
sendError( ERROR_CUDACONTEXT_DEVICE_NOTFOUND )
<< lexical_cast< std::string >( device );
return false;
}
// We assume GL interop here, otherwise use cudaSetDevice( device );
// Attention: this call requires a valid GL context!
cudaGLSetGLDevice( device );
int usedDevice = static_cast< int >( device );
#ifdef _WIN32
HGPUNV handle = 0;
if( !WGLEW_NV_gpu_affinity )
{
LBWARN <<"WGL_NV_gpu_affinity unsupported, ignoring device setting"
<< std::endl;
return true;
}
if( !wglEnumGpusNV( device, &handle ))
{
LBWARN << "wglEnumGpusNV failed : " << lunchbox::sysError << std::endl;
return false;
}
cudaWGLGetDevice( &usedDevice, handle );
#else
cudaGetDevice( &usedDevice );
#endif
LBASSERT( device == static_cast< uint32_t >( device ));
cudaGetDeviceProperties( &props, usedDevice );
cudaError_t err = cudaGetLastError();
if( cudaSuccess != err)
{
sendError( ERROR_CUDACONTEXT_INIT_FAILED ) <<
std::string( cudaGetErrorString( err ));
return false;
}
LBINFO << "Using CUDA device: " << device << std::endl;
return true;
#else
sendError( ERROR_CUDACONTEXT_MISSING_SUPPORT );
return false;
#endif
}
static inline void checkCudaError(cudaError_t err, const char* file, const int line, const char* func)
{
if (cudaSuccess != err)
cv::error(cv::Error::GpuApiCallError, cudaGetErrorString(err), func, file, line);
}
void ControlCubeCache::_reSizeCache()
{
_nLevels = _nextnLevels;
_levelCube = _nextLevelCube;
_offset = _nextOffset;
_nextnLevels = 0;
_nextLevelCube = 0;
_dimCube = exp2(_nLevels - _levelCube) + 2 * CUBE_INC;
_sizeElement = pow(_dimCube, 3);
int dimV = exp2(_nLevels);
_minValue = coordinateToIndex(vmml::vector<3,int>(0,0,0), _levelCube, _nLevels);
_maxValue = coordinateToIndex(vmml::vector<3,int>(dimV-1,dimV-1,dimV-1), _levelCube, _nLevels);
int dc = exp2(_nLevels - _levelCube);
vmml::vector<3,int> mn = _cpuCache->getMinCoord();
vmml::vector<3,int> mx = _cpuCache->getMaxCoord();
_maxC = mx - mn;
if ((mx.x() - mn.x()) % dc != 0)
_maxC[0] += dc;
if ((mx.y() - mn.y()) % dc != 0)
_maxC[1] += dc;
if ((mx.z() - mn.z()) % dc != 0)
_maxC[2] += dc;
if (cudaSuccess != cudaSetDevice(_device))
{
std::cerr<<"Control Cube Cache, error setting device: "<<cudaGetErrorString(cudaGetLastError())<<std::endl;
throw;
}
if (_memory != 0)
if (cudaSuccess != cudaFree((void*)_memory))
{
std::cerr<<"Control Cube Cache, error resizing cache: "<<cudaGetErrorString(cudaGetLastError())<<std::endl;
throw;
}
size_t total = 0;
size_t free = 0;
if (cudaSuccess != cudaMemGetInfo(&free, &total))
{
std::cerr<<"Control Cube Cache, error resizing cache: "<<cudaGetErrorString(cudaGetLastError())<<std::endl;
throw;
}
float memorySize = (0.80f*free); // Get 80% of free memory
_maxNumCubes = memorySize/ (_sizeElement*sizeof(float));
if (_maxNumCubes == 0)
{
std::cerr<<"Control Cube Cache: Memory aviable is not enough "<<memorySize/1024/1024<<" MB"<<std::endl;
throw;
}
if (cudaSuccess != cudaMalloc((void**)&_memory, _maxNumCubes*_sizeElement*sizeof(float)))
{
std::cerr<<"Control Cube Cache, error resizing cache: "<<cudaGetErrorString(cudaGetLastError())<<std::endl;
throw;
}
_freeSlots = _maxNumCubes;
ControlElementCache::_reSizeCache();
}
static void HandleError(const char *file, int line, cudaError_t err)
{
printf("ERROR in %s:%d: %s (%d)\n", file, line,
cudaGetErrorString(err), err);
exit(1);
}
void cudaAssert(cudaError code, const char *file, int line){
if (code != cudaSuccess){
fprintf(stderr,"cudaErrChk: %s %s %d\n", cudaGetErrorString(code), file, line);
//exit(code);
}
}
void MFNHashTypePlainCUDA::freeThreadAndDeviceMemory() {
trace_printf("MFNHashTypePlainCUDA::freeThreadAndDeviceMemory()\n");
cudaError_t err;
// Free all the memory, then look for errors.
