mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2026-10-25 22:28:17 +08:00
Apply consistent code formatting across the repo. Add clang-format and pre-commit hooks.
This commit is contained in:
Rob Armstrong
@@ -31,10 +31,10 @@
|
||||
*/
|
||||
|
||||
// system includes
|
||||
#include <algorithm>
|
||||
#include <cstdio>
|
||||
#include <ctime>
|
||||
#include <vector>
|
||||
#include <algorithm>
|
||||
#ifdef USE_PTHREADS
|
||||
#include <pthread.h>
|
||||
#else
|
||||
@@ -51,291 +51,287 @@
|
||||
#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
|
||||
// SRAND48 and DRAND48 don't exist on windows, but these are the equivalent
|
||||
// functions
|
||||
void srand48(long seed) { srand((unsigned int)seed); }
|
||||
void srand48(long seed) { srand((unsigned int)seed); }
|
||||
double drand48() { return double(rand()) / RAND_MAX; }
|
||||
#endif
|
||||
|
||||
const char *sSDKname = "UnifiedMemoryStreams";
|
||||
|
||||
// simple task
|
||||
template <typename T>
|
||||
struct Task {
|
||||
unsigned int size, id;
|
||||
T *data;
|
||||
T *result;
|
||||
T *vector;
|
||||
template <typename T> struct Task
|
||||
{
|
||||
unsigned int size, id;
|
||||
T *data;
|
||||
T *result;
|
||||
T *vector;
|
||||
|
||||
Task() : size(0), id(0), data(NULL), result(NULL), vector(NULL){};
|
||||
Task(unsigned int s) : size(s), id(0), data(NULL), result(NULL) {
|
||||
// allocate unified memory -- the operation performed in this example will
|
||||
// be a DGEMV
|
||||
checkCudaErrors(cudaMallocManaged(&data, sizeof(T) * size * size));
|
||||
checkCudaErrors(cudaMallocManaged(&result, sizeof(T) * size));
|
||||
checkCudaErrors(cudaMallocManaged(&vector, sizeof(T) * size));
|
||||
checkCudaErrors(cudaDeviceSynchronize());
|
||||
}
|
||||
|
||||
~Task() {
|
||||
// ensure all memory is deallocated
|
||||
checkCudaErrors(cudaDeviceSynchronize());
|
||||
checkCudaErrors(cudaFree(data));
|
||||
checkCudaErrors(cudaFree(result));
|
||||
checkCudaErrors(cudaFree(vector));
|
||||
}
|
||||
|
||||
void allocate(const unsigned int s, const unsigned int unique_id) {
|
||||
// allocate unified memory outside of constructor
|
||||
id = unique_id;
|
||||
size = s;
|
||||
checkCudaErrors(cudaMallocManaged(&data, sizeof(T) * size * size));
|
||||
checkCudaErrors(cudaMallocManaged(&result, sizeof(T) * size));
|
||||
checkCudaErrors(cudaMallocManaged(&vector, sizeof(T) * size));
|
||||
checkCudaErrors(cudaDeviceSynchronize());
|
||||
|
||||
// populate data with random elements
|
||||
for (unsigned int i = 0; i < size * size; i++) {
|
||||
data[i] = drand48();
|
||||
Task()
|
||||
: size(0)
|
||||
, id(0)
|
||||
, data(NULL)
|
||||
, result(NULL)
|
||||
, vector(NULL) {};
|
||||
Task(unsigned int s)
|
||||
: size(s)
|
||||
, id(0)
|
||||
, data(NULL)
|
||||
, result(NULL)
|
||||
{
|
||||
// allocate unified memory -- the operation performed in this example will
|
||||
// be a DGEMV
|
||||
checkCudaErrors(cudaMallocManaged(&data, sizeof(T) * size * size));
|
||||
checkCudaErrors(cudaMallocManaged(&result, sizeof(T) * size));
|
||||
checkCudaErrors(cudaMallocManaged(&vector, sizeof(T) * size));
|
||||
checkCudaErrors(cudaDeviceSynchronize());
|
||||
}
|
||||
|
||||
for (unsigned int i = 0; i < size; i++) {
|
||||
result[i] = 0.;
|
||||
vector[i] = drand48();
|
||||
~Task()
|
||||
{
|
||||
// ensure all memory is deallocated
|
||||
checkCudaErrors(cudaDeviceSynchronize());
