386 lines
12 KiB
Plaintext
386 lines
12 KiB
Plaintext
/*=============================================================================
|
||
*------------------------------------------------------------------------------
|
||
* Copyright 2015: Tom Deakin, Simon McIntosh-Smith, University of Bristol HPC
|
||
* Based on John D. McCalpin’s original STREAM benchmark for CPUs
|
||
*------------------------------------------------------------------------------
|
||
* License:
|
||
* 1. You are free to use this program and/or to redistribute
|
||
* this program.
|
||
* 2. You are free to modify this program for your own use,
|
||
* including commercial use, subject to the publication
|
||
* restrictions in item 3.
|
||
* 3. You are free to publish results obtained from running this
|
||
* program, or from works that you derive from this program,
|
||
* with the following limitations:
|
||
* 3a. In order to be referred to as "GPU-STREAM benchmark results",
|
||
* published results must be in conformance to the GPU-STREAM
|
||
* Run Rules published at
|
||
* http://github.com/UoB-HPC/GPU-STREAM/wiki/Run-Rules
|
||
* and incorporated herein by reference.
|
||
* The copyright holders retain the
|
||
* right to determine conformity with the Run Rules.
|
||
* 3b. Results based on modified source code or on runs not in
|
||
* accordance with the GPU-STREAM Run Rules must be clearly
|
||
* labelled whenever they are published. Examples of
|
||
* proper labelling include:
|
||
* "tuned GPU-STREAM benchmark results"
|
||
* "based on a variant of the GPU-STREAM benchmark code"
|
||
* Other comparable, clear and reasonable labelling is
|
||
* acceptable.
|
||
* 3c. Submission of results to the GPU-STREAM benchmark web site
|
||
* is encouraged, but not required.
|
||
* 4. Use of this program or creation of derived works based on this
|
||
* program constitutes acceptance of these licensing restrictions.
|
||
* 5. Absolutely no warranty is expressed or implied.
|
||
*———————————————————————————————————-----------------------------------------*/
|
||
|
||
|
||
#include <iostream>
|
||
#include <fstream>
|
||
#include <vector>
|
||
#include <chrono>
|
||
#include <cfloat>
|
||
#include <cmath>
|
||
|
||
#include <cuda.h>
|
||
#include "common.h"
|
||
|
||
std::string getDeviceName(int device);
|
||
|
||
// Code to check CUDA errors
|
||
void check_cuda_error(void)
|
||
{
|
||
cudaError_t err = cudaGetLastError();
|
||
if (err != cudaSuccess)
|
||
{
|
||
std::cerr
|
||
<< "Error: "
|
||
<< cudaGetErrorString(err)
|
||
<< std::endl;
|
||
exit(err);
|
||
}
|
||
}
|
||
|
||
template <typename T>
|
||
__global__ void copy(const T * a, T * c)
|
||
{
|
||
const int i = blockDim.x * blockIdx.x + threadIdx.x;
|
||
c[i] = a[i];
|
||
}
|
||
|
||
template <typename T>
|
||
__global__ void mul(T * b, const T * c)
|
||
{
|
||
const T scalar = 3.0;
|
||
const int i = blockDim.x * blockIdx.x + threadIdx.x;
|
||
b[i] = scalar * c[i];
|
||
}
|
||
|
||
template <typename T>
|
||
__global__ void add(const T * a, const T * b, T * c)
|
||
{
