void cleanup()
{
cutilCheckError( cutDeleteTimer( timer));
if(h_img)cutFree(h_img);
cutilSafeCall(cudaFree(d_img));
cutilSafeCall(cudaFree(d_temp));
// Refer to boxFilter_kernel.cu for implementation
freeTextures();
//DEPRECATED: cutilSafeCall(cudaGLUnregisterBufferObject(pbo));
cudaGraphicsUnregisterResource(cuda_pbo_resource);
glDeleteBuffersARB(1, &pbo);
glDeleteTextures(1, &texid);
glDeleteProgramsARB(1, &shader);
if (g_CheckRender) {
delete g_CheckRender;
g_CheckRender = NULL;
}
if (g_FrameBufferObject) {
delete g_FrameBufferObject;
g_FrameBufferObject = NULL;
}
}
void cleanup()
{
if (g_bQAReadback) {
cudaFree(d_pos);
cudaFree(d_normal);
} else {
cutilCheckError( cutDeleteTimer( timer ));
deleteVBO(&posVbo, &cuda_posvbo_resource);
deleteVBO(&normalVbo, &cuda_normalvbo_resource);
}
cudppDestroyPlan(scanplan);
cutilSafeCall(cudaFree(d_edgeTable));
cutilSafeCall(cudaFree(d_triTable));
cutilSafeCall(cudaFree(d_numVertsTable));
cutilSafeCall(cudaFree(d_voxelVerts));
cutilSafeCall(cudaFree(d_voxelVertsScan));
cutilSafeCall(cudaFree(d_voxelOccupied));
cutilSafeCall(cudaFree(d_voxelOccupiedScan));
cutilSafeCall(cudaFree(d_compVoxelArray));
if (d_volume) cutilSafeCall(cudaFree(d_volume));
if (g_CheckRender) {
delete g_CheckRender; g_CheckRender = NULL;
}
if (g_FrameBufferObject) {
delete g_FrameBufferObject; g_FrameBufferObject = NULL;
}
}
void cleanup()
{
cutilCheckError( cutDeleteTimer( timer));
deleteVBO(&vbo);
if (g_CheckRender) {
delete g_CheckRender; g_CheckRender = NULL;
}
}
void cleanup()
{
cutilCheckError(cutStopTimer(timer));
cutilCheckError(cutDeleteTimer( timer));
cudaFree(a_d);cudaFree(b_d);cudaFree(r_d);
cudaThreadExit();
}
void cleanup()
{
cutilCheckError( cutDeleteTimer( hTimer));
glDeleteProgramsARB(1, &shader);
if (g_CheckRender) {
delete g_CheckRender; g_CheckRender = NULL;
}
if (g_FrameBufferObject) {
delete g_FrameBufferObject; g_FrameBufferObject = NULL;
}
}
void cleanup()
{
if (psystem) delete psystem;
if (renderer) delete renderer;
if (floorProg) delete floorProg;
cutilCheckError(cutDeleteTimer(timer));
if (params) delete params;
if (floorTex) glDeleteTextures(1, &floorTex);
if (g_CheckRender) {
delete g_CheckRender; g_CheckRender = NULL;
}
}
void cleanup(void) {
cutilSafeCall(cudaGraphicsUnregisterResource(cuda_pbo_resource));
glBindBuffer(GL_PIXEL_UNPACK_BUFFER, 0);
glDeleteBuffers(1, &pbo_buffer);
glDeleteTextures(1, &texid);
deleteTexture();
if (g_CheckRender) {
delete g_CheckRender; g_CheckRender = NULL;
}
cutilCheckError(cutDeleteTimer(timer));
}
void cleanup()
{
cutilCheckError( cutDeleteTimer( timer));
freeCudaBuffers();
if (pbo) {
cutilSafeCall(cudaGraphicsUnregisterResource(cuda_pbo_resource));
glDeleteBuffersARB(1, &pbo);
glDeleteTextures(1, &tex);
}
if (g_CheckRender) {
delete g_CheckRender; g_CheckRender = NULL;
}
}
void cleanup()
{
free(a_h);free(b_h);free(r_h);
free(control);
cutilCheckError(cutStopTimer(timer));
cutilCheckError(cutDeleteTimer( timer));
cudaFree(a_d);cudaFree(b_d);cudaFree(r_d);
cutilSafeCall(release());
checkCUDAError("release");
cudaThreadExit();
}
void _runBenchmark(int iterations)
{
// once without timing to prime the device
if (!useCpu)
m_nbody->update(activeParams.m_timestep);
if (useCpu)
{
cutCreateTimer(&timer);
cutStartTimer(timer);
}
else
{
cutilSafeCall(cudaEventRecord(startEvent, 0));
}
for (int i = 0; i < iterations; ++i)
{
m_nbody->update(activeParams.m_timestep);
}
float milliseconds = 0;
if (useCpu)
{
cutStopTimer(timer);
milliseconds = cutGetTimerValue(timer);
cutDeleteTimer(timer);
}
else
{
cutilSafeCall(cudaEventRecord(stopEvent, 0));
cutilSafeCall(cudaEventSynchronize(stopEvent));
cutilSafeCall( cudaEventElapsedTime(&milliseconds, startEvent, stopEvent));
}
double interactionsPerSecond = 0;
double gflops = 0;
computePerfStats(interactionsPerSecond, gflops, milliseconds, iterations);
shrLog("%d bodies, total time for %d iterations: %.3f ms\n",
numBodies, iterations, milliseconds);
shrLog("= %.3f billion interactions per second\n", interactionsPerSecond);
shrLog("= %.3f %s-precision GFLOP/s at %d flops per interaction\n", gflops,
(sizeof(T) > 4) ? "double" : "single", flopsPerInteraction);
}
~NBodyDemo()
{
if (m_nbodyCpu)
delete m_nbodyCpu;
if (m_nbodyCuda)
delete m_nbodyCuda;
if (m_hPos)
delete [] m_hPos;
if (m_hVel)
delete [] m_hVel;
if (m_hColor)
delete [] m_hColor;
cutilSafeCall(cudaEventDestroy(startEvent));
cutilSafeCall(cudaEventDestroy(stopEvent));
cutilSafeCall(cudaEventDestroy(hostMemSyncEvent));
cutilCheckError(cutDeleteTimer(demoTimer));
delete m_renderer;
}
void cleanup()
{
cutilCheckError( cutDeleteTimer( timer));
if (!h_img) {
free(h_img);
}
cutilSafeCall(cudaFree(d_img));
cutilSafeCall(cudaFree(d_temp));
if (!runBenchmark) {
if (pbo) {
cutilSafeCall(cudaGLUnregisterBufferObject(pbo));
glDeleteBuffersARB(1, &pbo);
}
if (texid) {
glDeleteTextures(1, &texid);
}
}
if (g_CheckRender) {
delete g_CheckRender; g_CheckRender = NULL;
}
}
int main(int, char* argv[])
{
// In assignment 2.1, we will fix filter_radius as 1,
// and therefore it's filter size is 3x3.