cudaFree((void *)this->DeviceHashlistAddress);
cudaFreeHost((void *)this->HostSuccessAddress);
delete[] this->HostSuccessReportedAddress;
// Only cudaFree if zeroCopy is in use.
if (!this->useZeroCopy) {
cudaFree((void *)this->DeviceSuccessAddress);
cudaFree((void *)this->DeviceFoundPasswordsAddress);
}
cudaFreeHost((void *)this->HostFoundPasswordsAddress);
cudaFreeHost((void*)this->HostStartPointAddress);
cudaFree((void *)this->DeviceStartPointAddress);
cudaFree((void *)this->DeviceStartPasswords32Address);
// Free salted hashes if in use.
if (this->hashAttributes.hashUsesWordlist) {
cudaFree((void *)this->DeviceWordlistBlocks);
cudaFree((void *)this->DeviceWordlistLengths);
}
if (this->hashAttributes.hashUsesSalt) {
cudaFree((void *)this->DeviceSaltLengthsAddress);
cudaFree((void *)this->DeviceSaltValuesAddress);
}
// Only free the bitmap memory if it has been allocated.
if (this->DeviceBitmap256kb_Address) {
cudaFree((void *)this->DeviceBitmap256kb_Address);
this->DeviceBitmap256kb_Address = 0;
}
if (this->DeviceBitmap128mb_a_Address) {
cudaFree((void *)this->DeviceBitmap128mb_a_Address);
this->DeviceBitmap128mb_a_Address = 0;
}
if (this->DeviceBitmap128mb_b_Address) {
cudaFree((void *)this->DeviceBitmap128mb_b_Address);
this->DeviceBitmap128mb_b_Address = 0;
}
if (this->DeviceBitmap128mb_c_Address) {
cudaFree((void *)this->DeviceBitmap128mb_c_Address);
this->DeviceBitmap128mb_c_Address = 0;
}
if (this->DeviceBitmap128mb_d_Address) {
cudaFree((void *)this->DeviceBitmap128mb_d_Address);
this->DeviceBitmap128mb_d_Address = 0;
}
// Get any error that occurred above and report it.
err = cudaGetLastError();
if (err != cudaSuccess) {
printf("Thread %d: CUDA error freeing memory: %s. Exiting.\n",
this->threadId, cudaGetErrorString( err));
exit(1);
}
}
void gpu_data::
set_size(
size_t new_size
)
{
if (new_size == 0)
{
if (device_in_use)
{
// Wait for any possible CUDA kernels that might be using our memory block to
// complete before we free the memory.
synchronize_stream(0);
device_in_use = false;
}
wait_for_transfer_to_finish();
data_size = 0;
host_current = true;
device_current = true;
device_in_use = false;
data_host.reset();
data_device.reset();
}
else if (new_size != data_size)
{
if (device_in_use)
{
// Wait for any possible CUDA kernels that might be using our memory block to
// complete before we free the memory.
synchronize_stream(0);
device_in_use = false;
}
wait_for_transfer_to_finish();
data_size = new_size;
host_current = true;
device_current = true;
device_in_use = false;
try
{
CHECK_CUDA(cudaGetDevice(&the_device_id));
// free memory blocks before we allocate new ones.
data_host.reset();
data_device.reset();
void* data;
CHECK_CUDA(cudaMallocHost(&data, new_size*sizeof(float)));
// Note that we don't throw exceptions since the free calls are invariably
// called in destructors. They also shouldn't fail anyway unless someone
// is resetting the GPU card in the middle of their program.
data_host.reset((float*)data, [](float* ptr){
auto err = cudaFreeHost(ptr);
if(err!=cudaSuccess)
std::cerr << "cudaFreeHost() failed. Reason: " << cudaGetErrorString(err) << std::endl;
});
CHECK_CUDA(cudaMalloc(&data, new_size*sizeof(float)));
data_device.reset((float*)data, [](float* ptr){
auto err = cudaFree(ptr);
if(err!=cudaSuccess)
std::cerr << "cudaFree() failed. Reason: " << cudaGetErrorString(err) << std::endl;
});
if (!cuda_stream)
{
cudaStream_t cstream;
CHECK_CUDA(cudaStreamCreateWithFlags(&cstream, cudaStreamNonBlocking));
cuda_stream.reset(cstream, [](void* ptr){
auto err = cudaStreamDestroy((cudaStream_t)ptr);
if(err!=cudaSuccess)
std::cerr << "cudaStreamDestroy() failed. Reason: " << cudaGetErrorString(err) << std::endl;
});
}
}
catch(...)