|
||||
checkCudaErrors(cudaFree(data));
|
||||
checkCudaErrors(cudaFree(result));
|
||||
checkCudaErrors(cudaFree(vector));
|
||||
}
|
||||
|
||||
void allocate(const unsigned int s, const unsigned int unique_id)
|
||||
{
|
||||
// allocate unified memory outside of constructor
|
||||
id = unique_id;
|
||||
size = s;
|
||||
checkCudaErrors(cudaMallocManaged(&data, sizeof(T) * size * size));
|
||||
checkCudaErrors(cudaMallocManaged(&result, sizeof(T) * size));
|
||||
checkCudaErrors(cudaMallocManaged(&vector, sizeof(T) * size));
|
||||
checkCudaErrors(cudaDeviceSynchronize());
|
||||
|
||||
// populate data with random elements
|
||||
for (unsigned int i = 0; i < size * size; i++) {
|
||||
data[i] = drand48();
|
||||
}
|
||||
|
||||
for (unsigned int i = 0; i < size; i++) {
|
||||
result[i] = 0.;
|
||||
vector[i] = drand48();
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
#ifdef USE_PTHREADS
|
||||
struct threadData_t {
|
||||
int tid;
|
||||
Task<double> *TaskListPtr;
|
||||
cudaStream_t *streams;
|
||||
cublasHandle_t *handles;
|
||||
int taskSize;
|
||||
struct threadData_t
|
||||
{
|
||||
int tid;
|
||||
Task<double> *TaskListPtr;
|
||||
cudaStream_t *streams;
|
||||
cublasHandle_t *handles;
|
||||
int taskSize;
|
||||
};
|
||||
|
||||
typedef struct threadData_t threadData;
|
||||
#endif
|
||||
|
||||
// simple host dgemv: assume data is in row-major format and square
|
||||
template <typename T>
|
||||
void gemv(int m, int n, T alpha, T *A, T *x, T beta, T *result) {
|
||||
// rows
|
||||
for (int i = 0; i < n; i++) {
|
||||
result[i] *= beta;
|
||||
template <typename T> void gemv(int m, int n, T alpha, T *A, T *x, T beta, T *result)
|
||||
{
|
||||
// rows
|
||||
for (int i = 0; i < n; i++) {
|
||||
result[i] *= beta;
|
||||
|
||||
for (int j = 0; j < n; j++) {
|
||||
result[i] += A[i * n + j] * x[j];
|
||||
for (int j = 0; j < n; j++) {
|
||||
result[i] += A[i * n + j] * x[j];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// execute a single task on either host or device depending on size
|
||||
#ifdef USE_PTHREADS
|
||||
void *execute(void *inpArgs) {
|
||||
threadData *dataPtr = (threadData *)inpArgs;
|
||||
cudaStream_t *stream = dataPtr->streams;
|
||||
cublasHandle_t *handle = dataPtr->handles;
|
||||
int tid = dataPtr->tid;
|
||||
void *execute(void *inpArgs)
|
||||
{
|
||||
threadData *dataPtr = (threadData *)inpArgs;
|
||||
cudaStream_t *stream = dataPtr->streams;
|
||||
cublasHandle_t *handle = dataPtr->handles;
|
||||
int tid = dataPtr->tid;
|
||||
|
||||
for (int i = 0; i < dataPtr->taskSize; i++) {
|
||||
Task<double> &t = dataPtr->TaskListPtr[i];
|
||||
for (int i = 0; i < dataPtr->taskSize; i++) {
|
||||
Task<double> &t = dataPtr->TaskListPtr[i];
|
||||
|
||||
if (t.size < 100) {
|
||||
// perform on host
|
||||
printf("Task [%d], thread [%d] executing on host (%d)\n", t.id, tid,
|
||||
t.size);
|
||||
if (t.size < 100) {
|
||||
// perform on host
|
||||
printf("Task [%d], thread [%d] executing on host (%d)\n", t.id, tid, t.size);
|
||||
|
||||
// attach managed memory to a (dummy) stream to allow host access while
|
||||
// the device is running
|
||||
checkCudaErrors(
|
||||
cudaStreamAttachMemAsync(stream[0], t.data, 0, cudaMemAttachHost));
|
||||
checkCudaErrors(
|
||||
cudaStreamAttachMemAsync(stream[0], t.vector, 0, cudaMemAttachHost));
|
||||
checkCudaErrors(
|
||||
