int main(int argc, char **argv) {
char *precisionChoice;
cutGetCmdLineArgumentstr(argc, (const char **)argv, "type", &precisionChoice);
if(precisionChoice == NULL)
useDoublePrecision = 0;
else {
if(!strcasecmp(precisionChoice, "double"))
useDoublePrecision = 1;
else
useDoublePrecision = 0;
}
const int MAX_GPU_COUNT = 8;
const int OPT_N = 256;
const int PATH_N = 1 << 18;
const unsigned int SEED = 777;
//Input data array
TOptionData optionData[OPT_N];
//Final GPU MC results
TOptionValue callValueGPU[OPT_N];
//"Theoretical" call values by Black-Scholes formula
float callValueBS[OPT_N];
//Solver config
TOptionPlan optionSolver[MAX_GPU_COUNT];
//OS thread ID
CUTThread threadID[MAX_GPU_COUNT];
//GPU number present in the system
int GPU_N;
int gpuBase, gpuIndex;
int i;
//Timer
unsigned int hTimer;
float time;
double
delta, ref, sumDelta, sumRef, sumReserve;
cutilSafeCall( cudaGetDeviceCount(&GPU_N) );
cutilCheckError( cutCreateTimer(&hTimer) );
#ifdef _EMU
GPU_N = 1;
#endif
printf("main(): generating input data...\n");
srand(123);
for(i = 0; i < OPT_N; i++) {
optionData[i].S = randFloat(5.0f, 50.0f);
optionData[i].X = randFloat(10.0f, 25.0f);
optionData[i].T = randFloat(1.0f, 5.0f);
optionData[i].R = 0.06f;
optionData[i].V = 0.10f;
callValueGPU[i].Expected = -1.0f;
callValueGPU[i].Confidence = -1.0f;
}
printf("main(): starting %i host threads...\n", GPU_N);
//Get option count for each GPU
for(i = 0; i < GPU_N; i++)
optionSolver[i].optionCount = OPT_N / GPU_N;
//Take into account cases with "odd" option counts
for(i = 0; i < (OPT_N % GPU_N); i++)
optionSolver[i].optionCount++;
//Assign GPU option ranges
gpuBase = 0;
for(i = 0; i < GPU_N; i++) {
optionSolver[i].device = i;
optionSolver[i].optionData = optionData + gpuBase;
optionSolver[i].callValue = callValueGPU + gpuBase;
optionSolver[i].seed = SEED;
optionSolver[i].pathN = PATH_N;
gpuBase += optionSolver[i].optionCount;
}
//Start the timer
cutilCheckError( cutResetTimer(hTimer) );
cutilCheckError( cutStartTimer(hTimer) );
//Start CPU thread for each GPU
for(gpuIndex = 0; gpuIndex < GPU_N; gpuIndex++)
threadID[gpuIndex] = cutStartThread((CUT_THREADROUTINE)solverThread, &optionSolver[gpuIndex]);
//Stop the timer
cutilCheckError( cutStopTimer(hTimer) );
time = cutGetTimerValue(hTimer);
printf("main(): waiting for GPU results...\n");
cutWaitForThreads(threadID, GPU_N);
printf("main(): GPU statistics\n");
for(i = 0; i < GPU_N; i++) {
printf("GPU #%i\n", optionSolver[i].device);
printf("Options : %i\n", optionSolver[i].optionCount);
printf("Simulation paths: %i\n", optionSolver[i].pathN);
}
printf("\nTotal time (ms.): %f\n", time);
printf("Options per sec.: %f\n", OPT_N / (time * 0.001));
#ifdef DO_CPU
printf("main(): running CPU MonteCarlo...\n");
TOptionValue callValueCPU;
sumDelta = 0;
sumRef = 0;
for(i = 0; i < OPT_N; i++) {
MonteCarloCPU(
callValueCPU,
optionData[i],
NULL,
PATH_N
);
delta = fabs(callValueCPU.Expected - callValueGPU[i].Expected);
ref = callValueCPU.Expected;
sumDelta += delta;
sumRef += fabs(ref);
printf("Exp : %f | %f\t", callValueCPU.Expected, callValueGPU[i].Expected);
printf("Conf: %f | %f\n", callValueCPU.Confidence, callValueGPU[i].Confidence);
}
printf("L1 norm: %E\n", sumDelta / sumRef);
#endif
printf("main(): comparing Monte Carlo and Black-Scholes results...\n");
sumDelta = 0;
sumRef = 0;
sumReserve = 0;
for(i = 0; i < OPT_N; i++) {
BlackScholesCall(
callValueBS[i],
optionData[i]
);
delta = fabs(callValueBS[i] - callValueGPU[i].Expected);
ref = callValueBS[i];
sumDelta += delta;
sumRef += fabs(ref);
if(delta > 1e-6) sumReserve += callValueGPU[i].Confidence / delta;
#ifdef PRINT_RESULTS
printf("BS: %f; delta: %E\n", callValueBS[i], delta);
#endif
