'''
#define SIZE 2048ul // Matrices are SIZE*SIZE.. 2048^2 should be efficiently implemented in CUBLAS
#define USEMEM 0.9 // Try to allocate 90% of memory
// Used to report op/s, measured through Visual Profiler, CUBLAS from CUDA 7.5
// (Seems that they indeed take the naive dim^3 approach)
#define OPS_PER_MUL 17188257792ul
#include <cstdio>
#include <string>
#include <map>
#include <vector>
#include <sys/types.h>
#include <signal.h>
#include <sys/wait.h>
#include <sys/time.h>
#include <string.h>
#include <unistd.h>
#include <time.h>
#include <fstream>
#include <cuda.h>
#include "cublas_v2.h"
void checkError(int rCode, std::string desc = "") {
static std::map<int, std::string> g_errorStrings;
if (!g_errorStrings.size()) {
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_INVALID_VALUE, "CUDA_ERROR_INVALID_VALUE"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_OUT_OF_MEMORY, "CUDA_ERROR_OUT_OF_MEMORY"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_NOT_INITIALIZED, "CUDA_ERROR_NOT_INITIALIZED"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_DEINITIALIZED, "CUDA_ERROR_DEINITIALIZED"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_NO_DEVICE, "CUDA_ERROR_NO_DEVICE"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_INVALID_DEVICE, "CUDA_ERROR_INVALID_DEVICE"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_INVALID_IMAGE, "CUDA_ERROR_INVALID_IMAGE"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_INVALID_CONTEXT, "CUDA_ERROR_INVALID_CONTEXT"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_MAP_FAILED, "CUDA_ERROR_MAP_FAILED"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_UNMAP_FAILED, "CUDA_ERROR_UNMAP_FAILED"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_ARRAY_IS_MAPPED, "CUDA_ERROR_ARRAY_IS_MAPPED"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_ALREADY_MAPPED, "CUDA_ERROR_ALREADY_MAPPED"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_NO_BINARY_FOR_GPU, "CUDA_ERROR_NO_BINARY_FOR_GPU"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_ALREADY_ACQUIRED, "CUDA_ERROR_ALREADY_ACQUIRED"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_NOT_MAPPED, "CUDA_ERROR_NOT_MAPPED"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_NOT_MAPPED_AS_ARRAY, "CUDA_ERROR_NOT_MAPPED_AS_ARRAY"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_NOT_MAPPED_AS_POINTER, "CUDA_ERROR_NOT_MAPPED_AS_POINTER"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_UNSUPPORTED_LIMIT, "CUDA_ERROR_UNSUPPORTED_LIMIT"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_CONTEXT_ALREADY_IN_USE, "CUDA_ERROR_CONTEXT_ALREADY_IN_USE"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_INVALID_SOURCE, "CUDA_ERROR_INVALID_SOURCE"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_FILE_NOT_FOUND, "CUDA_ERROR_FILE_NOT_FOUND"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND, "CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_SHARED_OBJECT_INIT_FAILED, "CUDA_ERROR_SHARED_OBJECT_INIT_FAILED"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_OPERATING_SYSTEM, "CUDA_ERROR_OPERATING_SYSTEM"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_INVALID_HANDLE, "CUDA_ERROR_INVALID_HANDLE"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_NOT_FOUND, "CUDA_ERROR_NOT_FOUND"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_NOT_READY, "CUDA_ERROR_NOT_READY"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_LAUNCH_FAILED, "CUDA_ERROR_LAUNCH_FAILED"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES, "CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_LAUNCH_TIMEOUT, "CUDA_ERROR_LAUNCH_TIMEOUT"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING, "CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE, "CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_CONTEXT_IS_DESTROYED, "CUDA_ERROR_CONTEXT_IS_DESTROYED"));
g_errorStrings.insert(std::pair<int, std::string>(CUDA_ERROR_UNKNOWN, "CUDA_ERROR_UNKNOWN"));
}
if (rCode != CUDA_SUCCESS)
throw ((desc == "") ?
