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Apply consistent code formatting across the repo. Add clang-format and pre-commit hooks.

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This commit is contained in:
Rob Armstrong
2025-03-27 10:30:07 -07:00
parent 2cd58fbc9a
commit ceab6e8bcc
782 changed files with 107230 additions and 106548 deletions
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287
Samples/0_Introduction/fp16ScalarProduct/fp16ScalarProduct.cu
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@@ -25,191 +25,188 @@
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#include "cuda_fp16.h"
#include "helper_cuda.h"
#include <cstdio>
#include <cstdlib>
#include <ctime>
#define NUM_OF_BLOCKS 128
#include "cuda_fp16.h"
#include "helper_cuda.h"
#define NUM_OF_BLOCKS 128
#define NUM_OF_THREADS 128
__forceinline__ __device__ void reduceInShared_intrinsics(half2 *const v) {
if (threadIdx.x < 64)
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 64]);
__syncthreads();
if (threadIdx.x < 32)
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 32]);
__syncthreads();
if (threadIdx.x < 16)
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 16]);
__syncthreads();
if (threadIdx.x < 8)
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 8]);
__syncthreads();
if (threadIdx.x < 4)
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 4]);
__syncthreads();
if (threadIdx.x < 2)
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 2]);
__syncthreads();
if (threadIdx.x < 1)
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 1]);
__syncthreads();
__forceinline__ __device__ void reduceInShared_intrinsics(half2 *const v)
{
if (threadIdx.x < 64)
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 64]);
__syncthreads();
if (threadIdx.x < 32)
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 32]);
__syncthreads();
if (threadIdx.x < 16)
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 16]);
__syncthreads();
if (threadIdx.x < 8)
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 8]);
__syncthreads();
if (threadIdx.x < 4)
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 4]);
__syncthreads();
if (threadIdx.x < 2)
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 2]);
__syncthreads();
if (threadIdx.x < 1)
v[threadIdx.x] = __hadd2(v[threadIdx.x], v[threadIdx.x + 1]);
__syncthreads();
}
__forceinline__ __device__ void reduceInShared_native(half2 *const v) {
if (threadIdx.x < 64) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 64];
__syncthreads();
if (threadIdx.x < 32) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 32];
__syncthreads();
if (threadIdx.x < 16) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 16];
__syncthreads();
if (threadIdx.x < 8) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 8];
__syncthreads();
if (threadIdx.x < 4) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 4];
__syncthreads();
if (threadIdx.x < 2) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 2];
__syncthreads();
if (threadIdx.x < 1) v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 1];
__syncthreads();
__forceinline__ __device__ void reduceInShared_native(half2 *const v)
{
if (threadIdx.x < 64)
v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 64];
__syncthreads();
if (threadIdx.x < 32)
v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 32];
__syncthreads();
if (threadIdx.x < 16)
v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 16];
__syncthreads();
if (threadIdx.x < 8)
v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 8];
__syncthreads();
if (threadIdx.x < 4)
v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 4];