const unsigned int filter_radius = 1;
unsigned int width, height;
unsigned char* h_img = NULL;
unsigned char* your_result = NULL;
unsigned char* gold_result = NULL;
// This is the filename of the image file in PGM format
// and the file should be put under the subdirectory, "data".
const char* image_filename = "test.pgm";
loadImage(&h_img, &width, &height, image_filename, argv[0]);
if (width <= 2*filter_radius || height <= 2*filter_radius) {
fprintf(stderr, "Filter radius is too large.\n");
exit(-1);
}
your_result = (unsigned char*) malloc(width*height*sizeof(unsigned char));
gold_result = (unsigned char*) malloc(width*height*sizeof(unsigned char));
// Run your median filter
{
unsigned int timer = 0;
cutilCheckError(cutCreateTimer(&timer));
cutilCheckError(cutStartTimer(timer));
// You should implemnt medianFilter() in medianFilter_kernel.cu
medianFilter(h_img, your_result, width, height, filter_radius);
cutilCheckError(cutStopTimer(timer));
printf("[Yours] Processing time: %f (ms) \n", cutGetTimerValue(timer));
cutilCheckError(cutDeleteTimer(timer));
}
// Run the reference median filter
{
unsigned int timer = 0;
cutilCheckError(cutCreateTimer(&timer));
cutilCheckError(cutStartTimer(timer));
medianFilter_gold(h_img, gold_result, width, height, filter_radius);
cutilCheckError(cutStopTimer(timer));
printf("[Gold] Processing time: %f (ms) \n", cutGetTimerValue(timer));
cutilCheckError(cutDeleteTimer(timer));
}
// You can use saveImage() to save the result.
// Under Windows, you can use IrfanView (http://www.irfanview.com/) to view PGM files.
// Or you can convert PGM into JPEG, PNG, and etc.
// saveImage(your_result, width, height, "output.pgm");
// saveImage(gold_result, width, height, "gold.pgm");
// Compare your result and the reference result.
{
if (test(your_result, gold_result, width, height, filter_radius)) {
printf("PASSED\n");
} else {
printf("FAILED\n");
}
}
free(gold_result);
free(your_result);
return 0;
}
int main(int argc, char **argv)
{
uchar *h_Data;
uint *h_HistogramCPU, *h_HistogramGPU;
uchar *d_Data;
uint *d_Histogram;
uint hTimer;
int PassFailFlag = 1;
uint byteCount = 64 * 1048576;
uint uiSizeMult = 1;
cudaDeviceProp deviceProp;
deviceProp.major = 0;
deviceProp.minor = 0;
int dev;
shrQAStart(argc, argv);
// set logfile name and start logs
shrSetLogFileName ("histogram.txt");
//Use command-line specified CUDA device, otherwise use device with highest Gflops/s
if( shrCheckCmdLineFlag(argc, (const char**)argv, "device") ) {
dev = cutilDeviceInit(argc, argv);
if (dev < 0) {
printf("No CUDA Capable Devices found, exiting...\n");
shrQAFinishExit(argc, (const char **)argv, QA_WAIVED);
}
} else {
cudaSetDevice( dev = cutGetMaxGflopsDeviceId() );
cutilSafeCall( cudaChooseDevice(&dev, &deviceProp) );
}
cutilSafeCall( cudaGetDeviceProperties(&deviceProp, dev) );
printf("CUDA device [%s] has %d Multi-Processors, Compute %d.%d\n",
deviceProp.name, deviceProp.multiProcessorCount, deviceProp.major, deviceProp.minor);
int version = deviceProp.major * 0x10 + deviceProp.minor;
if(version < 0x11)
{
printf("There is no device supporting a minimum of CUDA compute capability 1.1 for this SDK sample\n");
cutilDeviceReset();
shrQAFinishExit(argc, (const char **)argv, QA_WAIVED);
}
cutilCheckError(cutCreateTimer(&hTimer));
// Optional Command-line multiplier to increase size of array to histogram
if (shrGetCmdLineArgumentu(argc, (const char**)argv, "sizemult", &uiSizeMult))
{
uiSizeMult = CLAMP(uiSizeMult, 1, 10);
byteCount *= uiSizeMult;
}
shrLog("Initializing data...\n");
shrLog("...allocating CPU memory.\n");
h_Data = (uchar *)malloc(byteCount);
h_HistogramCPU = (uint *)malloc(HISTOGRAM256_BIN_COUNT * sizeof(uint));
h_HistogramGPU = (uint *)malloc(HISTOGRAM256_BIN_COUNT * sizeof(uint));
shrLog("...generating input data\n");
srand(2009);
for(uint i = 0; i < byteCount; i++)
h_Data[i] = rand() % 256;
shrLog("...allocating GPU memory and copying input data\n\n");
cutilSafeCall( cudaMalloc((void **)&d_Data, byteCount ) );
cutilSafeCall( cudaMalloc((void **)&d_Histogram, HISTOGRAM256_BIN_COUNT * sizeof(uint) ) );
cutilSafeCall( cudaMemcpy(d_Data, h_Data, byteCount, cudaMemcpyHostToDevice) );
{
shrLog("Starting up 64-bin histogram...\n\n");
initHistogram64();
shrLog("Running 64-bin GPU histogram for %u bytes (%u runs)...\n\n", byteCount, numRuns);
for(int iter = -1; iter < numRuns; iter++){
//iter == -1 -- warmup iteration
if(iter == 0){
cutilSafeCall( cutilDeviceSynchronize() );
cutilCheckError( cutResetTimer(hTimer) );
cutilCheckError( cutStartTimer(hTimer) );
}
histogram64(d_Histogram, d_Data, byteCount);
}
cutilSafeCall( cutilDeviceSynchronize() );
cutilCheckError( cutStopTimer(hTimer));
double dAvgSecs = 1.0e-3 * (double)cutGetTimerValue(hTimer) / (double)numRuns;