cudaStreamAttachMemAsync(stream[0], t.result, 0, cudaMemAttachHost));
|
||||
// necessary to ensure Async cudaStreamAttachMemAsync calls have finished
|
||||
checkCudaErrors(cudaStreamSynchronize(stream[0]));
|
||||
// call the host operation
|
||||
gemv(t.size, t.size, 1.0, t.data, t.vector, 0.0, t.result);
|
||||
} else {
|
||||
// perform on device
|
||||
printf("Task [%d], thread [%d] executing on device (%d)\n", t.id, tid,
|
||||
t.size);
|
||||
double one = 1.0;
|
||||
double zero = 0.0;
|
||||
// attach managed memory to a (dummy) stream to allow host access while
|
||||
// the device is running
|
||||
checkCudaErrors(cudaStreamAttachMemAsync(stream[0], t.data, 0, cudaMemAttachHost));
|
||||
checkCudaErrors(cudaStreamAttachMemAsync(stream[0], t.vector, 0, cudaMemAttachHost));
|
||||
checkCudaErrors(cudaStreamAttachMemAsync(stream[0], t.result, 0, cudaMemAttachHost));
|
||||
// necessary to ensure Async cudaStreamAttachMemAsync calls have finished
|
||||
checkCudaErrors(cudaStreamSynchronize(stream[0]));
|
||||
// call the host operation
|
||||
gemv(t.size, t.size, 1.0, t.data, t.vector, 0.0, t.result);
|
||||
}
|
||||
else {
|
||||
// perform on device
|
||||
printf("Task [%d], thread [%d] executing on device (%d)\n", t.id, tid, t.size);
|
||||
double one = 1.0;
|
||||
double zero = 0.0;
|
||||
|
||||
// attach managed memory to my stream
|
||||
checkCudaErrors(cublasSetStream(handle[tid + 1], stream[tid + 1]));
|
||||
checkCudaErrors(cudaStreamAttachMemAsync(stream[tid + 1], t.data, 0,
|
||||
cudaMemAttachSingle));
|
||||
checkCudaErrors(cudaStreamAttachMemAsync(stream[tid + 1], t.vector, 0,
|
||||
cudaMemAttachSingle));
|
||||
checkCudaErrors(cudaStreamAttachMemAsync(stream[tid + 1], t.result, 0,
|
||||
cudaMemAttachSingle));
|
||||
// call the device operation
|
||||
checkCudaErrors(cublasDgemv(handle[tid + 1], CUBLAS_OP_N, t.size, t.size,
|
||||
&one, t.data, t.size, t.vector, 1, &zero,
|
||||
t.result, 1));
|
||||
// attach managed memory to my stream
|
||||
checkCudaErrors(cublasSetStream(handle[tid + 1], stream[tid + 1]));
|
||||
checkCudaErrors(cudaStreamAttachMemAsync(stream[tid + 1], t.data, 0, cudaMemAttachSingle));
|
||||
checkCudaErrors(cudaStreamAttachMemAsync(stream[tid + 1], t.vector, 0, cudaMemAttachSingle));
|
||||
checkCudaErrors(cudaStreamAttachMemAsync(stream[tid + 1], t.result, 0, cudaMemAttachSingle));
|
||||
// call the device operation
|
||||
checkCudaErrors(cublasDgemv(
|
||||
handle[tid + 1], CUBLAS_OP_N, t.size, t.size, &one, t.data, t.size, t.vector, 1, &zero, t.result, 1));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pthread_exit(NULL);
|
||||
pthread_exit(NULL);
|
||||
}
|
||||
#else
|
||||
template <typename T>
|
||||
void execute(Task<T> &t, cublasHandle_t *handle, cudaStream_t *stream,
|
||||
int tid) {
|
||||
if (t.size < 100) {
|
||||
// perform on host
|
||||
printf("Task [%d], thread [%d] executing on host (%d)\n", t.id, tid,
|
||||
t.size);
|
||||
template <typename T> void execute(Task<T> &t, cublasHandle_t *handle, cudaStream_t *stream, int tid)
|
||||
{
|
||||
if (t.size < 100) {
|
||||
// perform on host
|
||||
printf("Task [%d], thread [%d] executing on host (%d)\n", t.id, tid, t.size);
|
||||
|
||||
// attach managed memory to a (dummy) stream to allow host access while the
|
||||
// device is running
|
||||
checkCudaErrors(
|
||||
cudaStreamAttachMemAsync(stream[0], t.data, 0, cudaMemAttachHost));