__syncthreads();
if (threadIdx.x < 2)
v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 2];
__syncthreads();
if (threadIdx.x < 1)
v[threadIdx.x] = v[threadIdx.x] + v[threadIdx.x + 1];
__syncthreads();
}
__global__ void scalarProductKernel_intrinsics(half2 const *const a,
half2 const *const b,
float *const results,
size_t const size) {
const int stride = gridDim.x * blockDim.x;
__shared__ half2 shArray[NUM_OF_THREADS];
__global__ void
scalarProductKernel_intrinsics(half2 const *const a, half2 const *const b, float *const results, size_t const size)
{
const int stride = gridDim.x * blockDim.x;
__shared__ half2 shArray[NUM_OF_THREADS];
shArray[threadIdx.x] = __float2half2_rn(0.f);
half2 value = __float2half2_rn(0.f);
shArray[threadIdx.x] = __float2half2_rn(0.f);
half2 value = __float2half2_rn(0.f);
for (int i = threadIdx.x + blockDim.x + blockIdx.x; i < size; i += stride) {
value = __hfma2(a[i], b[i], value);
}
for (int i = threadIdx.x + blockDim.x + blockIdx.x; i < size; i += stride) {
value = __hfma2(a[i], b[i], value);
}
shArray[threadIdx.x] = value;
__syncthreads();
reduceInShared_intrinsics(shArray);
shArray[threadIdx.x] = value;
__syncthreads();
reduceInShared_intrinsics(shArray);
if (threadIdx.x == 0) {
half2 result = shArray[0];
float f_result = __low2float(result) + __high2float(result);
results[blockIdx.x] = f_result;
}
if (threadIdx.x == 0) {
half2 result = shArray[0];
float f_result = __low2float(result) + __high2float(result);
results[blockIdx.x] = f_result;
}
}
__global__ void scalarProductKernel_native(half2 const *const a,
half2 const *const b,
float *const results,
size_t const size) {
const int stride = gridDim.x * blockDim.x;
__shared__ half2 shArray[NUM_OF_THREADS];
__global__ void
scalarProductKernel_native(half2 const *const a, half2 const *const b, float *const results, size_t const size)
{
const int stride = gridDim.x * blockDim.x;
__shared__ half2 shArray[NUM_OF_THREADS];
half2 value(0.f, 0.f);
shArray[threadIdx.x] = value;
half2 value(0.f, 0.f);
shArray[threadIdx.x] = value;
for (int i = threadIdx.x + blockDim.x + blockIdx.x; i < size; i += stride) {
value = a[i] * b[i] + value;
}
for (int i = threadIdx.x + blockDim.x + blockIdx.x; i < size; i += stride) {
value = a[i] * b[i] + value;
}
shArray[threadIdx.x] = value;
__syncthreads();
reduceInShared_native(shArray);
shArray[threadIdx.x] = value;
__syncthreads();
reduceInShared_native(shArray);
if (threadIdx.x == 0) {
half2 result = shArray[0];
float f_result = (float)result.y + (float)result.x;
results[blockIdx.x] = f_result;
}
if (threadIdx.x == 0) {
half2 result = shArray[0];
float f_result = (float)result.y + (float)result.x;
results[blockIdx.x] = f_result;
}
}
void generateInput(half2 *a, size_t size) {
for (size_t i = 0; i < size; ++i) {
half2 temp;
temp.x = static_cast<float>(rand() % 4);
temp.y = static_cast<float>(rand() % 2);
a[i] = temp;
}
void generateInput(half2 *a, size_t size)
{
for (size_t i = 0; i < size; ++i) {
half2 temp;
temp.x = static_cast<float>(rand() % 4);
temp.y = static_cast<float>(rand() % 2);
a[i] = temp;
}
}
int main(int argc, char *argv[]) {
srand((unsigned int)time(NULL));
size_t size = NUM_OF_BLOCKS * NUM_OF_THREADS * 16;
int main(int argc, char *argv[])
{
srand((unsigned int)time(NULL));
size_t size = NUM_OF_BLOCKS * NUM_OF_THREADS * 16;
half2 *vec[2];
half2 *devVec[2];
half2 *vec[2];
half2 *devVec[2];
float *results;
float *devResults;
float *results;
float *devResults;
int devID = findCudaDevice(argc, (const char **)argv);
int devID = findCudaDevice(argc, (const char **)argv);
cudaDeviceProp devProp;
checkCudaErrors(cudaGetDeviceProperties(&devProp, devID));