{
set_size(0);
throw;
}
}
}
extern int scanhash_groestlcoin(int thr_id, uint32_t *pdata, uint32_t *ptarget,
uint32_t max_nonce, uint32_t *hashes_done)
{
static THREAD uint32_t *foundNounce = nullptr;
uint32_t start_nonce = pdata[19];
unsigned int intensity = (device_sm[device_map[thr_id]] > 500) ? 24 : 23;
uint32_t throughputmax = device_intensity(device_map[thr_id], __func__, 1U << intensity);
uint32_t throughput = min(throughputmax, max_nonce - start_nonce) & 0xfffffc00;
if (opt_benchmark)
ptarget[7] = 0x0000000f;
// init
static THREAD volatile bool init = false;
if(!init)
{
CUDA_SAFE_CALL(cudaSetDevice(device_map[thr_id]));
cudaSetDeviceFlags(cudaDeviceScheduleBlockingSync);
cudaDeviceSetCacheConfig(cudaFuncCachePreferL1);
CUDA_SAFE_CALL(cudaStreamCreate(&gpustream[thr_id]));
groestlcoin_cpu_init(thr_id, throughputmax);
CUDA_SAFE_CALL(cudaMallocHost(&foundNounce, 2 * 4));
init = true;
}
// Endian Drehung ist notwendig
uint32_t endiandata[32];
for (int kk=0; kk < 32; kk++)
be32enc(&endiandata[kk], pdata[kk]);
// Context mit dem Endian gedrehten Blockheader vorbereiten (Nonce wird später ersetzt)
groestlcoin_cpu_setBlock(thr_id, endiandata);
do
{
// GPU
const uint32_t Htarg = ptarget[7];
groestlcoin_cpu_hash(thr_id, throughput, pdata[19], foundNounce, ptarget[7]);
if(stop_mining) {mining_has_stopped[thr_id] = true; cudaStreamDestroy(gpustream[thr_id]); pthread_exit(nullptr);}
if(foundNounce[0] < 0xffffffff)
{
uint32_t tmpHash[8];
endiandata[19] = SWAP32(foundNounce[0]);
groestlhash(tmpHash, endiandata);
if(tmpHash[7] <= Htarg && fulltest(tmpHash, ptarget))
{
int res = 1;
if(opt_benchmark)
applog(LOG_INFO, "GPU #%d Found nounce %08x", device_map[thr_id], foundNounce[0]);
*hashes_done = pdata[19] - start_nonce + throughput;
if(foundNounce[1] != 0xffffffff)
{
endiandata[19] = SWAP32(foundNounce[1]);
groestlhash(tmpHash, endiandata);
if(tmpHash[7] <= Htarg && fulltest(tmpHash, ptarget))
{
pdata[21] = foundNounce[1];
res++;
if(opt_benchmark)
applog(LOG_INFO, "GPU #%d Found second nounce %08x", device_map[thr_id], foundNounce[1]);
}
else
{
if(tmpHash[7] != Htarg)
{
applog(LOG_WARNING, "GPU #%d: result for %08x does not validate on CPU!", device_map[thr_id], foundNounce[1]);
}
}
}
pdata[19] = foundNounce[0];
return res;
}
else
{
if(tmpHash[7] != Htarg)
{
applog(LOG_WARNING, "GPU #%d: result for %08x does not validate on CPU!", device_map[thr_id], foundNounce[0]);
}
}
}
pdata[19] += throughput;
cudaError_t err = cudaGetLastError();
if(err != cudaSuccess)
{
applog(LOG_ERR, "GPU #%d: %s", device_map[thr_id], cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
} while(!work_restart[thr_id].restart && ((uint64_t)max_nonce > ((uint64_t)(pdata[19]) + (uint64_t)throughput)));
*hashes_done = pdata[19] - start_nonce;
return 0;
}
PetscErrorCode PetscOptionsCheckInitial_Private(void)
{
char string[64],mname[PETSC_MAX_PATH_LEN],*f;
MPI_Comm comm = PETSC_COMM_WORLD;
PetscBool flg1 = PETSC_FALSE,flg2 = PETSC_FALSE,flg3 = PETSC_FALSE,flag;
PetscErrorCode ierr;