|
||
const int i = blockDim.x * blockIdx.x + threadIdx.x;
|
||
c[i] = a[i] + b[i];
|
||
}
|
||
|
||
template <typename T>
|
||
__global__ void triad(T * a, const T * b, const T * c)
|
||
{
|
||
const T scalar = 3.0;
|
||
const int i = blockDim.x * blockIdx.x + threadIdx.x;
|
||
a[i] = b[i] + scalar * c[i];
|
||
}
|
||
|
||
int main(int argc, char *argv[])
|
||
{
|
||
|
||
// Print out run information
|
||
std::cout
|
||
<< "GPU-STREAM" << std::endl
|
||
<< "Version: " << VERSION_STRING << std::endl
|
||
<< "Implementation: CUDA" << std::endl;
|
||
|
||
parseArguments(argc, argv);
|
||
|
||
if (NTIMES < 2)
|
||
throw std::runtime_error("Chosen number of times is invalid, must be >= 2");
|
||
|
||
std::cout << "Precision: ";
|
||
if (useFloat) std::cout << "float";
|
||
else std::cout << "double";
|
||
std::cout << std::endl << std::endl;
|
||
|
||
std::cout << "Running kernels " << NTIMES << " times" << std::endl;
|
||
|
||
if (ARRAY_SIZE % 1024 != 0)
|
||
{
|
||
unsigned int OLD_ARRAY_SIZE = ARRAY_SIZE;
|
||
ARRAY_SIZE -= ARRAY_SIZE % 1024;
|
||
std::cout
|
||
<< "Warning: array size must divide 1024" << std::endl
|
||
<< "Resizing array from " << OLD_ARRAY_SIZE
|
||
<< " to " << ARRAY_SIZE << std::endl;
|
||
if (ARRAY_SIZE == 0)
|
||
throw std::runtime_error("Array size must be >= 1024");
|
||
}
|
||
|
||
// Get precision (used to reset later)
|
||
std::streamsize ss = std::cout.precision();
|
||
|
||
size_t DATATYPE_SIZE;
|
||
|
||
if (useFloat)
|
||
{
|
||
DATATYPE_SIZE = sizeof(float);
|
||
}
|
||
else
|
||
{
|
||
DATATYPE_SIZE = sizeof(double);
|
||
}
|
||
|
||
// Display number of bytes in array
|
||
std::cout << std::setprecision(1) << std::fixed
|
||
<< "Array size: " << ARRAY_SIZE*DATATYPE_SIZE/1024.0/1024.0 << " MB"
|
||
<< " (=" << ARRAY_SIZE*DATATYPE_SIZE/1024.0/1024.0/1024.0 << " GB)"
|
||
<< std::endl;
|
||
std::cout << "Total size: " << 3.0*ARRAY_SIZE*DATATYPE_SIZE/1024.0/1024.0 << " MB"
|
||
<< " (=" << 3.0*ARRAY_SIZE*DATATYPE_SIZE/1024.0/1024.0/1024.0 << " GB)"
|
||
<< std::endl;
|
||
|
||
// Reset precision
|
||
std::cout.precision(ss);
|
||
|
||
// Check device index is in range
|
||
int count;
|
||
cudaGetDeviceCount(&count);
|
||
check_cuda_error();
|
||
if (deviceIndex >= count)
|
||
throw std::runtime_error("Chosen device index is invalid");
|
||
cudaSetDevice(deviceIndex);
|
||
check_cuda_error();
|
||
|
||
// Print out device name
|
||
std::cout << "Using CUDA device " << getDeviceName(deviceIndex) << std::endl;
|
||
|
||
// Check buffers fit on the device
|
||
cudaDeviceProp props;
|
||
cudaGetDeviceProperties(&props, deviceIndex);
|
||
if (props.totalGlobalMem < 3*DATATYPE_SIZE*ARRAY_SIZE)
|
||
throw std::runtime_error("Device does not have enough memory for all 3 buffers");
|
||
|
||
// Create host vectors
|
||
void * h_a = malloc(ARRAY_SIZE*DATATYPE_SIZE);
|
||
void * h_b = malloc(ARRAY_SIZE*DATATYPE_SIZE);
|
||
void * h_c = malloc(ARRAY_SIZE*DATATYPE_SIZE);
|
||
|
||
// Initilise arrays
|
||
for (unsigned int i = 0; i < ARRAY_SIZE; i++)
|
||
{
|
||
if (useFloat)
|
||