std::string("Error: ") :
(std::string("Error in \"") + desc +
std::string("\": "))) +
g_errorStrings[rCode];
}
void checkError(cublasStatus_t rCode, std::string desc = "") {
static std::map<cublasStatus_t, std::string> g_errorStrings;
if (!g_errorStrings.size()) {
g_errorStrings.insert(std::pair<cublasStatus_t, std::string>(CUBLAS_STATUS_NOT_INITIALIZED, "CUBLAS_STATUS_NOT_INITIALIZED"));
g_errorStrings.insert(std::pair<cublasStatus_t, std::string>(CUBLAS_STATUS_ALLOC_FAILED, "CUBLAS_STATUS_ALLOC_FAILED"));
g_errorStrings.insert(std::pair<cublasStatus_t, std::string>(CUBLAS_STATUS_INVALID_VALUE, "CUBLAS_STATUS_INVALID_VALUE"));
g_errorStrings.insert(std::pair<cublasStatus_t, std::string>(CUBLAS_STATUS_ARCH_MISMATCH, "CUBLAS_STATUS_ARCH_MISMATCH"));
g_errorStrings.insert(std::pair<cublasStatus_t, std::string>(CUBLAS_STATUS_MAPPING_ERROR, "CUBLAS_STATUS_MAPPING_ERROR"));
g_errorStrings.insert(std::pair<cublasStatus_t, std::string>(CUBLAS_STATUS_EXECUTION_FAILED, "CUBLAS_STATUS_EXECUTION_FAILED"));
g_errorStrings.insert(std::pair<cublasStatus_t, std::string>(CUBLAS_STATUS_INTERNAL_ERROR, "CUBLAS_STATUS_INTERNAL_ERROR"));
}
if (rCode != CUBLAS_STATUS_SUCCESS)
throw ((desc == "") ?
std::string("Error: ") :
(std::string("Error in \"") + desc + std::string("\": "))) +
g_errorStrings[rCode];
}
double getTime()
{
struct timeval t;
gettimeofday(&t, NULL);
return (double)t.tv_sec + (double)t.tv_usec / 1e6;
}
bool g_running = false;
template <class T> class GPU_Test {
public:
GPU_Test(int dev, bool doubles, bool tensors) :
d_devNumber(dev), d_doubles(doubles), d_tensors(tensors) {
checkError(cuDeviceGet(&d_dev, d_devNumber));
checkError(cuCtxCreate(&d_ctx, 0, d_dev));
bind();
//checkError(cublasInit());
checkError(cublasCreate(&d_cublas), "init");
if(d_tensors)
checkError(cublasSetMathMode(d_cublas, CUBLAS_TENSOR_OP_MATH));
checkError(cuMemAllocHost((void**)&d_faultyElemsHost, sizeof(int)));
d_error = 0;
g_running = true;
struct sigaction action;
memset(&action, 0, sizeof(struct sigaction));
action.sa_handler = termHandler;
sigaction(SIGTERM, &action, NULL);
}
~GPU_Test() {
bind();
checkError(cuMemFree(d_Cdata), "Free A");
checkError(cuMemFree(d_Adata), "Free B");
checkError(cuMemFree(d_Bdata), "Free C");
cuMemFreeHost(d_faultyElemsHost);
printf("Freed memory for dev %d\n", d_devNumber);
cublasDestroy(d_cublas);
printf("Uninitted cublas\n");
}
static void termHandler(int signum)
{
g_running = false;
}
unsigned long long int getErrors() {
if (*d_faultyElemsHost) {
d_error += (long long int)*d_faultyElemsHost;
}
unsigned long long int tempErrs = d_error;
d_error = 0;
return tempErrs;
}
size_t getIters() {
return d_iters;
}
void bind() {
checkError(cuCtxSetCurrent(d_ctx), "Bind CTX");
}
size_t totalMemory() {
bind();
size_t freeMem, totalMem;
checkError(cuMemGetInfo(&freeMem, &totalMem));
return totalMem;
}
size_t availMemory() {