|
||||
checkCudaErrors(
|
||||
cudaStreamAttachMemAsync(stream[0], t.vector, 0, cudaMemAttachHost));
|
||||
checkCudaErrors(
|
||||
cudaStreamAttachMemAsync(stream[0], t.result, 0, cudaMemAttachHost));
|
||||
// necessary to ensure Async cudaStreamAttachMemAsync calls have finished
|
||||
checkCudaErrors(cudaStreamSynchronize(stream[0]));
|
||||
// call the host operation
|
||||
gemv(t.size, t.size, 1.0, t.data, t.vector, 0.0, t.result);
|
||||
} else {
|
||||
// perform on device
|
||||
printf("Task [%d], thread [%d] executing on device (%d)\n", t.id, tid,
|
||||
t.size);
|
||||
double one = 1.0;
|
||||
double zero = 0.0;
|
||||
// attach managed memory to a (dummy) stream to allow host access while the
|
||||
// device is running
|
||||
checkCudaErrors(cudaStreamAttachMemAsync(stream[0], t.data, 0, cudaMemAttachHost));
|
||||
checkCudaErrors(cudaStreamAttachMemAsync(stream[0], t.vector, 0, cudaMemAttachHost));
|
||||
checkCudaErrors(cudaStreamAttachMemAsync(stream[0], t.result, 0, cudaMemAttachHost));
|
||||
// necessary to ensure Async cudaStreamAttachMemAsync calls have finished
|
||||
checkCudaErrors(cudaStreamSynchronize(stream[0]));
|
||||
// call the host operation
|
||||
gemv(t.size, t.size, 1.0, t.data, t.vector, 0.0, t.result);
|
||||
}
|
||||
else {
|
||||
// perform on device
|
||||
printf("Task [%d], thread [%d] executing on device (%d)\n", t.id, tid, t.size);
|
||||
double one = 1.0;
|
||||
double zero = 0.0;
|
||||
|
||||
// attach managed memory to my stream
|
||||
checkCudaErrors(cublasSetStream(handle[tid + 1], stream[tid + 1]));
|
||||
checkCudaErrors(cudaStreamAttachMemAsync(stream[tid + 1], t.data, 0,
|
||||
cudaMemAttachSingle));
|
||||
checkCudaErrors(cudaStreamAttachMemAsync(stream[tid + 1], t.vector, 0,
|
||||
cudaMemAttachSingle));
|
||||
checkCudaErrors(cudaStreamAttachMemAsync(stream[tid + 1], t.result, 0,
|
||||
cudaMemAttachSingle));
|
||||
// call the device operation
|
||||
checkCudaErrors(cublasDgemv(handle[tid + 1], CUBLAS_OP_N, t.size, t.size,
|
||||
&one, t.data, t.size, t.vector, 1, &zero,
|
||||
t.result, 1));
|
||||
}
|
||||
// attach managed memory to my stream
|
||||
checkCudaErrors(cublasSetStream(handle[tid + 1], stream[tid + 1]));
|
||||
checkCudaErrors(cudaStreamAttachMemAsync(stream[tid + 1], t.data, 0, cudaMemAttachSingle));
|
||||
checkCudaErrors(cudaStreamAttachMemAsync(stream[tid + 1], t.vector, 0, cudaMemAttachSingle));
|
||||
checkCudaErrors(cudaStreamAttachMemAsync(stream[tid + 1], t.result, 0, cudaMemAttachSingle));
|
||||
// call the device operation
|
||||
checkCudaErrors(cublasDgemv(
|
||||
handle[tid + 1], CUBLAS_OP_N, t.size, t.size, &one, t.data, t.size, t.vector, 1, &zero, t.result, 1));
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
// populate a list of tasks with random sizes
|
||||
template <typename T>
|
||||
void initialise_tasks(std::vector<Task<T> > &TaskList) {
|
||||
for (unsigned int i = 0; i < TaskList.size(); i++) {
|
||||
// generate random size
|
||||
int size;
|
||||
size = std::max((int)(drand48() * 1000.0), 64);
|
||||
TaskList[i].allocate(size, i);
|
||||
}
|
||||
template <typename T> void initialise_tasks(std::vector<Task<T>> &TaskList)
|
||||
{
|
||||
for (unsigned int i = 0; i < TaskList.size(); i++) {
|
||||
// generate random size
|
||||
int size;
|
||||
size = std::max((int)(drand48() * 1000.0), 64);
|
||||