{
|
||
((float*)h_a)[i] = 1.0f;
|
||
((float*)h_b)[i] = 2.0f;
|
||
((float*)h_c)[i] = 0.0f;
|
||
}
|
||
else
|
||
{
|
||
((double*)h_a)[i] = 1.0;
|
||
((double*)h_b)[i] = 2.0;
|
||
((double*)h_c)[i] = 0.0;
|
||
}
|
||
}
|
||
|
||
// Create device buffers
|
||
void * d_a, * d_b, *d_c;
|
||
cudaMalloc(&d_a, ARRAY_SIZE*DATATYPE_SIZE);
|
||
check_cuda_error();
|
||
cudaMalloc(&d_b, ARRAY_SIZE*DATATYPE_SIZE);
|
||
check_cuda_error();
|
||
cudaMalloc(&d_c, ARRAY_SIZE*DATATYPE_SIZE);
|
||
check_cuda_error();
|
||
|
||
// Copy host memory to device
|
||
cudaMemcpy(d_a, h_a, ARRAY_SIZE*DATATYPE_SIZE, cudaMemcpyHostToDevice);
|
||
check_cuda_error();
|
||
cudaMemcpy(d_b, h_b, ARRAY_SIZE*DATATYPE_SIZE, cudaMemcpyHostToDevice);
|
||
check_cuda_error();
|
||
cudaMemcpy(d_c, h_c, ARRAY_SIZE*DATATYPE_SIZE, cudaMemcpyHostToDevice);
|
||
check_cuda_error();
|
||
|
||
// Make sure the copies are finished
|
||
cudaDeviceSynchronize();
|
||
check_cuda_error();
|
||
|
||
// List of times
|
||
std::vector< std::vector<double> > timings;
|
||
|
||
// Declare timers
|
||
std::chrono::high_resolution_clock::time_point t1, t2;
|
||
|
||
// Main loop
|
||
for (unsigned int k = 0; k < NTIMES; k++)
|
||
{
|
||
std::vector<double> times;
|
||
t1 = std::chrono::high_resolution_clock::now();
|
||
if (useFloat)
|
||
copy<<<ARRAY_SIZE/1024, 1024>>>((float*)d_a, (float*)d_c);
|
||
else
|
||
copy<<<ARRAY_SIZE/1024, 1024>>>((double*)d_a, (double*)d_c);
|
||
check_cuda_error();
|
||
cudaDeviceSynchronize();
|
||
check_cuda_error();
|
||
t2 = std::chrono::high_resolution_clock::now();
|
||
times.push_back(std::chrono::duration_cast<std::chrono::duration<double> >(t2 - t1).count());
|
||
|
||
|
||
t1 = std::chrono::high_resolution_clock::now();
|
||
if (useFloat)
|
||
mul<<<ARRAY_SIZE/1024, 1024>>>((float*)d_b, (float*)d_c);
|
||
else
|
||
mul<<<ARRAY_SIZE/1024, 1024>>>((double*)d_b, (double*)d_c);
|
||
check_cuda_error();
|
||
cudaDeviceSynchronize();
|
||
check_cuda_error();
|
||
t2 = std::chrono::high_resolution_clock::now();
|
||
times.push_back(std::chrono::duration_cast<std::chrono::duration<double> >(t2 - t1).count());
|
||
|
||
|
||
t1 = std::chrono::high_resolution_clock::now();
|
||
if (useFloat)
|
||
add<<<ARRAY_SIZE/1024, 1024>>>((float*)d_a, (float*)d_b, (float*)d_c);
|
||
else
|
||
add<<<ARRAY_SIZE/1024, 1024>>>((double*)d_a, (double*)d_b, (double*)d_c);
|
||
check_cuda_error();
|
||
cudaDeviceSynchronize();
|
||
check_cuda_error();
|
||
t2 = std::chrono::high_resolution_clock::now();
|
||
times.push_back(std::chrono::duration_cast<std::chrono::duration<double> >(t2 - t1).count());
|
||
|
||
|
||
t1 = std::chrono::high_resolution_clock::now();
|
||
if (useFloat)
|
||
triad<<<ARRAY_SIZE/1024, 1024>>>((float*)d_a, (float*)d_b, (float*)d_c);
|
||
else
|
||
triad<<<ARRAY_SIZE/1024, 1024>>>((double*)d_a, (double*)d_b, (double*)d_c);
|
||
check_cuda_error();
|
||
cudaDeviceSynchronize();
|
||
check_cuda_error();
|
||
t2 = std::chrono::high_resolution_clock::now();
|
||
times.push_back(std::chrono::duration_cast<std::chrono::duration<double> >(t2 - t1).count());