bind();
size_t freeMem, totalMem;
checkError(cuMemGetInfo(&freeMem, &totalMem));
return freeMem;
}
void initBuffers(T *A, T *B) {
bind();
size_t useBytes = (size_t)((double)availMemory()*USEMEM);
printf("Initialized device %d with %lu MB of memory (%lu MB available, using %lu MB of it), %s%s\n",
d_devNumber, totalMemory()/1024ul/1024ul, availMemory()/1024ul/1024ul, useBytes/1024ul/1024ul,
d_doubles ? "using DOUBLES" : "using FLOATS", d_tensors ? ", using Tensor Cores" : "");
size_t d_resultSize = sizeof(T)*SIZE*SIZE;
d_iters = (useBytes - 2*d_resultSize)/d_resultSize; // We remove A and B sizes
//printf("Results are %d bytes each, thus performing %d iterations\n", d_resultSize, d_iters);
checkError(cuMemAlloc(&d_Cdata, d_iters*d_resultSize), "C alloc");
checkError(cuMemAlloc(&d_Adata, d_resultSize), "A alloc");
checkError(cuMemAlloc(&d_Bdata, d_resultSize), "B alloc");
checkError(cuMemAlloc(&d_faultyElemData, sizeof(int)), "faulty data");
// Populating matrices A and B
checkError(cuMemcpyHtoD(d_Adata, A, d_resultSize), "A -> device");
checkError(cuMemcpyHtoD(d_Bdata, B, d_resultSize), "A -> device");
initCompareKernel();
}
void compute() {
bind();
static const float alpha = 1.0f;
static const float beta = 0.0f;
static const double alphaD = 1.0;
static const double betaD = 0.0;
for (size_t i = 0; i < d_iters; ++i) {
if (d_doubles)
checkError(cublasDgemm(d_cublas, CUBLAS_OP_N, CUBLAS_OP_N,
SIZE, SIZE, SIZE, &alphaD,
(const double*)d_Adata, SIZE,
(const double*)d_Bdata, SIZE,
&betaD,
(double*)d_Cdata + i*SIZE*SIZE, SIZE), "DGEMM");
else
checkError(cublasSgemm(d_cublas, CUBLAS_OP_N, CUBLAS_OP_N,
SIZE, SIZE, SIZE, &alpha,
(const float*)d_Adata, SIZE,
(const float*)d_Bdata, SIZE,
&beta,
(float*)d_Cdata + i*SIZE*SIZE, SIZE), "SGEMM");
}
}
void initCompareKernel() {
const char *kernelFile = "compare.ptx";
{
std::ifstream f(kernelFile);
checkError(f.good() ? CUDA_SUCCESS : CUDA_ERROR_NOT_FOUND, std::string("couldn't find file \"") + kernelFile + "\" from working directory");
}
checkError(cuModuleLoad(&d_module, kernelFile), "load module");
checkError(cuModuleGetFunction(&d_function, d_module,
d_doubles ? "compareD" : "compare"), "get func");
checkError(cuFuncSetCacheConfig(d_function, CU_FUNC_CACHE_PREFER_L1), "L1 config");
checkError(cuParamSetSize(d_function, __alignof(T*) + __alignof(int*) + __alignof(size_t)), "set param size");
checkError(cuParamSetv(d_function, 0, &d_Cdata, sizeof(T*)), "set param");
checkError(cuParamSetv(d_function, __alignof(T*), &d_faultyElemData, sizeof(T*)), "set param");
checkError(cuParamSetv(d_function, __alignof(T*) + __alignof(int*), &d_iters, sizeof(size_t)), "set param");
checkError(cuFuncSetBlockShape(d_function, g_blockSize, g_blockSize, 1), "set block size");
}
void compare() {
checkError(cuMemsetD32Async(d_faultyElemData, 0, 1, 0), "memset");