PetscReal si;
PetscInt intensity;
int i;
PetscMPIInt rank;
char version[256];
#if !defined(PETSC_HAVE_THREADSAFETY)
PetscReal logthreshold;
PetscBool flg4 = PETSC_FALSE;
#endif
PetscFunctionBegin;
ierr = MPI_Comm_rank(PETSC_COMM_WORLD,&rank);CHKERRQ(ierr);
#if !defined(PETSC_HAVE_THREADSAFETY)
/*
Setup the memory management; support for tracing malloc() usage
*/
ierr = PetscOptionsHasName(NULL,"-malloc_log",&flg3);CHKERRQ(ierr);
logthreshold = 0.0;
ierr = PetscOptionsGetReal(NULL,"-malloc_log_threshold",&logthreshold,&flg1);CHKERRQ(ierr);
if (flg1) flg3 = PETSC_TRUE;
#if defined(PETSC_USE_DEBUG)
ierr = PetscOptionsGetBool(NULL,"-malloc",&flg1,&flg2);CHKERRQ(ierr);
if ((!flg2 || flg1) && !petscsetmallocvisited) {
if (flg2 || !(PETSC_RUNNING_ON_VALGRIND)) {
/* turn off default -malloc if valgrind is being used */
ierr = PetscSetUseTrMalloc_Private();CHKERRQ(ierr);
}
}
#else
ierr = PetscOptionsGetBool(NULL,"-malloc_dump",&flg1,NULL);CHKERRQ(ierr);
ierr = PetscOptionsGetBool(NULL,"-malloc",&flg2,NULL);CHKERRQ(ierr);
if (flg1 || flg2 || flg3) {ierr = PetscSetUseTrMalloc_Private();CHKERRQ(ierr);}
#endif
if (flg3) {
ierr = PetscMallocSetDumpLogThreshold((PetscLogDouble)logthreshold);CHKERRQ(ierr);
}
flg1 = PETSC_FALSE;
ierr = PetscOptionsGetBool(NULL,"-malloc_debug",&flg1,NULL);CHKERRQ(ierr);
if (flg1) {
ierr = PetscSetUseTrMalloc_Private();CHKERRQ(ierr);
ierr = PetscMallocDebug(PETSC_TRUE);CHKERRQ(ierr);
}
flg1 = PETSC_FALSE;
ierr = PetscOptionsGetBool(NULL,"-malloc_test",&flg1,NULL);CHKERRQ(ierr);
#if defined(PETSC_USE_DEBUG)
if (flg1 && !PETSC_RUNNING_ON_VALGRIND) {
ierr = PetscSetUseTrMalloc_Private();CHKERRQ(ierr);
ierr = PetscMallocSetDumpLog();CHKERRQ(ierr);
ierr = PetscMallocDebug(PETSC_TRUE);CHKERRQ(ierr);
}
#endif
flg1 = PETSC_FALSE;
ierr = PetscOptionsGetBool(NULL,"-malloc_info",&flg1,NULL);CHKERRQ(ierr);
if (!flg1) {
flg1 = PETSC_FALSE;
ierr = PetscOptionsGetBool(NULL,"-memory_view",&flg1,NULL);CHKERRQ(ierr);
}
if (flg1) {
ierr = PetscMemorySetGetMaximumUsage();CHKERRQ(ierr);
}
#endif
#if defined(PETSC_USE_LOG)
ierr = PetscOptionsHasName(NULL,"-objects_dump",&PetscObjectsLog);CHKERRQ(ierr);
#endif
/*
Set the display variable for graphics
*/
ierr = PetscSetDisplay();CHKERRQ(ierr);
/*
Print the PETSc version information
*/
ierr = PetscOptionsHasName(NULL,"-v",&flg1);CHKERRQ(ierr);
ierr = PetscOptionsHasName(NULL,"-version",&flg2);CHKERRQ(ierr);
ierr = PetscOptionsHasName(NULL,"-help",&flg3);CHKERRQ(ierr);
if (flg1 || flg2 || flg3) {
/*
Print "higher-level" package version message
*/
if (PetscExternalVersionFunction) {
ierr = (*PetscExternalVersionFunction)(comm);CHKERRQ(ierr);
}
ierr = PetscGetVersion(version,256);CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm,"--------------------------------------------\