TaskList[i].allocate(size, i);
|
||||
}
|
||||
}
|
||||
|
||||
int main(int argc, char **argv) {
|
||||
// set device
|
||||
cudaDeviceProp device_prop;
|
||||
int dev_id = findCudaDevice(argc, (const char **)argv);
|
||||
checkCudaErrors(cudaGetDeviceProperties(&device_prop, dev_id));
|
||||
int main(int argc, char **argv)
|
||||
{
|
||||
// set device
|
||||
cudaDeviceProp device_prop;
|
||||
int dev_id = findCudaDevice(argc, (const char **)argv);
|
||||
checkCudaErrors(cudaGetDeviceProperties(&device_prop, dev_id));
|
||||
|
||||
if (!device_prop.managedMemory) {
|
||||
// This samples requires being run on a device that supports Unified Memory
|
||||
fprintf(stderr, "Unified Memory not supported on this device\n");
|
||||
if (!device_prop.managedMemory) {
|
||||
// This samples requires being run on a device that supports Unified Memory
|
||||
fprintf(stderr, "Unified Memory not supported on this device\n");
|
||||
|
||||
exit(EXIT_WAIVED);
|
||||
}
|
||||
exit(EXIT_WAIVED);
|
||||
}
|
||||
|
||||
if (device_prop.computeMode == cudaComputeModeProhibited) {
|
||||
// This sample requires being run with a default or process exclusive mode
|
||||
fprintf(stderr,
|
||||
"This sample requires a device in either default or process "
|
||||
"exclusive mode\n");
|
||||
if (device_prop.computeMode == cudaComputeModeProhibited) {
|
||||
// This sample requires being run with a default or process exclusive mode
|
||||
fprintf(stderr,
|
||||
"This sample requires a device in either default or process "
|
||||
"exclusive mode\n");
|
||||
|
||||
exit(EXIT_WAIVED);
|
||||
}
|
||||
exit(EXIT_WAIVED);
|
||||
}
|
||||
|
||||
// randomise task sizes
|
||||
int seed = (int)time(NULL);
|
||||
srand48(seed);
|
||||
// randomise task sizes
|
||||
int seed = (int)time(NULL);
|
||||
srand48(seed);
|
||||
|
||||
// set number of threads
|
||||
const int nthreads = 4;
|
||||
// set number of threads
|
||||
const int nthreads = 4;
|
||||
|
||||
// number of streams = number of threads
|
||||
cudaStream_t *streams = new cudaStream_t[nthreads + 1];
|
||||
cublasHandle_t *handles = new cublasHandle_t[nthreads + 1];
|
||||
// number of streams = number of threads
|
||||
cudaStream_t *streams = new cudaStream_t[nthreads + 1];
|
||||
cublasHandle_t *handles = new cublasHandle_t[nthreads + 1];
|
||||
|
||||
for (int i = 0; i < nthreads + 1; i++) {
|
||||
checkCudaErrors(cudaStreamCreate(&streams[i]));
|
||||
checkCudaErrors(cublasCreate(&handles[i]));
|
||||
}
|
||||
for (int i = 0; i < nthreads + 1; i++) {
|
||||
checkCudaErrors(cudaStreamCreate(&streams[i]));
|
||||
checkCudaErrors(cublasCreate(&handles[i]));
|
||||
}
|
||||
|
||||
// create list of N tasks
|
||||
unsigned int N = 40;
|
||||
std::vector<Task<double> > TaskList(N);
|
||||
initialise_tasks(TaskList);
|
||||
// create list of N tasks
|
||||
unsigned int N = 40;
|
||||
std::vector<Task<double>> TaskList(N);
|
||||
initialise_tasks(TaskList);
|
||||
|
||||
printf("Executing tasks on host / device\n");
|
||||
printf("Executing tasks on host / device\n");
|
||||
|
||||
// run through all tasks using threads and streams
|
||||
#ifdef USE_PTHREADS
|
||||
pthread_t threads[nthreads];
|
||||
threadData *InputToThreads = new threadData[nthreads];
|
||||
pthread_t threads[nthreads];
|
||||
threadData *InputToThreads = new threadData[nthreads];
|
||||
|
||||
for (int i = 0; i < nthreads; i++) {
|
||||