checkError(cuLaunchGridAsync(d_function, SIZE/g_blockSize, SIZE/g_blockSize, 0), "Launch grid");
checkError(cuMemcpyDtoHAsync(d_faultyElemsHost, d_faultyElemData, sizeof(int), 0), "Read faultyelemdata");
}
bool shouldRun()
{
return g_running;
}
private:
bool d_doubles;
bool d_tensors;
int d_devNumber;
size_t d_iters;
size_t d_resultSize;
long long int d_error;
static const int g_blockSize = 16;
CUdevice d_dev;
CUcontext d_ctx;
CUmodule d_module;
CUfunction d_function;
CUdeviceptr d_Cdata;
CUdeviceptr d_Adata;
CUdeviceptr d_Bdata;
CUdeviceptr d_faultyElemData;
int *d_faultyElemsHost;
cublasHandle_t d_cublas;
};
// Returns the number of devices
int initCuda() {
checkError(cuInit(0));
int deviceCount = 0;
checkError(cuDeviceGetCount(&deviceCount));
if (!deviceCount)
throw std::string("No CUDA devices");
#ifdef USEDEV
if (USEDEV >= deviceCount)
throw std::string("Not enough devices for USEDEV");
#endif
return deviceCount;
}
template<class T> void startBurn(int index, int writeFd, T *A, T *B, bool doubles, bool tensors) {
GPU_Test<T> *our;
try {
our = new GPU_Test<T>(index, doubles, tensors);
our->initBuffers(A, B);
} catch (std::string e) {
fprintf(stderr, "Couldn't init a GPU test: %s\n", e.c_str());
exit(124);
}
// The actual work
try {
int eventIndex = 0;
const int maxEvents = 2;
CUevent events[maxEvents];
for (int i = 0; i < maxEvents; ++i)
cuEventCreate(events + i, 0);
int nonWorkIters = maxEvents;
while (our->shouldRun()) {
our->compute();
our->compare();
checkError(cuEventRecord(events[eventIndex], 0), "Record event");
eventIndex = ++eventIndex % maxEvents;
while (cuEventQuery(events[eventIndex]) != CUDA_SUCCESS) usleep(1000);
if (--nonWorkIters > 0) continue;
int ops = our->getIters();
write(writeFd, &ops, sizeof(int));
ops = our->getErrors();
write(writeFd, &ops, sizeof(int));
}
for (int i = 0; i < maxEvents; ++i)
cuEventSynchronize(events[i]);
delete our;
} catch (std::string e) {
fprintf(stderr, "Failure during compute: %s\n", e.c_str());
int ops = -1;
// Signalling that we failed
write(writeFd, &ops, sizeof(int));
write(writeFd, &ops, sizeof(int));
exit(111);
}
}
int pollTemp(pid_t *p) {
int tempPipe[2];
pipe(tempPipe);
pid_t myPid = fork();
if (!myPid) {
close(tempPipe[0]);
dup2(tempPipe[1], STDOUT_FILENO); // Stdout
//execlp("nvidia-smi", "nvidia-smi", "-l", "5", "-q", "-d", "TEMPERATURE", NULL);//原代码这里没注释掉，但是TX2是不能使用nvidia-smi的，所以这里留着会报错；
//fprintf(stderr, "Could not invoke nvidia-smi, no temps available\n");//原代码这里没注释掉，但是TX2是不能使用nvidia-smi的，所以这里留着会报错；
exit(0);
}
*p = myPid;
close(tempPipe[1]);
return tempPipe[0];
}
void updateTemps(int handle, std::vector<int> *temps) {
const int readSize = 10240;
static int gpuIter = 0;
char data[readSize+1];
int curPos = 0;
do {
read(handle, data+curPos, sizeof(char));
} while (data[curPos++] != '\n');
data[curPos-1] = 0;
int tempValue;
// FIXME: The syntax of this print might change in the future..