------------------------------\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm,"%s\n",version);CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm,"%s",PETSC_AUTHOR_INFO);CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm,"See docs/changes/index.html for recent updates.\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm,"See docs/faq.html for problems.\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm,"See docs/manualpages/index.html for help. \n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm,"Libraries linked from %s\n",PETSC_LIB_DIR);CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm,"--------------------------------------------\
------------------------------\n");CHKERRQ(ierr);
}
/*
Print "higher-level" package help message
*/
if (flg3) {
if (PetscExternalHelpFunction) {
ierr = (*PetscExternalHelpFunction)(comm);CHKERRQ(ierr);
}
}
/*
Setup the error handling
*/
flg1 = PETSC_FALSE;
ierr = PetscOptionsGetBool(NULL,"-on_error_abort",&flg1,NULL);CHKERRQ(ierr);
if (flg1) {
ierr = MPI_Comm_set_errhandler(PETSC_COMM_WORLD,MPI_ERRORS_ARE_FATAL);CHKERRQ(ierr);
ierr = PetscPushErrorHandler(PetscAbortErrorHandler,0);CHKERRQ(ierr);
}
flg1 = PETSC_FALSE;
ierr = PetscOptionsGetBool(NULL,"-on_error_mpiabort",&flg1,NULL);CHKERRQ(ierr);
if (flg1) { ierr = PetscPushErrorHandler(PetscMPIAbortErrorHandler,0);CHKERRQ(ierr);}
flg1 = PETSC_FALSE;
ierr = PetscOptionsGetBool(NULL,"-mpi_return_on_error",&flg1,NULL);CHKERRQ(ierr);
if (flg1) {
ierr = MPI_Comm_set_errhandler(comm,MPI_ERRORS_RETURN);CHKERRQ(ierr);
}
flg1 = PETSC_FALSE;
ierr = PetscOptionsGetBool(NULL,"-no_signal_handler",&flg1,NULL);CHKERRQ(ierr);
if (!flg1) {ierr = PetscPushSignalHandler(PetscSignalHandlerDefault,(void*)0);CHKERRQ(ierr);}
flg1 = PETSC_FALSE;
ierr = PetscOptionsGetBool(NULL,"-fp_trap",&flg1,NULL);CHKERRQ(ierr);
if (flg1) {ierr = PetscSetFPTrap(PETSC_FP_TRAP_ON);CHKERRQ(ierr);}
ierr = PetscOptionsGetInt(NULL,"-check_pointer_intensity",&intensity,&flag);CHKERRQ(ierr);
if (flag) {ierr = PetscCheckPointerSetIntensity(intensity);CHKERRQ(ierr);}
/*
Setup debugger information
*/
ierr = PetscSetDefaultDebugger();CHKERRQ(ierr);
ierr = PetscOptionsGetString(NULL,"-on_error_attach_debugger",string,64,&flg1);CHKERRQ(ierr);
if (flg1) {
MPI_Errhandler err_handler;
ierr = PetscSetDebuggerFromString(string);CHKERRQ(ierr);
ierr = MPI_Comm_create_errhandler((MPI_Handler_function*)Petsc_MPI_DebuggerOnError,&err_handler);CHKERRQ(ierr);
ierr = MPI_Comm_set_errhandler(comm,err_handler);CHKERRQ(ierr);
ierr = PetscPushErrorHandler(PetscAttachDebuggerErrorHandler,0);CHKERRQ(ierr);
}
ierr = PetscOptionsGetString(NULL,"-debug_terminal",string,64,&flg1);CHKERRQ(ierr);
if (flg1) { ierr = PetscSetDebugTerminal(string);CHKERRQ(ierr); }
ierr = PetscOptionsGetString(NULL,"-start_in_debugger",string,64,&flg1);CHKERRQ(ierr);
ierr = PetscOptionsGetString(NULL,"-stop_for_debugger",string,64,&flg2);CHKERRQ(ierr);
if (flg1 || flg2) {
PetscMPIInt size;
PetscInt lsize,*nodes;
MPI_Errhandler err_handler;
/*
we have to make sure that all processors have opened
connections to all other processors, otherwise once the
debugger has stated it is likely to receive a SIGUSR1
and kill the program.