shrLog("histogram64() time (average) : %.5f sec, %.4f MB/sec\n\n", dAvgSecs, ((double)byteCount * 1.0e-6) / dAvgSecs);
shrLogEx(LOGBOTH | MASTER, 0, "histogram64, Throughput = %.4f MB/s, Time = %.5f s, Size = %u Bytes, NumDevsUsed = %u, Workgroup = %u\n",
(1.0e-6 * (double)byteCount / dAvgSecs), dAvgSecs, byteCount, 1, HISTOGRAM64_THREADBLOCK_SIZE);
shrLog("\nValidating GPU results...\n");
shrLog(" ...reading back GPU results\n");
cutilSafeCall( cudaMemcpy(h_HistogramGPU, d_Histogram, HISTOGRAM64_BIN_COUNT * sizeof(uint), cudaMemcpyDeviceToHost) );
shrLog(" ...histogram64CPU()\n");
histogram64CPU(
h_HistogramCPU,
h_Data,
byteCount
);
shrLog(" ...comparing the results...\n");
for(uint i = 0; i < HISTOGRAM64_BIN_COUNT; i++)
if(h_HistogramGPU[i] != h_HistogramCPU[i]) PassFailFlag = 0;
shrLog(PassFailFlag ? " ...64-bin histograms match\n\n" : " ***64-bin histograms do not match!!!***\n\n" );
shrLog("Shutting down 64-bin histogram...\n\n\n");
closeHistogram64();
}
{
shrLog("Initializing 256-bin histogram...\n");
initHistogram256();
shrLog("Running 256-bin GPU histogram for %u bytes (%u runs)...\n\n", byteCount, numRuns);
for(int iter = -1; iter < numRuns; iter++){
//iter == -1 -- warmup iteration
if(iter == 0){
cutilSafeCall( cutilDeviceSynchronize() );
cutilCheckError( cutResetTimer(hTimer) );
cutilCheckError( cutStartTimer(hTimer) );
}
histogram256(d_Histogram, d_Data, byteCount);
}
cutilSafeCall( cutilDeviceSynchronize() );
cutilCheckError( cutStopTimer(hTimer));
double dAvgSecs = 1.0e-3 * (double)cutGetTimerValue(hTimer) / (double)numRuns;
shrLog("histogram256() time (average) : %.5f sec, %.4f MB/sec\n\n", dAvgSecs, ((double)byteCount * 1.0e-6) / dAvgSecs);
shrLogEx(LOGBOTH | MASTER, 0, "histogram256, Throughput = %.4f MB/s, Time = %.5f s, Size = %u Bytes, NumDevsUsed = %u, Workgroup = %u\n",
(1.0e-6 * (double)byteCount / dAvgSecs), dAvgSecs, byteCount, 1, HISTOGRAM256_THREADBLOCK_SIZE);
shrLog("\nValidating GPU results...\n");
shrLog(" ...reading back GPU results\n");
cutilSafeCall( cudaMemcpy(h_HistogramGPU, d_Histogram, HISTOGRAM256_BIN_COUNT * sizeof(uint), cudaMemcpyDeviceToHost) );
shrLog(" ...histogram256CPU()\n");
histogram256CPU(
h_HistogramCPU,
h_Data,
byteCount
);
shrLog(" ...comparing the results\n");
for(uint i = 0; i < HISTOGRAM256_BIN_COUNT; i++)
if(h_HistogramGPU[i] != h_HistogramCPU[i]) PassFailFlag = 0;
shrLog(PassFailFlag ? " ...256-bin histograms match\n\n" : " ***256-bin histograms do not match!!!***\n\n" );
shrLog("Shutting down 256-bin histogram...\n\n\n");
closeHistogram256();
}
shrLog("Shutting down...\n");
cutilCheckError(cutDeleteTimer(hTimer));
cutilSafeCall( cudaFree(d_Histogram) );
cutilSafeCall( cudaFree(d_Data) );
free(h_HistogramGPU);
free(h_HistogramCPU);
free(h_Data);
cutilDeviceReset();
shrLog("%s - Test Summary\n", sSDKsample);
// pass or fail (for both 64 bit and 256 bit histograms)
shrQAFinishExit(argc, (const char **)argv, (PassFailFlag ? QA_PASSED : QA_FAILED));
}
int main(int argc, char **argv)
{
GpuProfiling::initProf();
// Start logs
shrSetLogFileName ("scan.txt");
shrLog("%s Starting...\n\n", argv[0]);
//Use command-line specified CUDA device, otherwise use device with highest Gflops/s
if( cutCheckCmdLineFlag(argc, (const char**)argv, "device") )
cutilDeviceInit(argc, argv);
else
cudaSetDevice( cutGetMaxGflopsDeviceId() );
uint *d_Input, *d_Output;
uint *h_Input, *h_OutputCPU, *h_OutputGPU;
uint hTimer;
const uint N = 13 * 1048576 / 2;
shrLog("Allocating and initializing host arrays...\n");
cutCreateTimer(&hTimer);
h_Input = (uint *)malloc(N * sizeof(uint));
h_OutputCPU = (uint *)malloc(N * sizeof(uint));
h_OutputGPU = (uint *)malloc(N * sizeof(uint));
srand(2009);
for(uint i = 0; i < N; i++)
h_Input[i] = rand();
shrLog("Allocating and initializing CUDA arrays...\n");
cutilSafeCall( cudaMalloc((void **)&d_Input, N * sizeof(uint)) );
cutilSafeCall( cudaMalloc((void **)&d_Output, N * sizeof(uint)) );
cutilSafeCall( cudaMemcpy(d_Input, h_Input, N * sizeof(uint), cudaMemcpyHostToDevice) );
shrLog("Initializing CUDA-C scan...\n\n");
initScan();
int globalFlag = 1;
size_t szWorkgroup;
const int iCycles = 100;
shrLog("*** Running GPU scan for short arrays (%d identical iterations)...\n\n", iCycles);
for(uint arrayLength = MIN_SHORT_ARRAY_SIZE; arrayLength <= MAX_SHORT_ARRAY_SIZE; arrayLength <<= 1){
shrLog("Running scan for %u elements (%u arrays)...\n", arrayLength, N / arrayLength);
cutilSafeCall( cudaThreadSynchronize() );
cutResetTimer(hTimer);
cutStartTimer(hTimer);
for(int i = 0; i < iCycles; i++)
{
szWorkgroup = scanExclusiveShort(d_Output, d_Input, N / arrayLength, arrayLength);
}
cutilSafeCall( cudaThreadSynchronize());
cutStopTimer(hTimer);
double timerValue = 1.0e-3 * cutGetTimerValue(hTimer) / iCycles;