}
sumReserve /= OPT_N;
printf("L1 norm : %E\n", sumDelta / sumRef);
printf("Average reserve: %f\n", sumReserve);
printf((sumReserve > 1.0f) ? "PASSED\n" : "FAILED.\n");
printf("Shutting down...\n");
cutilCheckError( cutDeleteTimer(hTimer) );
cutilExit(argc, argv);
}
int main(int argc, char **argv)
{
char *multiMethodChoice = NULL;
char *scalingChoice = NULL;
bool use_threads = true;
bool bqatest = false;
bool strongScaling = false;
pArgc = &argc;
pArgv = argv;
printf("%s Starting...\n\n", argv[0]);
if (checkCmdLineFlag(argc, (const char **)argv, "qatest"))
{
bqatest = true;
}
getCmdLineArgumentString(argc, (const char **)argv, "method", &multiMethodChoice);
getCmdLineArgumentString(argc, (const char **)argv, "scaling", &scalingChoice);
if (checkCmdLineFlag(argc, (const char **)argv, "h") ||
checkCmdLineFlag(argc, (const char **)argv, "help"))
{
usage();
exit(EXIT_SUCCESS);
}
if (multiMethodChoice == NULL)
{
use_threads = true;
}
else
{
if (!strcasecmp(multiMethodChoice, "threaded"))
{
use_threads = true;
}
else
{
use_threads = false;
}
}
if (use_threads == false)
{
printf("Using single CPU thread for multiple GPUs\n");
}
if (scalingChoice == NULL)
{
strongScaling = false;
}
else
{
if (!strcasecmp(scalingChoice, "strong"))
{
strongScaling = true;
}
else
{
strongScaling = false;
}
}
//GPU number present in the system
int GPU_N;
checkCudaErrors(cudaGetDeviceCount(&GPU_N));
int nOptions = 256;
nOptions = adjustProblemSize(GPU_N, nOptions);
// select problem size
int scale = (strongScaling) ? 1 : GPU_N;
int OPT_N = nOptions * scale;
int PATH_N = 262144;
const unsigned long long SEED = 777;
// initialize the timers
hTimer = new StopWatchInterface*[GPU_N];
for (int i=0; i<GPU_N; i++)
{
sdkCreateTimer(&hTimer[i]);
sdkResetTimer(&hTimer[i]);
}
//Input data array
TOptionData *optionData = new TOptionData[OPT_N];
//Final GPU MC results
TOptionValue *callValueGPU = new TOptionValue[OPT_N];
//"Theoretical" call values by Black-Scholes formula
float *callValueBS = new float[OPT_N];
//Solver config
TOptionPlan *optionSolver = new TOptionPlan[GPU_N];
//OS thread ID
CUTThread *threadID = new CUTThread[GPU_N];
int gpuBase, gpuIndex;
int i;
float time;
double delta, ref, sumDelta, sumRef, sumReserve;
printf("MonteCarloMultiGPU\n");
printf("==================\n");
printf("Parallelization method = %s\n", use_threads ? "threaded" : "streamed");
printf("Problem scaling = %s\n", strongScaling? "strong" : "weak");
printf("Number of GPUs = %d\n", GPU_N);
printf("Total number of options = %d\n", OPT_N);
printf("Number of paths = %d\n", PATH_N);
printf("main(): generating input data...\n");
srand(123);
for (i=0; i < OPT_N; i++)
{
optionData[i].S = randFloat(5.0f, 50.0f);
optionData[i].X = randFloat(10.0f, 25.0f);
optionData[i].T = randFloat(1.0f, 5.0f);
optionData[i].R = 0.06f;
optionData[i].V = 0.10f;
callValueGPU[i].Expected = -1.0f;
callValueGPU[i].Confidence = -1.0f;
}
printf("main(): starting %i host threads...\n", GPU_N);
//Get option count for each GPU
for (i = 0; i < GPU_N; i++)
{
optionSolver[i].optionCount = OPT_N / GPU_N;
}
//Take into account cases with "odd" option counts
for (i = 0; i < (OPT_N % GPU_N); i++)
{
optionSolver[i].optionCount++;
}
//Assign GPU option ranges
gpuBase = 0;
for (i = 0; i < GPU_N; i++)
{
optionSolver[i].device = i;
optionSolver[i].optionData = optionData + gpuBase;
optionSolver[i].callValue = callValueGPU + gpuBase;
// all devices use the same global seed, but start
// the sequence at a different offset
optionSolver[i].seed = SEED;
optionSolver[i].pathN = PATH_N;
gpuBase += optionSolver[i].optionCount;
}
if (use_threads || bqatest)
{
//Start CPU thread for each GPU
for (gpuIndex = 0; gpuIndex < GPU_N; gpuIndex++)