*/
ierr = MPI_Comm_size(PETSC_COMM_WORLD,&size);CHKERRQ(ierr);
if (size > 2) {
PetscMPIInt dummy = 0;
MPI_Status status;
for (i=0; i<size; i++) {
if (rank != i) {
ierr = MPI_Send(&dummy,1,MPI_INT,i,109,PETSC_COMM_WORLD);CHKERRQ(ierr);
}
}
for (i=0; i<size; i++) {
if (rank != i) {
ierr = MPI_Recv(&dummy,1,MPI_INT,i,109,PETSC_COMM_WORLD,&status);CHKERRQ(ierr);
}
}
}
/* check if this processor node should be in debugger */
ierr = PetscMalloc1(size,&nodes);CHKERRQ(ierr);
lsize = size;
ierr = PetscOptionsGetIntArray(NULL,"-debugger_nodes",nodes,&lsize,&flag);CHKERRQ(ierr);
if (flag) {
for (i=0; i<lsize; i++) {
if (nodes[i] == rank) { flag = PETSC_FALSE; break; }
}
}
if (!flag) {
ierr = PetscSetDebuggerFromString(string);CHKERRQ(ierr);
ierr = PetscPushErrorHandler(PetscAbortErrorHandler,0);CHKERRQ(ierr);
if (flg1) {
ierr = PetscAttachDebugger();CHKERRQ(ierr);
} else {
ierr = PetscStopForDebugger();CHKERRQ(ierr);
}
ierr = MPI_Comm_create_errhandler((MPI_Handler_function*)Petsc_MPI_AbortOnError,&err_handler);CHKERRQ(ierr);
ierr = MPI_Comm_set_errhandler(comm,err_handler);CHKERRQ(ierr);
}
ierr = PetscFree(nodes);CHKERRQ(ierr);
}
ierr = PetscOptionsGetString(NULL,"-on_error_emacs",emacsmachinename,128,&flg1);CHKERRQ(ierr);
if (flg1 && !rank) {ierr = PetscPushErrorHandler(PetscEmacsClientErrorHandler,emacsmachinename);CHKERRQ(ierr);}
/*
Setup profiling and logging
*/
#if defined(PETSC_USE_INFO)
{
char logname[PETSC_MAX_PATH_LEN]; logname[0] = 0;
ierr = PetscOptionsGetString(NULL,"-info",logname,250,&flg1);CHKERRQ(ierr);
if (flg1 && logname[0]) {
ierr = PetscInfoAllow(PETSC_TRUE,logname);CHKERRQ(ierr);
} else if (flg1) {
ierr = PetscInfoAllow(PETSC_TRUE,NULL);CHKERRQ(ierr);
}
}
#endif
#if defined(PETSC_USE_LOG)
mname[0] = 0;
ierr = PetscOptionsGetString(NULL,"-history",mname,PETSC_MAX_PATH_LEN,&flg1);CHKERRQ(ierr);
if (flg1) {
if (mname[0]) {
ierr = PetscOpenHistoryFile(mname,&petsc_history);CHKERRQ(ierr);
} else {
ierr = PetscOpenHistoryFile(NULL,&petsc_history);CHKERRQ(ierr);
}
}
#if defined(PETSC_HAVE_MPE)
flg1 = PETSC_FALSE;
ierr = PetscOptionsHasName(NULL,"-log_mpe",&flg1);CHKERRQ(ierr);
if (flg1) {ierr = PetscLogMPEBegin();CHKERRQ(ierr);}
#endif
flg1 = PETSC_FALSE;
flg2 = PETSC_FALSE;
flg3 = PETSC_FALSE;
ierr = PetscOptionsGetBool(NULL,"-log_all",&flg1,NULL);CHKERRQ(ierr);
ierr = PetscOptionsGetBool(NULL,"-log",&flg2,NULL);CHKERRQ(ierr);
ierr = PetscOptionsHasName(NULL,"-log_summary",&flg3);CHKERRQ(ierr);
ierr = PetscOptionsHasName(NULL,"-log_view",&flg4);CHKERRQ(ierr);
if (flg1) { ierr = PetscLogAllBegin();CHKERRQ(ierr); }
else if (flg2 || flg3 || flg4) { ierr = PetscLogBegin();CHKERRQ(ierr);}
ierr = PetscOptionsGetString(NULL,"-log_trace",mname,250,&flg1);CHKERRQ(ierr);
if (flg1) {
char name[PETSC_MAX_PATH_LEN],fname[PETSC_MAX_PATH_LEN];
FILE *file;
if (mname[0]) {
sprintf(name,"%s.%d",mname,rank);
ierr = PetscFixFilename(name,fname);CHKERRQ(ierr);
file = fopen(fname,"w");
if (!file) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_FILE_OPEN,"Unable to open trace file: %s",fname);
} else file = PETSC_STDOUT;
ierr = PetscLogTraceBegin(file);CHKERRQ(ierr);
}
#endif
ierr = PetscOptionsGetBool(NULL,"-saws_options",&PetscOptionsPublish,NULL);CHKERRQ(ierr);
#if defined(PETSC_HAVE_CUDA)
ierr = PetscOptionsHasName(NULL,"-cuda_show_devices",&flg1);CHKERRQ(ierr);
if (flg1) {
struct cudaDeviceProp prop;
int devCount;
int device;
cudaError_t err = cudaSuccess;
err = cudaGetDeviceCount(&devCount);
if (err != cudaSuccess) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SYS,"error in cudaGetDeviceCount %s",cudaGetErrorString(err));
for (device = 0; device < devCount; ++device) {
err = cudaGetDeviceProperties(&prop, device);
if (err != cudaSuccess) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SYS,"error in cudaGetDeviceProperties %s",cudaGetErrorString(err));
ierr = PetscPrintf(PETSC_COMM_WORLD, "CUDA device %d: %s\n", device, prop.name);CHKERRQ(ierr);
}
}
{
int size;
ierr = MPI_Comm_size(PETSC_COMM_WORLD,&size);CHKERRQ(ierr);
if (size>1) {
int devCount, device, rank;
cudaError_t err = cudaSuccess;
/* check to see if we force multiple ranks to hit the same GPU */
ierr = PetscOptionsGetInt(NULL,"-cuda_set_device", &device, &flg1);CHKERRQ(ierr);
if (flg1) {
err = cudaSetDevice(device);
if (err != cudaSuccess) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SYS,"error in cudaSetDevice %s",cudaGetErrorString(err));
} else {