shrLog("Validating the results...\n");
shrLog("...reading back GPU results\n");
cutilSafeCall( cudaMemcpy(h_OutputGPU, d_Output, N * sizeof(uint), cudaMemcpyDeviceToHost) );
shrLog(" ...scanExclusiveHost()\n");
scanExclusiveHost(h_OutputCPU, h_Input, N / arrayLength, arrayLength);
// Compare GPU results with CPU results and accumulate error for this test
shrLog(" ...comparing the results\n");
int localFlag = 1;
for(uint i = 0; i < N; i++)
{
if(h_OutputCPU[i] != h_OutputGPU[i])
{
localFlag = 0;
break;
}
}
// Log message on individual test result, then accumulate to global flag
shrLog(" ...Results %s\n\n", (localFlag == 1) ? "Match" : "DON'T Match !!!");
globalFlag = globalFlag && localFlag;
// Data log
if (arrayLength == MAX_SHORT_ARRAY_SIZE)
{
shrLog("\n");
shrLogEx(LOGBOTH | MASTER, 0, "scan-Short, Throughput = %.4f MElements/s, Time = %.5f s, Size = %u Elements, NumDevsUsed = %u, Workgroup = %u\n",
(1.0e-6 * (double)arrayLength/timerValue), timerValue, arrayLength, 1, szWorkgroup);
shrLog("\n");
}
}
shrLog("***Running GPU scan for large arrays (%u identical iterations)...\n\n", iCycles);
for(uint arrayLength = MIN_LARGE_ARRAY_SIZE; arrayLength <= MAX_LARGE_ARRAY_SIZE; arrayLength <<= 1){
shrLog("Running scan for %u elements (%u arrays)...\n", arrayLength, N / arrayLength);
cutilSafeCall( cudaThreadSynchronize() );
cutResetTimer(hTimer);
cutStartTimer(hTimer);
for(int i = 0; i < iCycles; i++)
{
szWorkgroup = scanExclusiveLarge(d_Output, d_Input, N / arrayLength, arrayLength);
}
cutilSafeCall( cudaThreadSynchronize() );
cutStopTimer(hTimer);
double timerValue = 1.0e-3 * cutGetTimerValue(hTimer) / iCycles;
shrLog("Validating the results...\n");
shrLog("...reading back GPU results\n");
cutilSafeCall( cudaMemcpy(h_OutputGPU, d_Output, N * sizeof(uint), cudaMemcpyDeviceToHost) );
shrLog("...scanExclusiveHost()\n");
scanExclusiveHost(h_OutputCPU, h_Input, N / arrayLength, arrayLength);
// Compare GPU results with CPU results and accumulate error for this test
shrLog(" ...comparing the results\n");
int localFlag = 1;
for(uint i = 0; i < N; i++)
{
if(h_OutputCPU[i] != h_OutputGPU[i])
{
localFlag = 0;
break;
}
}
// Log message on individual test result, then accumulate to global flag
shrLog(" ...Results %s\n\n", (localFlag == 1) ? "Match" : "DON'T Match !!!");
globalFlag = globalFlag && localFlag;
// Data log
if (arrayLength == MAX_LARGE_ARRAY_SIZE)
{
shrLog("\n");
shrLogEx(LOGBOTH | MASTER, 0, "scan-Large, Throughput = %.4f MElements/s, Time = %.5f s, Size = %u Elements, NumDevsUsed = %u, Workgroup = %u\n",
(1.0e-6 * (double)arrayLength/timerValue), timerValue, arrayLength, 1, szWorkgroup);
shrLog("\n");
}
}
// pass or fail (cumulative... all tests in the loop)
shrLog(globalFlag ? "PASSED\n\n" : "FAILED\n\n");
GpuProfiling::printResults();
shrLog("Shutting down...\n");
closeScan();
cutilSafeCall( cudaFree(d_Output));
cutilSafeCall( cudaFree(d_Input));
cutilCheckError( cutDeleteTimer(hTimer) );
cudaThreadExit();
exit(0);
shrEXIT(argc, (const char**)argv);
}
////////////////////////////////////////////////////////////////////////////////
// Main program
////////////////////////////////////////////////////////////////////////////////
int main(int argc, char **argv){
const unsigned int OPT_N_MAX = 512;
unsigned int useDoublePrecision;
printf("[binomialOptions]\n");
int devID = cutilDeviceInit(argc, argv);
if (devID < 0) {
printf("exiting...\n");
cutilExit(argc, argv);
exit(0);
}
cutilSafeCall(cudaGetDevice(&devID));
cudaDeviceProp deviceProp;
cutilSafeCall(cudaGetDeviceProperties(&deviceProp, devID));
char *precisionChoice;
cutGetCmdLineArgumentstr(argc, (const char **)argv, "type", &precisionChoice);
if(precisionChoice == NULL) {
useDoublePrecision = 0;
} else {
if(!strcasecmp(precisionChoice, "double"))
useDoublePrecision = 1;
else
useDoublePrecision = 0;
}
printf(useDoublePrecision ? "Using double precision...\n" : "Using single precision...\n");
const int OPT_N = deviceEmulation() ? 1 : OPT_N_MAX;
TOptionData optionData[OPT_N_MAX];
float
callValueBS[OPT_N_MAX],
callValueGPU[OPT_N_MAX],
callValueCPU[OPT_N_MAX];
double
sumDelta, sumRef, gpuTime, errorVal;
unsigned int hTimer;
int i;
cutilCheckError( cutCreateTimer(&hTimer) );
int version = deviceProp.major * 10 + deviceProp.minor;
if(useDoublePrecision && version < 13){
printf("Double precision is not supported.\n");
return 0;
}
printf("Generating input data...\n");
//Generate options set
srand(123);
for(i = 0; i < OPT_N; i++){
optionData[i].S = randData(5.0f, 30.0f);
optionData[i].X = randData(1.0f, 100.0f);
optionData[i].T = randData(0.25f, 10.0f);
optionData[i].R = 0.06f;
optionData[i].V = 0.10f;
BlackScholesCall(callValueBS[i], optionData[i]);
}
printf("Running GPU binomial tree...\n");
cutilSafeCall( cudaThreadSynchronize() );