cudaDeviceProp devProp;
checkCudaErrors(cudaGetDeviceProperties(&devProp, devID));
if (devProp.major < 5 || (devProp.major == 5 && devProp.minor < 3)) {
printf(
"ERROR: fp16ScalarProduct requires GPU devices with compute SM 5.3 or "
"higher.\n");
return EXIT_WAIVED;
}
if (devProp.major < 5 || (devProp.major == 5 && devProp.minor < 3)) {
printf("ERROR: fp16ScalarProduct requires GPU devices with compute SM 5.3 or "
"higher.\n");
return EXIT_WAIVED;
}
for (int i = 0; i < 2; ++i) {
checkCudaErrors(cudaMallocHost((void **)&vec[i], size * sizeof *vec[i]));
checkCudaErrors(cudaMalloc((void **)&devVec[i], size * sizeof *devVec[i]));
}
for (int i = 0; i < 2; ++i) {
checkCudaErrors(cudaMallocHost((void **)&vec[i], size * sizeof *vec[i]));
checkCudaErrors(cudaMalloc((void **)&devVec[i], size * sizeof *devVec[i]));
}
checkCudaErrors(
cudaMallocHost((void **)&results, NUM_OF_BLOCKS * sizeof *results));
checkCudaErrors(
cudaMalloc((void **)&devResults, NUM_OF_BLOCKS * sizeof *devResults));
checkCudaErrors(cudaMallocHost((void **)&results, NUM_OF_BLOCKS * sizeof *results));
checkCudaErrors(cudaMalloc((void **)&devResults, NUM_OF_BLOCKS * sizeof *devResults));
for (int i = 0; i < 2; ++i) {
generateInput(vec[i], size);
checkCudaErrors(cudaMemcpy(devVec[i], vec[i], size * sizeof *vec[i],
cudaMemcpyHostToDevice));
}
for (int i = 0; i < 2; ++i) {
generateInput(vec[i], size);
checkCudaErrors(cudaMemcpy(devVec[i], vec[i], size * sizeof *vec[i], cudaMemcpyHostToDevice));
}
scalarProductKernel_native<<<NUM_OF_BLOCKS, NUM_OF_THREADS>>>(
devVec[0], devVec[1], devResults, size);
scalarProductKernel_native<<<NUM_OF_BLOCKS, NUM_OF_THREADS>>>(devVec[0], devVec[1], devResults, size);
checkCudaErrors(cudaMemcpy(results, devResults,
NUM_OF_BLOCKS * sizeof *results,
cudaMemcpyDeviceToHost));
checkCudaErrors(cudaMemcpy(results, devResults, NUM_OF_BLOCKS * sizeof *results, cudaMemcpyDeviceToHost));
float result_native = 0;
for (int i = 0; i < NUM_OF_BLOCKS; ++i) {
result_native += results[i];
}
printf("Result native operators\t: %f \n", result_native);
float result_native = 0;
for (int i = 0; i < NUM_OF_BLOCKS; ++i) {
result_native += results[i];
}
printf("Result native operators\t: %f \n", result_native);
scalarProductKernel_intrinsics<<<NUM_OF_BLOCKS, NUM_OF_THREADS>>>(
devVec[0], devVec[1], devResults, size);
scalarProductKernel_intrinsics<<<NUM_OF_BLOCKS, NUM_OF_THREADS>>>(devVec[0], devVec[1], devResults, size);
checkCudaErrors(cudaMemcpy(results, devResults,
NUM_OF_BLOCKS * sizeof *results,
cudaMemcpyDeviceToHost));
checkCudaErrors(cudaMemcpy(results, devResults, NUM_OF_BLOCKS * sizeof *results, cudaMemcpyDeviceToHost));
float result_intrinsics = 0;
for (int i = 0; i < NUM_OF_BLOCKS; ++i) {
result_intrinsics += results[i];
}
printf("Result intrinsics\t: %f \n", result_intrinsics);
float result_intrinsics = 0;
for (int i = 0; i < NUM_OF_BLOCKS; ++i) {
result_intrinsics += results[i];
}
printf("Result intrinsics\t: %f \n", result_intrinsics);
printf("&&&& fp16ScalarProduct %s\n",
(fabs(result_intrinsics - result_native) < 0.00001) ? "PASSED"
: "FAILED");
printf("&&&& fp16ScalarProduct %s\n", (fabs(result_intrinsics - result_native) < 0.00001) ? "PASSED" : "FAILED");
for (int i = 0; i < 2; ++i) {
checkCudaErrors(cudaFree(devVec[i]));
checkCudaErrors(cudaFreeHost(vec[i]));
}
for (int i = 0; i < 2; ++i) {
checkCudaErrors(cudaFree(devVec[i]));
checkCudaErrors(cudaFreeHost(vec[i]));
}
checkCudaErrors(cudaFree(devResults));
checkCudaErrors(cudaFreeHost(results));
checkCudaErrors(cudaFree(devResults));
checkCudaErrors(cudaFreeHost(results));
return EXIT_SUCCESS;
return EXIT_SUCCESS;
}