if (sscanf(data, " GPU Current Temp : %d C", &tempValue) == 1) {
//printf("read temp val %d\n", tempValue);
temps->at(gpuIter) = tempValue;
gpuIter = (gpuIter+1)%(temps->size());
} else if (!strcmp(data, " Gpu : N/A"))
gpuIter = (gpuIter+1)%(temps->size()); // We rotate the iterator for N/A values as well
}
void listenClients(std::vector<int> clientFd, std::vector<pid_t> clientPid, int runTime) {
fd_set waitHandles;
pid_t tempPid;
int tempHandle = pollTemp(&tempPid);
int maxHandle = tempHandle;
FD_ZERO(&waitHandles);
FD_SET(tempHandle, &waitHandles);
for (size_t i = 0; i < clientFd.size(); ++i) {
if (clientFd.at(i) > maxHandle)
maxHandle = clientFd.at(i);
FD_SET(clientFd.at(i), &waitHandles);
}
std::vector<int> clientTemp;
std::vector<int> clientErrors;
std::vector<int> clientCalcs;
std::vector<struct timespec> clientUpdateTime;
std::vector<float> clientGflops;
std::vector<bool> clientFaulty;
time_t startTime = time(0);
for (size_t i = 0; i < clientFd.size(); ++i) {
clientTemp.push_back(0);
clientErrors.push_back(0);
clientCalcs.push_back(0);
struct timespec thisTime;
clock_gettime(CLOCK_REALTIME, &thisTime);
clientUpdateTime.push_back(thisTime);
clientGflops.push_back(0.0f);
clientFaulty.push_back(false);
}
int changeCount;
float nextReport = 10.0f;
bool childReport = false;
while ((changeCount = select(maxHandle+1, &waitHandles, NULL, NULL, NULL))) {
size_t thisTime = time(0);
struct timespec thisTimeSpec;
clock_gettime(CLOCK_REALTIME, &thisTimeSpec);
//printf("got new data! %d\n", changeCount);
// Going through all descriptors
for (size_t i = 0; i < clientFd.size(); ++i)
if (FD_ISSET(clientFd.at(i), &waitHandles)) {
// First, reading processed
int processed, errors;
read(clientFd.at(i), &processed, sizeof(int));
// Then errors
read(clientFd.at(i), &errors, sizeof(int));
clientErrors.at(i) += errors;
if (processed == -1)
clientCalcs.at(i) = -1;
else
{
double flops = (double)processed * (double)OPS_PER_MUL;
struct timespec clientPrevTime = clientUpdateTime.at(i);
double clientTimeDelta = (double)thisTimeSpec.tv_sec + (double)thisTimeSpec.tv_nsec / 1000000000.0 - ((double)clientPrevTime.tv_sec + (double)clientPrevTime.tv_nsec / 1000000000.0);
clientUpdateTime.at(i) = thisTimeSpec;
clientGflops.at(i) = (double)((unsigned long long int)processed * OPS_PER_MUL) / clientTimeDelta / 1000.0 / 1000.0 / 1000.0;
clientCalcs.at(i) += processed;
}
childReport = true;
}
if (FD_ISSET(tempHandle, &waitHandles))
updateTemps(tempHandle, &clientTemp);
// Resetting the listeners
FD_ZERO(&waitHandles);
FD_SET(tempHandle, &waitHandles);
for (size_t i = 0; i < clientFd.size(); ++i)
FD_SET(clientFd.at(i), &waitHandles);
// Printing progress (if a child has initted already)
if (childReport) {
float elapsed = fminf((float)(thisTime-startTime)/(float)runTime*100.0f, 100.0f);
printf("\r%.1f%% ", elapsed);
printf("proc'd: ");
for (size_t i = 0; i < clientCalcs.size(); ++i) {
printf("%d (%.0f Gflop/s) ", clientCalcs.at(i), clientGflops.at(i));
if (i != clientCalcs.size() - 1)
printf("- ");
}
printf(" errors: ");
for (size_t i = 0; i < clientErrors.size(); ++i) {
std::string note = "%d ";
if (clientCalcs.at(i) == -1)
note += " (DIED!)";
else if (clientErrors.at(i))
note += " (WARNING!)";
printf(note.c_str(), clientErrors.at(i));
if (i != clientCalcs.size() - 1)
printf("- ");
}
printf(" temps: ");
for (size_t i = 0; i < clientTemp.size(); ++i) {
printf(clientTemp.at(i) != 0 ? "%d C " : "-- ", clientTemp.at(i));
if (i != clientCalcs.size() - 1)
printf("- ");
}
fflush(stdout);
if (nextReport < elapsed) {
nextReport = elapsed + 10.0f;
printf("\n\tSummary at: ");