cutilCheckError( cutResetTimer(hTimer) );
cutilCheckError( cutStartTimer(hTimer) );
if(useDoublePrecision)
binomialOptions_SM13(callValueGPU, optionData, OPT_N);
else
binomialOptions_SM10(callValueGPU, optionData, OPT_N);
cutilSafeCall( cudaThreadSynchronize() );
cutilCheckError( cutStopTimer(hTimer) );
gpuTime = cutGetTimerValue(hTimer);
printf("Options count : %i \n", OPT_N);
printf("Time steps : %i \n", NUM_STEPS);
printf("binomialOptionsGPU() time: %f msec\n", gpuTime);
printf("Options per second : %f \n", OPT_N / (gpuTime * 0.001));
printf("Running CPU binomial tree...\n");
for(i = 0; i < OPT_N; i++)
binomialOptionsCPU(callValueCPU[i], optionData[i]);
printf("Comparing the results...\n");
sumDelta = 0;
sumRef = 0;
printf("GPU binomial vs. Black-Scholes\n");
for(i = 0; i < OPT_N; i++){
sumDelta += fabs(callValueBS[i] - callValueGPU[i]);
sumRef += fabs(callValueBS[i]);
}
if(sumRef >1E-5)
printf("L1 norm: %E\n", sumDelta / sumRef);
else
printf("Avg. diff: %E\n", sumDelta / (double)OPT_N);
printf("CPU binomial vs. Black-Scholes\n");
sumDelta = 0;
sumRef = 0;
for(i = 0; i < OPT_N; i++){
sumDelta += fabs(callValueBS[i]- callValueCPU[i]);
sumRef += fabs(callValueBS[i]);
}
if(sumRef >1E-5)
printf("L1 norm: %E\n", sumDelta / sumRef);
else
printf("Avg. diff: %E\n", sumDelta / (double)OPT_N);
printf("CPU binomial vs. GPU binomial\n");
sumDelta = 0;
sumRef = 0;
for(i = 0; i < OPT_N; i++){
sumDelta += fabs(callValueGPU[i] - callValueCPU[i]);
sumRef += callValueCPU[i];
}
if(sumRef > 1E-5)
printf("L1 norm: %E\n", errorVal = sumDelta / sumRef);
else
printf("Avg. diff: %E\n", errorVal = sumDelta / (double)OPT_N);
printf("Shutting down...\n");
printf("\n[binomialOptions] - Test Summary:\n");
printf((errorVal < 5e-4) ? "PASSED\n" : "FAILED\n");
cutilCheckError( cutDeleteTimer(hTimer) );
cudaThreadExit();
cutilExit(argc, argv);
}
////////////////////////////////////////////////////////////////////////////////
// Test driver
////////////////////////////////////////////////////////////////////////////////
int main(int argc, char** argv){
uint
*h_SrcKey, *h_SrcVal, *h_DstKey, *h_DstVal;
uint
*d_SrcKey, *d_SrcVal, *d_BufKey, *d_BufVal, *d_DstKey, *d_DstVal;
uint hTimer;
const uint N = 4 * 1048576;
const uint DIR = 1;
const uint numValues = 65536;
printf("Allocating and initializing host arrays...\n\n");
cutCreateTimer(&hTimer);
h_SrcKey = (uint *)malloc(N * sizeof(uint));
h_SrcVal = (uint *)malloc(N * sizeof(uint));
h_DstKey = (uint *)malloc(N * sizeof(uint));
h_DstVal = (uint *)malloc(N * sizeof(uint));
srand(2009);
for(uint i = 0; i < N; i++)
h_SrcKey[i] = rand() % numValues;
fillValues(h_SrcVal, N);
printf("Allocating and initializing CUDA arrays...\n\n");
cutilSafeCall( cudaMalloc((void **)&d_DstKey, N * sizeof(uint)) );
cutilSafeCall( cudaMalloc((void **)&d_DstVal, N * sizeof(uint)) );
cutilSafeCall( cudaMalloc((void **)&d_BufKey, N * sizeof(uint)) );
cutilSafeCall( cudaMalloc((void **)&d_BufVal, N * sizeof(uint)) );
cutilSafeCall( cudaMalloc((void **)&d_SrcKey, N * sizeof(uint)) );
cutilSafeCall( cudaMalloc((void **)&d_SrcVal, N * sizeof(uint)) );
cutilSafeCall( cudaMemcpy(d_SrcKey, h_SrcKey, N * sizeof(uint), cudaMemcpyHostToDevice) );
cutilSafeCall( cudaMemcpy(d_SrcVal, h_SrcVal, N * sizeof(uint), cudaMemcpyHostToDevice) );
printf("Initializing GPU merge sort...\n");
initMergeSort();
printf("Running GPU merge sort...\n");
cutilSafeCall( cudaThreadSynchronize() );
cutResetTimer(hTimer);
cutStartTimer(hTimer);
mergeSort(
d_DstKey,
d_DstVal,
d_BufKey,
d_BufVal,
d_SrcKey,
d_SrcVal,
N,
DIR
);
cutilSafeCall( cudaThreadSynchronize() );
cutStopTimer(hTimer);
printf("Time: %f ms\n", cutGetTimerValue(hTimer));
printf("Reading back GPU merge sort results...\n");
cutilSafeCall( cudaMemcpy(h_DstKey, d_DstKey, N * sizeof(uint), cudaMemcpyDeviceToHost) );
cutilSafeCall( cudaMemcpy(h_DstVal, d_DstVal, N * sizeof(uint), cudaMemcpyDeviceToHost) );
printf("Inspecting the results...\n");
uint keysFlag = validateSortedKeys(
h_DstKey,
h_SrcKey,
1,
N,
numValues,
DIR
);
uint valuesFlag = validateSortedValues(
h_DstKey,
h_DstVal,
h_SrcKey,
1,
N
);
printf( (keysFlag && valuesFlag) ? "TEST PASSED\n" : "TEST FAILED\n");
printf("Shutting down...\n");
closeMergeSort();
cutilCheckError( cutDeleteTimer(hTimer) );
cutilSafeCall( cudaFree(d_SrcVal) );
cutilSafeCall( cudaFree(d_SrcKey) );
cutilSafeCall( cudaFree(d_BufVal) );
cutilSafeCall( cudaFree(d_BufKey) );
cutilSafeCall( cudaFree(d_DstVal) );
cutilSafeCall( cudaFree(d_DstKey) );
free(h_DstVal);
free(h_DstKey);
free(h_SrcVal);
free(h_SrcKey);
cudaThreadExit();
cutilExit(argc, argv);
}
void shutDown(unsigned char k, int /*x*/, int /*y*/)
{
switch (k){
case '\033':
case 'q':
case 'Q':
printf("Shutting down...\n");
cutilCheckError( cutStopTimer(hTimer) );