{
threadID[gpuIndex] = cutStartThread((CUT_THREADROUTINE)solverThread, &optionSolver[gpuIndex]);
}
printf("main(): waiting for GPU results...\n");
cutWaitForThreads(threadID, GPU_N);
printf("main(): GPU statistics, threaded\n");
for (i = 0; i < GPU_N; i++)
{
cudaDeviceProp deviceProp;
checkCudaErrors(cudaGetDeviceProperties(&deviceProp, optionSolver[i].device));
printf("GPU Device #%i: %s\n", optionSolver[i].device, deviceProp.name);
printf("Options : %i\n", optionSolver[i].optionCount);
printf("Simulation paths: %i\n", optionSolver[i].pathN);
time = sdkGetTimerValue(&hTimer[i]);
printf("Total time (ms.): %f\n", time);
printf("Options per sec.: %f\n", OPT_N / (time * 0.001));
}
printf("main(): comparing Monte Carlo and Black-Scholes results...\n");
sumDelta = 0;
sumRef = 0;
sumReserve = 0;
for (i = 0; i < OPT_N; i++)
{
BlackScholesCall(callValueBS[i], optionData[i]);
delta = fabs(callValueBS[i] - callValueGPU[i].Expected);
ref = callValueBS[i];
sumDelta += delta;
sumRef += fabs(ref);
if (delta > 1e-6)
{
sumReserve += callValueGPU[i].Confidence / delta;
}
#ifdef PRINT_RESULTS
printf("BS: %f; delta: %E\n", callValueBS[i], delta);
#endif
}
sumReserve /= OPT_N;
}
if (!use_threads || bqatest)
{
multiSolver(optionSolver, GPU_N);
printf("main(): GPU statistics, streamed\n");
for (i = 0; i < GPU_N; i++)
{
cudaDeviceProp deviceProp;
checkCudaErrors(cudaGetDeviceProperties(&deviceProp, optionSolver[i].device));
printf("GPU Device #%i: %s\n", optionSolver[i].device, deviceProp.name);
printf("Options : %i\n", optionSolver[i].optionCount);
printf("Simulation paths: %i\n", optionSolver[i].pathN);
}
time = sdkGetTimerValue(&hTimer[0]);
printf("\nTotal time (ms.): %f\n", time);
printf("\tNote: This is elapsed time for all to compute.\n");
printf("Options per sec.: %f\n", OPT_N / (time * 0.001));
printf("main(): comparing Monte Carlo and Black-Scholes results...\n");
sumDelta = 0;
sumRef = 0;
sumReserve = 0;
for (i = 0; i < OPT_N; i++)
{
BlackScholesCall(callValueBS[i], optionData[i]);
delta = fabs(callValueBS[i] - callValueGPU[i].Expected);
ref = callValueBS[i];
sumDelta += delta;
sumRef += fabs(ref);
if (delta > 1e-6)
{
sumReserve += callValueGPU[i].Confidence / delta;
}
#ifdef PRINT_RESULTS
printf("BS: %f; delta: %E\n", callValueBS[i], delta);
#endif
}
sumReserve /= OPT_N;
}
#ifdef DO_CPU
printf("main(): running CPU MonteCarlo...\n");
TOptionValue callValueCPU;
sumDelta = 0;
sumRef = 0;
for (i = 0; i < OPT_N; i++)
{
MonteCarloCPU(
callValueCPU,
optionData[i],
NULL,
PATH_N
);
delta = fabs(callValueCPU.Expected - callValueGPU[i].Expected);
ref = callValueCPU.Expected;
sumDelta += delta;
sumRef += fabs(ref);
printf("Exp : %f | %f\t", callValueCPU.Expected, callValueGPU[i].Expected);
printf("Conf: %f | %f\n", callValueCPU.Confidence, callValueGPU[i].Confidence);
}
printf("L1 norm: %E\n", sumDelta / sumRef);
#endif
printf("Shutting down...\n");
for (int i=0; i<GPU_N; i++)
{
sdkStartTimer(&hTimer[i]);
checkCudaErrors(cudaSetDevice(i));
// cudaDeviceReset causes the driver to clean up all state. While
// not mandatory in normal operation, it is good practice. It is also
// needed to ensure correct operation when the application is being
// profiled. Calling cudaDeviceReset causes all profile data to be
// flushed before the application exits
cudaDeviceReset();
}
delete[] optionSolver;
delete[] callValueBS;
delete[] callValueGPU;
delete[] optionData;
delete[] threadID;
delete[] hTimer;
printf("Test Summary...\n");
printf("L1 norm : %E\n", sumDelta / sumRef);
printf("Average reserve: %f\n", sumReserve);
printf(sumReserve > 1.0f ? "Test passed\n" : "Test failed!\n");
exit(sumReserve > 1.0f ? EXIT_SUCCESS : EXIT_FAILURE);
}