checkCudaErrors(cudaSetDevice(dev_id));
|
||||
InputToThreads[i].tid = i;
|
||||
InputToThreads[i].streams = streams;
|
||||
InputToThreads[i].handles = handles;
|
||||
for (int i = 0; i < nthreads; i++) {
|
||||
checkCudaErrors(cudaSetDevice(dev_id));
|
||||
InputToThreads[i].tid = i;
|
||||
InputToThreads[i].streams = streams;
|
||||
InputToThreads[i].handles = handles;
|
||||
|
||||
if ((TaskList.size() / nthreads) == 0) {
|
||||
InputToThreads[i].taskSize = (TaskList.size() / nthreads);
|
||||
InputToThreads[i].TaskListPtr =
|
||||
&TaskList[i * (TaskList.size() / nthreads)];
|
||||
} else {
|
||||
if (i == nthreads - 1) {
|
||||
InputToThreads[i].taskSize =
|
||||
(TaskList.size() / nthreads) + (TaskList.size() % nthreads);
|
||||
InputToThreads[i].TaskListPtr =
|
||||
&TaskList[i * (TaskList.size() / nthreads) +
|
||||
(TaskList.size() % nthreads)];
|
||||
} else {
|
||||
InputToThreads[i].taskSize = (TaskList.size() / nthreads);
|
||||
InputToThreads[i].TaskListPtr =
|
||||
&TaskList[i * (TaskList.size() / nthreads)];
|
||||
}
|
||||
if ((TaskList.size() / nthreads) == 0) {
|
||||
InputToThreads[i].taskSize = (TaskList.size() / nthreads);
|
||||
InputToThreads[i].TaskListPtr = &TaskList[i * (TaskList.size() / nthreads)];
|
||||
}
|
||||
else {
|
||||
if (i == nthreads - 1) {
|
||||
InputToThreads[i].taskSize = (TaskList.size() / nthreads) + (TaskList.size() % nthreads);
|
||||
InputToThreads[i].TaskListPtr =
|
||||
&TaskList[i * (TaskList.size() / nthreads) + (TaskList.size() % nthreads)];
|
||||
}
|
||||
else {
|
||||
InputToThreads[i].taskSize = (TaskList.size() / nthreads);
|
||||
InputToThreads[i].TaskListPtr = &TaskList[i * (TaskList.size() / nthreads)];
|
||||
}
|
||||
}
|
||||
|
||||
pthread_create(&threads[i], NULL, &execute, &InputToThreads[i]);
|
||||
}
|
||||
for (int i = 0; i < nthreads; i++) {
|
||||
pthread_join(threads[i], NULL);
|
||||
}
|
||||
|
||||
pthread_create(&threads[i], NULL, &execute, &InputToThreads[i]);
|
||||
}
|
||||
for (int i = 0; i < nthreads; i++) {
|
||||
pthread_join(threads[i], NULL);
|
||||
}
|
||||
#else
|
||||
omp_set_num_threads(nthreads);
|
||||
omp_set_num_threads(nthreads);
|
||||
#pragma omp parallel for schedule(dynamic)
|
||||
for (int i = 0; i < TaskList.size(); i++) {
|
||||
checkCudaErrors(cudaSetDevice(dev_id));
|
||||
int tid = omp_get_thread_num();
|
||||
execute(TaskList[i], handles, streams, tid);
|
||||
}
|
||||
for (int i = 0; i < TaskList.size(); i++) {
|
||||
checkCudaErrors(cudaSetDevice(dev_id));
|
||||
int tid = omp_get_thread_num();
|
||||
execute(TaskList[i], handles, streams, tid);
|
||||
}
|
||||
#endif
|
||||
|
||||
cudaDeviceSynchronize();
|
||||
cudaDeviceSynchronize();
|
||||
|
||||
// Destroy CUDA Streams, cuBlas handles
|
||||
for (int i = 0; i < nthreads + 1; i++) {
|
||||
cudaStreamDestroy(streams[i]);
|
||||
cublasDestroy(handles[i]);
|
||||
}
|
||||
// Destroy CUDA Streams, cuBlas handles
|
||||
for (int i = 0; i < nthreads + 1; i++) {
|
||||
cudaStreamDestroy(streams[i]);
|
||||
cublasDestroy(handles[i]);
|
||||
}
|
||||
|
||||
// Free TaskList
|
||||
std::vector<Task<double> >().swap(TaskList);
|
||||
// Free TaskList
|
||||
std::vector<Task<double>>().swap(TaskList);
|
||||
|
||||
printf("All Done!\n");
|
||||
exit(EXIT_SUCCESS);
|
||||
printf("All Done!\n");
|
||||
exit(EXIT_SUCCESS);
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user