cutilCheckError( cutDeleteTimer(hTimer) );
// DEPRECATED: cutilSafeCall( cudaGLRegisterBufferObject(gl_PBO) );
cutilSafeCall(cudaGraphicsGLRegisterBuffer(&cuda_pbo_resource, gl_PBO,
cudaGraphicsMapFlagsWriteDiscard));
glBindBuffer(GL_PIXEL_UNPACK_BUFFER_ARB, 0);
glDeleteBuffers(1, &gl_PBO);
glDeleteTextures(1, &gl_Tex);
cutilSafeCall( CUDA_FreeArray() );
free(h_Src);
printf("Shutdown done.\n");
cutilDeviceReset();
exit(0);
break;
case '1':
printf("Passthrough.\n");
g_Kernel = 0;
break;
case '2':
printf("KNN method \n");
g_Kernel = 1;
break;
case '3':
printf("NLM method\n");
g_Kernel = 2;
break;
case '4':
printf("Quick NLM(NLM2) method\n");
g_Kernel = 3;
break;
case ' ':
printf(g_Diag ? "LERP highlighting mode.\n" : "Normal mode.\n");
g_Diag = !g_Diag;
break;
case 'n':
printf("Decrease noise level.\n");
knnNoise -= noiseStep;
nlmNoise -= noiseStep;
break;
case 'N':
printf("Increase noise level.\n");
knnNoise += noiseStep;
nlmNoise += noiseStep;
break;
case 'l':
printf("Decrease LERP quotent.\n");
lerpC = MAX(lerpC - lerpStep, 0.0f);
break;
case 'L':
printf("Increase LERP quotent.\n");
lerpC = MIN(lerpC + lerpStep, 1.0f);
break;
case 'f' : case 'F':
g_FPS = true;
break;
case '?':
printf("lerpC = %5.5f\n", lerpC);
printf("knnNoise = %5.5f\n", knnNoise);
printf("nlmNoise = %5.5f\n", nlmNoise);
break;
}
}
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);
}
void shmoo(int minN, int maxN, int maxThreads, int maxBlocks, ReduceType datatype)
{
fprintf(stderr, "Shmoo wasn't implemented in this modified kernel!\n");
exit(1);
// create random input data on CPU
unsigned int bytes = maxN * sizeof(T);
T *h_idata = (T*) malloc(bytes);
for(int i = 0; i < maxN; i++) {
// Keep the numbers small so we don't get truncation error in the sum
if (datatype == REDUCE_INT)
h_idata[i] = (T)(rand() & 0xFF);
else
h_idata[i] = (rand() & 0xFF) / (T)RAND_MAX;
}
int maxNumBlocks = MIN( maxN / maxThreads, MAX_BLOCK_DIM_SIZE);
// allocate mem for the result on host side
T* h_odata = (T*) malloc(maxNumBlocks*sizeof(T));
// allocate device memory and data
T* d_idata = NULL;
T* d_odata = NULL;
cutilSafeCallNoSync( cudaMalloc((void**) &d_idata, bytes) );
cutilSafeCallNoSync( cudaMalloc((void**) &d_odata, maxNumBlocks*sizeof(T)) );
// copy data directly to device memory
cutilSafeCallNoSync( cudaMemcpy(d_idata, h_idata, bytes, cudaMemcpyHostToDevice) );
cutilSafeCallNoSync( cudaMemcpy(d_odata, h_idata, maxNumBlocks*sizeof(T), cudaMemcpyHostToDevice) );
// warm-up
for (int kernel = 0; kernel < 7; kernel++)
{
sumreduce<T>(maxN, maxThreads, maxNumBlocks, kernel, d_idata, d_odata);
}
int testIterations = 100;
unsigned int timer = 0;
cutilCheckError( cutCreateTimer( &timer));
// print headers
shrLog("Time in milliseconds for various numbers of elements for each kernel\n\n\n");
shrLog("Kernel");
for (int i = minN; i <= maxN; i *= 2)
{
shrLog(", %d", i);
}
for (int kernel = 0; kernel < 7; kernel++)
{
shrLog("\n%d", kernel);
for (int i = minN; i <= maxN; i *= 2)
{
cutResetTimer(timer);
int numBlocks = 0;
int numThreads = 0;
getNumBlocksAndThreads(kernel, i, maxBlocks, maxThreads, numBlocks, numThreads);
float reduceTime;
if( numBlocks <= MAX_BLOCK_DIM_SIZE ) {
benchmarkReduceSum(i, numThreads, numBlocks, maxThreads, maxBlocks, kernel,
testIterations, false, 1, timer, h_odata, d_idata, d_odata);
reduceTime = cutGetAverageTimerValue(timer);
} else {
reduceTime = -1.0;
}
shrLog(", %.5f", reduceTime);
}
}
// cleanup
cutilCheckError(cutDeleteTimer(timer));
free(h_idata);
free(h_odata);
cutilSafeCallNoSync(cudaFree(d_idata));
cutilSafeCallNoSync(cudaFree(d_odata));
}
//////////////////////////////////////////////////////////////////////////////
// Program main
//////////////////////////////////////////////////////////////////////////////
int
main( int argc, char** argv)
{
printf("Run \"nbody -benchmark [-n=<numBodies>]\" to measure perfomance.\n\n");
bool benchmark =
(cutCheckCmdLineFlag(argc, (const char**) argv, "benchmark") != 0);
bool compareToCPU =
((cutCheckCmdLineFlag(argc, (const char**) argv, "compare") != 0) ||
!(cutCheckCmdLineFlag(argc, (const char**) argv, "noqatest") != 0));
bool regression =
(cutCheckCmdLineFlag(argc, (const char**) argv, "regression") != 0);
int devID;
cudaDeviceProp props;
// nBody has a mode that allows it to be run without using GL interop
if (benchmark || compareToCPU || regression) {
/*
if( cutCheckCmdLineFlag(argc, (const char**)argv, "device") ) {
cutilDeviceInit(argc, argv);
} else {
devID = cutGetMaxGflopsDeviceId();
cudaSetDevice( devID );
} */
}
else
{
// This mode shows the OpenGL results rendered
// First initialize OpenGL context, so we can properly set the GL for CUDA.
// This is necessary in order to achieve optimal performance with OpenGL/CUDA interop.