fflush(stdout);
system("date"); // Printing a date
fflush(stdout);
printf("\n");
//printf("\t(checkpoint)\n");
for (size_t i = 0; i < clientErrors.size(); ++i) {
if (clientErrors.at(i))
clientFaulty.at(i) = true;
clientErrors.at(i) = 0;
}
}
}
// Checking whether all clients are dead
bool oneAlive = false;
for (size_t i = 0; i < clientCalcs.size(); ++i)
if (clientCalcs.at(i) != -1)
oneAlive = true;
if (!oneAlive) {
fprintf(stderr, "\n\nNo clients are alive! Aborting\n");
exit(123);
}
if (startTime + runTime < thisTime)
break;
}
printf("\nKilling processes.. ");
fflush(stdout);
for (size_t i = 0; i < clientPid.size(); ++i)
kill(clientPid.at(i), 15);
kill(tempPid, 15);
close(tempHandle);
while (wait(NULL) != -1);
printf("done\n");
printf("\nTested %d GPUs:\n", (int)clientPid.size());
for (size_t i = 0; i < clientPid.size(); ++i)
printf("\tGPU %d: %s\n", (int)i, clientFaulty.at(i) ? "FAULTY" : "OK");
}
template<class T> void launch(int runLength, bool useDoubles, bool useTensorCores) {
//system("nvidia-smi -L");//原代码这里没注释掉，但是TX2是不能使用nvidia-smi的，所以这里留着会报错；
//system("tegrastats");//原代码这里根本没有，但是TX2是不能使用nvidia-smi，所以这里准备尝试曲线救国，使用tx2自带的tegrastats，但是尝试后不管用；
// Initting A and B with random data
T *A = (T*) malloc(sizeof(T)*SIZE*SIZE);
T *B = (T*) malloc(sizeof(T)*SIZE*SIZE);
srand(10);
for (size_t i = 0; i < SIZE*SIZE; ++i) {
A[i] = (T)((double)(rand()%1000000)/100000.0);
B[i] = (T)((double)(rand()%1000000)/100000.0);
}
// Forking a process.. This one checks the number of devices to use,
// returns the value, and continues to use the first one.
int mainPipe[2];
pipe(mainPipe);
int readMain = mainPipe[0];
std::vector<int> clientPipes;
std::vector<pid_t> clientPids;
clientPipes.push_back(readMain);
pid_t myPid = fork();
if (!myPid) {
// Child
close(mainPipe[0]);
int writeFd = mainPipe[1];
int devCount = initCuda();
write(writeFd, &devCount, sizeof(int));
startBurn<T>(0, writeFd, A, B, useDoubles, useTensorCores);
close(writeFd);
return;
} else {
clientPids.push_back(myPid);
close(mainPipe[1]);
int devCount;
read(readMain, &devCount, sizeof(int));
if (!devCount) {
fprintf(stderr, "No CUDA devices\n");
exit(EXIT_FAILURE);
} else {
for (int i = 1; i < devCount; ++i) {
int slavePipe[2];
pipe(slavePipe);
clientPipes.push_back(slavePipe[0]);
pid_t slavePid = fork();
if (!slavePid) {
// Child
close(slavePipe[0]);
initCuda();
startBurn<T>(i, slavePipe[1], A, B, useDoubles, useTensorCores);
close(slavePipe[1]);
return;
} else {
clientPids.push_back(slavePid);
close(slavePipe[1]);
}
}
listenClients(clientPipes, clientPids, runLength);
}
}
for (size_t i = 0; i < clientPipes.size(); ++i)
close(clientPipes.at(i));
free(A);
free(B);
}
int main(int argc, char **argv) {
int runLength = 10;
bool useDoubles = false;
bool useTensorCores = false;
int thisParam = 0;
std::vector<std::string> args(argv, argv + argc);
for (size_t i = 1; i < args.size(); ++i)
{
if (argc >= 2 && std::string(argv[i]).find("-d") != std::string::npos)
{
useDoubles = true;
thisParam++;
}
if (argc >= 2 && std::string(argv[i]).find("-tc") != std::string::npos)
{
useTensorCores = true;
thisParam++;
}
}
if (argc-thisParam < 2)
printf("Run length not specified in the command line. Burning for 10 secs\n");
else
runLength = atoi(argv[1+thisParam]);
useDoubles = true;//原代码这里根本没有，但是这里需要修改形式参数，增加gpu-burn的运行时间，之前只能10秒，增加这条之后可以达到95秒；
runLength = 60;//原代码这里根本没有，但是这里需要修改形式参数，增加gpu-burn的运行时间，之前只能10秒，增加这条之后可以达到95秒；
if (useDoubles)
launch<double>(runLength, useDoubles, useTensorCores);
else
launch<float>(runLength, useDoubles, useTensorCores);
return 0;
}
'''
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