/* we're not using the same GPU on multiple MPI threads. So try to allocated different GPUs to different processes */
/* First get the device count */
err = cudaGetDeviceCount(&devCount);
if (err != cudaSuccess) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SYS,"error in cudaGetDeviceCount %s",cudaGetErrorString(err));
/* next determine the rank and then set the device via a mod */
ierr = MPI_Comm_rank(PETSC_COMM_WORLD,&rank);CHKERRQ(ierr);
device = rank % devCount;
err = cudaSetDevice(device);
if (err != cudaSuccess) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SYS,"error in cudaSetDevice %s",cudaGetErrorString(err));
}
/* set the device flags so that it can map host memory ... do NOT throw exception on err!=cudaSuccess
multiple devices may try to set the flags on the same device. So long as one of them succeeds, things
are ok. */
err = cudaSetDeviceFlags(cudaDeviceMapHost);
if (err != cudaSuccess) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SYS,"error in cudaSetDeviceFlags %s",cudaGetErrorString(err));
} else {
int device;
cudaError_t err = cudaSuccess;
/* the code below works for serial GPU simulations */
ierr = PetscOptionsGetInt(NULL,"-cuda_set_device", &device, &flg1);CHKERRQ(ierr);
if (flg1) {
err = cudaSetDevice(device);
if (err != cudaSuccess) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SYS,"error in cudaSetDevice %s",cudaGetErrorString(err));
}
/* set the device flags so that it can map host memory ... here, we error check. */
err = cudaSetDeviceFlags(cudaDeviceMapHost);
if (err != cudaSuccess) SETERRQ1(PETSC_COMM_SELF,PETSC_ERR_SYS,"error in cudaSetDeviceFlags %s",cudaGetErrorString(err));
}
}
#endif
/*
Print basic help message
*/
ierr = PetscOptionsHasName(NULL,"-help",&flg1);CHKERRQ(ierr);
if (flg1) {
ierr = (*PetscHelpPrintf)(comm,"Options for all PETSc programs:\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -help: prints help method for each option\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -on_error_abort: cause an abort when an error is detected. Useful \n ");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," only when run in the debugger\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -on_error_attach_debugger [gdb,dbx,xxgdb,ups,noxterm]\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," start the debugger in new xterm\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," unless noxterm is given\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -start_in_debugger [gdb,dbx,xxgdb,ups,noxterm]\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," start all processes in the debugger\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -on_error_emacs <machinename>\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," emacs jumps to error file\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -debugger_nodes [n1,n2,..] Nodes to start in debugger\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -debugger_pause [m] : delay (in seconds) to attach debugger\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -stop_for_debugger : prints message on how to attach debugger manually\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," waits the delay for you to attach\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -display display: Location where X window graphics and debuggers are displayed\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -no_signal_handler: do not trap error signals\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -mpi_return_on_error: MPI returns error code, rather than abort on internal error\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -fp_trap: stop on floating point exceptions\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," note on IBM RS6000 this slows run greatly\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -malloc_dump <optional filename>: dump list of unfreed memory at conclusion\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -malloc: use our error checking malloc\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -malloc no: don't use error checking malloc\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -malloc_info: prints total memory usage\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -malloc_log: keeps log of all memory allocations\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -malloc_debug: enables extended checking for memory corruption\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -options_table: dump list of options inputted\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -options_left: dump list of unused options\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -options_left no: don't dump list of unused options\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -tmp tmpdir: alternative /tmp directory\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -shared_tmp: tmp directory is shared by all processors\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -not_shared_tmp: each processor has separate tmp directory\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -memory_view: print memory usage at end of run\n");CHKERRQ(ierr);