glutInit(&argc, argv);
glutInitDisplayMode(GLUT_RGB | GLUT_DEPTH | GLUT_DOUBLE);
glutInitWindowSize(720, 480);
glutCreateWindow("CUDA n-body system");
GLenum err = glewInit();
if (GLEW_OK != err)
{
printf("GLEW Error: %s\n", glewGetErrorString(err));
}
else
{
#if defined(WIN32)
wglSwapIntervalEXT(0);
#elif defined(LINUX)
glxSwapIntervalSGI(0);
#endif
}
initGL();
initParameters();
if( cutCheckCmdLineFlag(argc, (const char**)argv, "device") ) {
cutilGLDeviceInit(argc, argv);
} else {
devID = cutGetMaxGflopsDeviceId();
cudaGLSetGLDevice( devID );
}
}
// get number of SMs on this GPU
cutilSafeCall(cudaGetDevice(&devID));
cutilSafeCall(cudaGetDeviceProperties(&props, devID));
numIterations = 0;
int p = 256;
int q = 1;
cutGetCmdLineArgumenti(argc, (const char**) argv, "i", &numIterations);
cutGetCmdLineArgumenti(argc, (const char**) argv, "p", &p);
cutGetCmdLineArgumenti(argc, (const char**) argv, "q", &q);
// default number of bodies is #SMs * 4 * CTA size
numBodies = compareToCPU ? 4096 : p*q*4*props.multiProcessorCount;
cutGetCmdLineArgumenti(argc, (const char**) argv, "n", &numBodies);
switch (numBodies)
{
case 1024:
activeParams.m_clusterScale = 1.52f;
activeParams.m_velocityScale = 2.f;
break;
case 2048:
activeParams.m_clusterScale = 1.56f;
activeParams.m_velocityScale = 2.64f;
break;
case 4096:
activeParams.m_clusterScale = 1.68f;
activeParams.m_velocityScale = 2.98f;
break;
case 8192:
activeParams.m_clusterScale = 1.98f;
activeParams.m_velocityScale = 2.9f;
break;
default:
case 16384:
activeParams.m_clusterScale = 1.54f;
activeParams.m_velocityScale = 8.f;
break;
case 32768:
activeParams.m_clusterScale = 1.44f;
activeParams.m_velocityScale = 11.f;
break;
}
if (q * p > 256)
{
p = 256 / q;
printf("Setting p=%d, q=%d to maintain %d threads per block\n", p, q, 256);
}
if (q == 1 && numBodies < p)
{
p = numBodies;
}
init(numBodies, p, q, !(benchmark || compareToCPU));
reset(nbody, numBodies, NBODY_CONFIG_SHELL, !(benchmark || compareToCPU));
if (benchmark)
{
if (numIterations <= 0)
numIterations = 100;
runBenchmark(numIterations);
}
else if (compareToCPU || regression)
{
compareResults(regression, numBodies);
}
else
{
glutDisplayFunc(display);
glutReshapeFunc(reshape);
glutMouseFunc(mouse);
glutMotionFunc(motion);
glutKeyboardFunc(key);
glutSpecialFunc(special);
glutIdleFunc(idle);
cutilSafeCall(cudaEventRecord(startEvent, 0));
glutMainLoop();
}
if (nbodyCPU)
delete nbodyCPU;
if (nbodyCUDA)
delete nbodyCUDA;
if (hPos)
delete [] hPos;
if (hVel)
delete [] hVel;
if (hColor)
delete [] hColor;
cutilSafeCall(cudaEventDestroy(startEvent));
cutilSafeCall(cudaEventDestroy(stopEvent));
cutilCheckError(cutDeleteTimer(demoTimer));
return 0;
}
int main(int argc, char **argv)
{
// Start logs
shrSetLogFileName ("quasirandomGenerator.txt");
shrLog("%s Starting...\n\n", argv[0]);
unsigned int useDoublePrecision;
char *precisionChoice;
cutGetCmdLineArgumentstr(argc, (const char **)argv, "type", &precisionChoice);
if(precisionChoice == NULL)
useDoublePrecision = 0;
else{
if(!strcasecmp(precisionChoice, "double"))
useDoublePrecision = 1;
else
useDoublePrecision = 0;
}
unsigned int tableCPU[QRNG_DIMENSIONS][QRNG_RESOLUTION];
float
*h_OutputGPU;
float
*d_Output;
int
dim, pos;
double
delta, ref, sumDelta, sumRef, L1norm, gpuTime;
unsigned int hTimer;
if(sizeof(INT64) != 8){
shrLog("sizeof(INT64) != 8\n");
return 0;
}
// use command-line specified CUDA device, otherwise use device with highest Gflops/s
if( cutCheckCmdLineFlag(argc, (const char**)argv, "device") )
cutilDeviceInit(argc, argv);
else
cudaSetDevice( cutGetMaxGflopsDeviceId() );
cutilCheckError(cutCreateTimer(&hTimer));
int deviceIndex;
cutilSafeCall(cudaGetDevice(&deviceIndex));
cudaDeviceProp deviceProp;
cutilSafeCall(cudaGetDeviceProperties(&deviceProp, deviceIndex));
int version = deviceProp.major * 10 + deviceProp.minor;
if(useDoublePrecision && version < 13){
shrLog("Double precision not supported.\n");
cudaThreadExit();
return 0;
}
shrLog("Allocating GPU memory...\n");
cutilSafeCall( cudaMalloc((void **)&d_Output, QRNG_DIMENSIONS * N * sizeof(float)) );
shrLog("Allocating CPU memory...\n");
h_OutputGPU = (float *)malloc(QRNG_DIMENSIONS * N * sizeof(float));
shrLog("Initializing QRNG tables...\n\n");
initQuasirandomGenerator(tableCPU);
if(useDoublePrecision)
initTable_SM13(tableCPU);
else
initTable_SM10(tableCPU);
shrLog("Testing QRNG...\n\n");
cutilSafeCall( cudaMemset(d_Output, 0, QRNG_DIMENSIONS * N * sizeof(float)) );
int numIterations = 20;
for (int i = -1; i < numIterations; i++)
{
if (i == 0)
{
cutilSafeCall( cudaThreadSynchronize() );
cutilCheckError( cutResetTimer(hTimer) );
cutilCheckError( cutStartTimer(hTimer) );
}
if(useDoublePrecision)
quasirandomGenerator_SM13(d_Output, 0, N);
else
quasirandomGenerator_SM10(d_Output, 0, N);
}
cutilSafeCall( cudaThreadSynchronize() );
cutilCheckError(cutStopTimer(hTimer));
gpuTime = cutGetTimerValue(hTimer)/(double)numIterations*1e-3;
shrLogEx(LOGBOTH | MASTER, 0, "quasirandomGenerator, Throughput = %.4f GNumbers/s, Time = %.5f s, Size = %u Numbers, NumDevsUsed = %u, Workgroup = %u\n",
(double)QRNG_DIMENSIONS * (double)N * 1.0E-9 / gpuTime, gpuTime, QRNG_DIMENSIONS*N, 1, 128*QRNG_DIMENSIONS);
shrLog("\nReading GPU results...\n");
cutilSafeCall( cudaMemcpy(h_OutputGPU, d_Output, QRNG_DIMENSIONS * N * sizeof(float), cudaMemcpyDeviceToHost) );