|
||
|
||
timings.push_back(times);
|
||
|
||
}
|
||
|
||
// Check solutions
|
||
cudaMemcpy(h_a, d_a, ARRAY_SIZE*DATATYPE_SIZE, cudaMemcpyDeviceToHost);
|
||
check_cuda_error();
|
||
cudaMemcpy(h_b, d_b, ARRAY_SIZE*DATATYPE_SIZE, cudaMemcpyDeviceToHost);
|
||
check_cuda_error();
|
||
cudaMemcpy(h_c, d_c, ARRAY_SIZE*DATATYPE_SIZE, cudaMemcpyDeviceToHost);
|
||
check_cuda_error();
|
||
|
||
if (useFloat)
|
||
{
|
||
check_solution<float>(h_a, h_b, h_c);
|
||
}
|
||
else
|
||
{
|
||
check_solution<double>(h_a, h_b, h_c);
|
||
}
|
||
|
||
// Crunch results
|
||
size_t sizes[4] = {
|
||
2 * DATATYPE_SIZE * ARRAY_SIZE,
|
||
2 * DATATYPE_SIZE * ARRAY_SIZE,
|
||
3 * DATATYPE_SIZE * ARRAY_SIZE,
|
||
3 * DATATYPE_SIZE * ARRAY_SIZE
|
||
};
|
||
double min[4] = {DBL_MAX, DBL_MAX, DBL_MAX, DBL_MAX};
|
||
double max[4] = {0.0, 0.0, 0.0, 0.0};
|
||
double avg[4] = {0.0, 0.0, 0.0, 0.0};
|
||
|
||
// Ignore first result
|
||
for (unsigned int i = 1; i < NTIMES; i++)
|
||
{
|
||
for (int j = 0; j < 4; j++)
|
||
{
|
||
avg[j] += timings[i][j];
|
||
min[j] = std::min(min[j], timings[i][j]);
|
||
max[j] = std::max(max[j], timings[i][j]);
|
||
}
|
||
}
|
||
|
||
for (int j = 0; j < 4; j++)
|
||
avg[j] /= (double)(NTIMES-1);
|
||
|
||
// Display results
|
||
std::string labels[] = {"Copy", "Mul", "Add", "Triad"};
|
||
std::cout
|
||
<< std::left << std::setw(12) << "Function"
|
||
<< std::left << std::setw(12) << "MBytes/sec"
|
||
<< std::left << std::setw(12) << "Min (sec)"
|
||
<< std::left << std::setw(12) << "Max"
|
||
<< std::left << std::setw(12) << "Average"
|
||
<< std::endl;
|
||
|
||
for (int j = 0; j < 4; j++)
|
||
{
|
||
std::cout
|
||
<< std::left << std::setw(12) << labels[j]
|
||
<< std::left << std::setw(12) << std::setprecision(3) << 1.0E-06 * sizes[j]/min[j]
|
||
<< std::left << std::setw(12) << std::setprecision(5) << min[j]
|
||
<< std::left << std::setw(12) << std::setprecision(5) << max[j]
|
||
<< std::left << std::setw(12) << std::setprecision(5) << avg[j]
|
||
<< std::endl;
|
||
}
|
||
|
||
// Free host vectors
|
||
free(h_a);
|
||
free(h_b);
|
||
free(h_c);
|
||
|
||
// Free cuda buffers
|
||
cudaFree(d_a);
|
||
check_cuda_error();
|
||
cudaFree(d_b);
|
||
check_cuda_error();
|
||
cudaFree(d_c);
|
||
check_cuda_error();
|
||
|
||
}
|
||
|
||
std::string getDeviceName(int device)
|
||
{
|
||
struct cudaDeviceProp prop;
|
||
cudaGetDeviceProperties(&prop, device);
|
||
check_cuda_error();
|
||
return std::string(prop.name);
|
||
}
|
||
|
||
void listDevices(void)
|
||
{
|
||
// Get number of devices
|
||
int count;
|
||
cudaGetDeviceCount(&count);
|
||
check_cuda_error();
|
||
|
||
// Print device names
|
||
if (count == 0)
|
||
{
|
||
std::cout << "No devices found." << std::endl;
|
||
}
|
||
else
|
||
{
|
||
std::cout << std::endl;
|
||
std::cout << "Devices:" << std::endl;
|
||
for (int i = 0; i < count; i++)
|
||
{
|
||
std::cout << i << ": " << getDeviceName(i) << std::endl;
|
||
check_cuda_error();
|
||
}
|
||
std::cout << std::endl;
|
||
}
|
||
}
|
||
|