#if defined(PETSC_USE_LOG)
ierr = (*PetscHelpPrintf)(comm," -get_total_flops: total flops over all processors\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -log[_summary _summary_python]: logging objects and events\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -log_trace [filename]: prints trace of all PETSc calls\n");CHKERRQ(ierr);
#if defined(PETSC_HAVE_MPE)
ierr = (*PetscHelpPrintf)(comm," -log_mpe: Also create logfile viewable through Jumpshot\n");CHKERRQ(ierr);
#endif
ierr = (*PetscHelpPrintf)(comm," -info <optional filename>: print informative messages about the calculations\n");CHKERRQ(ierr);
#endif
ierr = (*PetscHelpPrintf)(comm," -v: prints PETSc version number and release date\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -options_file <file>: reads options from file\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm," -petsc_sleep n: sleeps n seconds before running program\n");CHKERRQ(ierr);
ierr = (*PetscHelpPrintf)(comm,"-----------------------------------------------\n");CHKERRQ(ierr);
}
#if defined(PETSC_HAVE_POPEN)
{
char machine[128];
ierr = PetscOptionsGetString(NULL,"-popen_machine",machine,128,&flg1);CHKERRQ(ierr);
if (flg1) {
ierr = PetscPOpenSetMachine(machine);CHKERRQ(ierr);
}
}
#endif
ierr = PetscOptionsGetReal(NULL,"-petsc_sleep",&si,&flg1);CHKERRQ(ierr);
if (flg1) {
ierr = PetscSleep(si);CHKERRQ(ierr);
}
ierr = PetscOptionsGetString(NULL,"-info_exclude",mname,PETSC_MAX_PATH_LEN,&flg1);CHKERRQ(ierr);
if (flg1) {
ierr = PetscStrstr(mname,"null",&f);CHKERRQ(ierr);
if (f) {
ierr = PetscInfoDeactivateClass(0);CHKERRQ(ierr);
}
}
#if defined(PETSC_HAVE_CUSP) || defined(PETSC_HAVE_VIENNACL)
ierr = PetscOptionsHasName(NULL,"-log_summary",&flg3);CHKERRQ(ierr);
if (!flg3) {
ierr = PetscOptionsHasName(NULL,"-log_view",&flg3);CHKERRQ(ierr);
}
#endif
#if defined(PETSC_HAVE_CUSP)
ierr = PetscOptionsGetBool(NULL,"-cusp_synchronize",&flg3,NULL);CHKERRQ(ierr);
PetscCUSPSynchronize = flg3;
#elif defined(PETSC_HAVE_VIENNACL)
ierr = PetscOptionsGetBool(NULL,"-viennacl_synchronize",&flg3,NULL);CHKERRQ(ierr);
PetscViennaCLSynchronize = flg3;
#endif
PetscFunctionReturn(0);
}
int
main( int argc,char** argv)
{
printf("hello world\n");
if (!InitCUDA())
{
return 0;
}
int iter = 1000;
int trainnum = 20;
bool isProfiler = false;
int intProfiler = 0;
int testnum = -1;
float maxtime = 0.0f;
cutGetCmdLineArgumenti(argc, (const char**) argv, "train", &trainnum);
cutGetCmdLineArgumenti(argc, (const char**) argv, "iter", &iter);
cutGetCmdLineArgumenti(argc, (const char**) argv, "profiler", &intProfiler);
cutGetCmdLineArgumenti(argc, (const char**) argv, "test", &testnum);
cutGetCmdLineArgumentf(argc, (const char**) argv, "maxtime", &maxtime);
printf("%d\n", intProfiler);
if(intProfiler)
{
isProfiler = true;
}
if(testnum == -1) testnum = trainnum /2;
printf("Iter = %d\n", iter);
printf("TrainNum = %d\n", trainnum);
printf("TestNum = %d\n", testnum);
CUT_DEVICE_INIT(argc, argv);
cublasStatus status;
status = cublasInit();
if(status != CUBLAS_STATUS_SUCCESS)
{
printf("Can't init cublas\n");
printf("%s\n", cudaGetErrorString(cudaGetLastError()));
return -1;
}
Image* imageList = new Image[trainnum+testnum];
read64("my_optdigits.tra", imageList, trainnum + testnum);
const int warmUpTime = 3;
if(!isProfiler)
{
freopen("verbose.txt", "w", stdout);
for(int i=0;i< warmUpTime;i++)
{
runImage(argc, argv, imageList, trainnum < warmUpTime ? trainnum : warmUpTime, 0, 10, false, 0.0f);
}
freopen("CON", "w", stdout);
printf("Warm-up complete.\n\n\n");
}
#ifdef _DEBUG
freopen("out.txt", "w", stdout);
#endif // _DEBUG
runImage(argc, argv, imageList, trainnum, testnum, iter, true, maxtime);
freopen("CON", "w", stdout);
delete[] imageList;
//TestReduce();
cublasShutdown();
if(!isProfiler)
{
CUT_EXIT(argc, argv);
}
//getchar();
return 0;
}