shrLog("Comparing to the CPU results...\n\n");
sumDelta = 0;
sumRef = 0;
for(dim = 0; dim < QRNG_DIMENSIONS; dim++)
for(pos = 0; pos < N; pos++){
ref = getQuasirandomValue63(pos, dim);
delta = (double)h_OutputGPU[dim * N + pos] - ref;
sumDelta += fabs(delta);
sumRef += fabs(ref);
}
shrLog("L1 norm: %E\n", sumDelta / sumRef);
shrLog("\nTesting inverseCNDgpu()...\n\n");
cutilSafeCall( cudaMemset(d_Output, 0, QRNG_DIMENSIONS * N * sizeof(float)) );
for (int i = -1; i < numIterations; i++)
{
if (i == 0)
{
cutilSafeCall( cudaThreadSynchronize() );
cutilCheckError( cutResetTimer(hTimer) );
cutilCheckError( cutStartTimer(hTimer) );
}
if(useDoublePrecision)
inverseCND_SM13(d_Output, NULL, QRNG_DIMENSIONS * N);
else
inverseCND_SM10(d_Output, NULL, QRNG_DIMENSIONS * N);
}
cutilSafeCall( cudaThreadSynchronize() );
cutilCheckError(cutStopTimer(hTimer));
gpuTime = cutGetTimerValue(hTimer)/(double)numIterations*1e-3;
shrLogEx(LOGBOTH | MASTER, 0, "quasirandomGenerator-inverse, Throughput = %.4f GNumbers/s, Time = %.5f s, Size = %u Numbers, NumDevsUsed = %u, Workgroup = %u\n",
(double)QRNG_DIMENSIONS * (double)N * 1E-9 / gpuTime, gpuTime, QRNG_DIMENSIONS*N, 1, 128);
shrLog("Reading GPU results...\n");
cutilSafeCall( cudaMemcpy(h_OutputGPU, d_Output, QRNG_DIMENSIONS * N * sizeof(float), cudaMemcpyDeviceToHost) );
shrLog("\nComparing to the CPU results...\n");
sumDelta = 0;
sumRef = 0;
for(pos = 0; pos < QRNG_DIMENSIONS * N; pos++){
double p = (double)(pos + 1) / (double)(QRNG_DIMENSIONS * N + 1);
ref = MoroInvCNDcpu(p);
delta = (double)h_OutputGPU[pos] - ref;
sumDelta += fabs(delta);
sumRef += fabs(ref);
}
shrLog("L1 norm: %E\n\n", L1norm = sumDelta / sumRef);
shrLog((L1norm < 1E-6) ? "PASSED\n\n" : "FAILED\n\n");
shrLog("Shutting down...\n");
cutilCheckError(cutDeleteTimer(hTimer));
free(h_OutputGPU);
cutilSafeCall( cudaFree(d_Output) );
cudaThreadExit();
shrEXIT(argc, (const char**)argv);
}
bool
runTestMax( int argc, char** argv, ReduceType datatype)
{
int size = 1<<24; // number of elements to reduce
int maxThreads = 256; // number of threads per block
int whichKernel = 6;
int maxBlocks = 64;
bool cpuFinalReduction = false;
int cpuFinalThreshold = 1;
cutGetCmdLineArgumenti( argc, (const char**) argv, "n", &size);
cutGetCmdLineArgumenti( argc, (const char**) argv, "threads", &maxThreads);
cutGetCmdLineArgumenti( argc, (const char**) argv, "kernel", &whichKernel);
cutGetCmdLineArgumenti( argc, (const char**) argv, "maxblocks", &maxBlocks);
shrLog("METHOD: MAX\n");
shrLog("%d elements\n", size);
shrLog("%d threads (max)\n", maxThreads);
cpuFinalReduction = (cutCheckCmdLineFlag( argc, (const char**) argv, "cpufinal") == CUTTrue);
cutGetCmdLineArgumenti( argc, (const char**) argv, "cputhresh", &cpuFinalThreshold);
bool runShmoo = (cutCheckCmdLineFlag(argc, (const char**) argv, "shmoo") == CUTTrue);
if (runShmoo)
{
shmoo<T>(1, 33554432, maxThreads, maxBlocks, datatype);
}
else
{
// create random input data on CPU
unsigned int bytes = size * sizeof(T);
T *h_idata = (T *) malloc(bytes);
for(int i=0; i<size; i++)
{
// Keep the numbers small so we don't get truncation error in the sum
if (datatype == REDUCE_INT)
h_idata[i] = (T)(rand() & 0xFF);
else
h_idata[i] = (rand() & 0xFF) / (T)RAND_MAX;
}
int numBlocks = 0;
int numThreads = 0;
getNumBlocksAndThreads(whichKernel, size, maxBlocks, maxThreads, numBlocks, numThreads);
if (numBlocks == 1) cpuFinalThreshold = 1;
// allocate mem for the result on host side
T* h_odata = (T*) malloc(numBlocks*sizeof(T));
shrLog("%d blocks\n\n", numBlocks);
// allocate device memory and data
T* d_idata = NULL;
T* d_odata = NULL;
cutilSafeCallNoSync( cudaMalloc((void**) &d_idata, bytes) );
cutilSafeCallNoSync( cudaMalloc((void**) &d_odata, numBlocks*sizeof(T)) );
// copy data directly to device memory
cutilSafeCallNoSync( cudaMemcpy(d_idata, h_idata, bytes, cudaMemcpyHostToDevice) );
cutilSafeCallNoSync( cudaMemcpy(d_odata, h_idata, numBlocks*sizeof(T), cudaMemcpyHostToDevice) );
// warm-up
maxreduce<T>(size, numThreads, numBlocks, whichKernel, d_idata, d_odata);
int testIterations = 100;
unsigned int timer = 0;
cutilCheckError( cutCreateTimer( &timer));
T gpu_result = 0;
gpu_result = benchmarkReduceMax<T>(size, numThreads, numBlocks, maxThreads, maxBlocks,
whichKernel, testIterations, cpuFinalReduction,
cpuFinalThreshold, timer,
h_odata, d_idata, d_odata);
double reduceTime = cutGetAverageTimerValue(timer) * 1e-3;
shrLogEx(LOGBOTH | MASTER, 0, "Reduction, Throughput = %.4f GB/s, Time = %.5f s, Size = %u Elements, NumDevsUsed = %d, Workgroup = %u\n",
1.0e-9 * ((double)bytes)/reduceTime, reduceTime, size, 1, numThreads);
// compute reference solution
T cpu_result = maxreduceCPU<T>(h_idata, size);
double threshold = 1e-12;
double diff = 0;
if (datatype == REDUCE_INT)
{
shrLog("\nGPU result = %d\n", gpu_result);
shrLog("CPU result = %d\n\n", cpu_result);
}
else
{
shrLog("\nGPU result = %f\n", gpu_result);
shrLog("CPU result = %f\n\n", cpu_result);
if (datatype == REDUCE_FLOAT)
threshold = 1e-8 * size;
diff = fabs((double)gpu_result - (double)cpu_result);
}
// cleanup
cutilCheckError( cutDeleteTimer(timer) );
free(h_idata);
free(h_odata);
cutilSafeCallNoSync(cudaFree(d_idata));
cutilSafeCallNoSync(cudaFree(d_odata));
if (datatype == REDUCE_INT) {
return (gpu_result == cpu_result);
} else {
return (diff < threshold);
}
}
return true;
}
