cuda-samples/Samples/simpleTemplates_nvrtc/simpleTemplates.cpp

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/* Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/* This sample is a templatized version of the template project.
* It also shows how to correctly templatize dynamically allocated shared
* memory arrays.
* Host code.
*/
// System includes
#include <stdio.h>
#include <assert.h>
#include <string.h>
#include <math.h>
// CUDA runtime
#include <cuda_runtime.h>
// helper functions and utilities to work with CUDA
#include <helper_functions.h>
#include <nvrtc_helper.h>
#ifndef MAX
#define MAX(a, b) (a > b ? a : b)
#endif
int g_TotalFailures = 0;
////////////////////////////////////////////////////////////////////////////////
// declaration, forward
template <class T>
void runTest(int argc, char **argv, int len);
template <class T>
void computeGold(T *reference, T *idata, const unsigned int len) {
const T T_len = static_cast<T>(len);
for (unsigned int i = 0; i < len; ++i) {
reference[i] = idata[i] * T_len;
}
}
////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////
int main(int argc, char **argv) {
printf("> runTest<float,32>\n");
runTest<float>(argc, argv, 32);
printf("> runTest<int,64>\n");
runTest<int>(argc, argv, 64);
printf("\n[simpleTemplates_nvrtc] -> Test Results: %d Failures\n",
g_TotalFailures);
exit(g_TotalFailures == 0 ? EXIT_SUCCESS : EXIT_FAILURE);
}
// To completely templatize runTest (below) with cutil, we need to use
// template specialization to wrap up CUTIL's array comparison and file writing
// functions for different types.
// Here's the generic wrapper for cutCompare*
template <class T>
class ArrayComparator {
public:
bool compare(const T *reference, T *data, unsigned int len) {
fprintf(stderr,
"Error: no comparison function implemented for this type\n");
return false;
}
};
// Here's the specialization for ints:
template <>
class ArrayComparator<int> {
public:
bool compare(const int *reference, int *data, unsigned int len) {
return compareData(reference, data, len, 0.15f, 0.0f);
}
};
// Here's the specialization for floats:
template <>
class ArrayComparator<float> {
public:
bool compare(const float *reference, float *data, unsigned int len) {
return compareData(reference, data, len, 0.15f, 0.15f);
}
};
// Here's the generic wrapper for cutWriteFile*
template <class T>
class ArrayFileWriter {
public:
bool write(const char *filename, T *data, unsigned int len, float epsilon) {
fprintf(stderr,
"Error: no file write function implemented for this type\n");
return false;
}
};
// Here's the specialization for ints:
template <>
class ArrayFileWriter<int> {
public:
bool write(const char *filename, int *data, unsigned int len, float epsilon) {
return sdkWriteFile(filename, data, len, epsilon, false);
}
};
// Here's the specialization for floats:
template <>
class ArrayFileWriter<float> {
public:
bool write(const char *filename, float *data, unsigned int len,
float epsilon) {
return sdkWriteFile(filename, data, len, epsilon, false);
}
};
template <typename T>
CUfunction getKernel(CUmodule in);
template <>
CUfunction getKernel<int>(CUmodule in) {
CUfunction kernel_addr;
checkCudaErrors(cuModuleGetFunction(&kernel_addr, in, "testInt"));
return kernel_addr;
}
template <>
CUfunction getKernel<float>(CUmodule in) {
CUfunction kernel_addr;
checkCudaErrors(cuModuleGetFunction(&kernel_addr, in, "testFloat"));
return kernel_addr;
}
////////////////////////////////////////////////////////////////////////////////
//! Run a simple test for CUDA
////////////////////////////////////////////////////////////////////////////////
static bool moduleLoaded = false;
CUmodule module;
char *cubin, *kernel_file;
size_t cubinSize;
template <class T>
void runTest(int argc, char **argv, int len) {
if (!moduleLoaded) {
kernel_file = sdkFindFilePath("simpleTemplates_kernel.cu", argv[0]);
compileFileToCUBIN(kernel_file, argc, argv, &cubin, &cubinSize, 0);
module = loadCUBIN(cubin, argc, argv);
moduleLoaded = true;
}
// create and start timer
StopWatchInterface *timer = NULL;
sdkCreateTimer(&timer);
// start the timer
sdkStartTimer(&timer);
unsigned int num_threads = len;
unsigned int mem_size = sizeof(float) * num_threads;
// allocate host memory
T *h_idata = (T *)malloc(mem_size);
// initialize the memory
for (unsigned int i = 0; i < num_threads; ++i) {
h_idata[i] = (T)i;
}
// allocate device memory
CUdeviceptr d_idata;
checkCudaErrors(cuMemAlloc(&d_idata, mem_size));
// copy host memory to device
checkCudaErrors(cuMemcpyHtoD(d_idata, h_idata, mem_size));
// allocate device memory for result
CUdeviceptr d_odata;
checkCudaErrors(cuMemAlloc(&d_odata, mem_size));
// setup execution parameters
dim3 grid(1, 1, 1);
dim3 threads(num_threads, 1, 1);
// execute the kernel
CUfunction kernel_addr = getKernel<T>(module);
void *arr[] = {(void *)&d_idata, (void *)&d_odata};
checkCudaErrors(
cuLaunchKernel(kernel_addr, grid.x, grid.y, grid.z, /* grid dim */
threads.x, threads.y, threads.z, /* block dim */
mem_size, 0, /* shared mem, stream */
&arr[0], /* arguments */
0));
// check if kernel execution generated and error
checkCudaErrors(cuCtxSynchronize());
// allocate mem for the result on host side
T *h_odata = (T *)malloc(mem_size);
// copy result from device to host
checkCudaErrors(cuMemcpyDtoH(h_odata, d_odata, sizeof(T) * num_threads));
sdkStopTimer(&timer);
printf("Processing time: %f (ms)\n", sdkGetTimerValue(&timer));
sdkDeleteTimer(&timer);
// compute reference solution
T *reference = (T *)malloc(mem_size);
computeGold<T>(reference, h_idata, num_threads);
ArrayComparator<T> comparator;
ArrayFileWriter<T> writer;
// check result
if (checkCmdLineFlag(argc, (const char **)argv, "regression")) {
// write file for regression test
writer.write("./data/regression.dat", h_odata, num_threads, 0.0f);
} else {
// custom output handling when no regression test running
// in this case check if the result is equivalent to the expected solution
bool res = comparator.compare(reference, h_odata, num_threads);
printf("Compare %s\n\n", (1 == res) ? "OK" : "MISMATCH");
g_TotalFailures += (1 != res);
}
// cleanup memory
free(h_idata);
free(h_odata);
free(reference);
checkCudaErrors(cuMemFree(d_idata));
checkCudaErrors(cuMemFree(d_odata));
}