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Pull request for HIP version

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This commit is contained in:
sunway513 2016-03-14 11:44:30 -05:00
parent 71d5813484
commit fdeb20601f
6 changed files with 473 additions and 2 deletions
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18
Makefile
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@ -6,7 +6,8 @@ ifeq ($(PLATFORM), Darwin)
LDLIBS = -framework OpenCL LDLIBS = -framework OpenCL
endif endif
all: gpu-stream-ocl gpu-stream-cuda all: gpu-stream-ocl gpu-stream-cuda gpu-stream-hip
gpu-stream-ocl: ocl-stream.cpp common.o Makefile gpu-stream-ocl: ocl-stream.cpp common.o Makefile
$(CXX) $(CXXFLAGS) -Wno-deprecated-declarations common.o $< -o $@ $(LDLIBS) $(CXX) $(CXXFLAGS) -Wno-deprecated-declarations common.o $< -o $@ $(LDLIBS)
@ -19,9 +20,22 @@ ifeq ($(shell which nvcc > /dev/null; echo $$?), 0)
else else
$(error "Cannot find nvcc, please install CUDA toolkit") $(error "Cannot find nvcc, please install CUDA toolkit")
endif endif
HIP_PATH?=../../..
HIPCC=$(HIP_PATH)/bin/hipcc
hip-stream.o : hip-stream.cpp
$(HIPCC) $(CXXFLAGS) -c $< -o $@
gpu-stream-hip: hip-stream.o common.o Makefile
ifeq ($(shell which $(HIPCC) > /dev/null; echo $$?), 0)
$(HIPCC) $(CXXFLAGS) common.o $< -lm -o $@
else
$(error "Cannot find $(HIPCC), please install HIP toolkit")
endif
.PHONY: clean .PHONY: clean
clean: clean:
rm -f gpu-stream-ocl gpu-stream-cuda *.o rm -f gpu-stream-ocl gpu-stream-cuda gpu-stream-hip *.o

7
README.md
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@ -13,6 +13,13 @@ Build the OpenCL and CUDA binaries with `make` (CUDA version requires CUDA >= v6
Run the OpenCL version with `./gpu-stream-ocl` and the CUDA version with `./gpu-stream-cuda` Run the OpenCL version with `./gpu-stream-ocl` and the CUDA version with `./gpu-stream-cuda`
For HIP version, follow the instructions on the following blog to properly install ROCK and ROCR drivers:
http://gpuopen.com/getting-started-with-boltzmann-components-platforms-installation/
Clone from the HIP repository in the following link:
https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP
Build the HIP binaries with make gpu-stream-hip, run it with './gpu-stream-hip'
Android Android
------- -------

398
hip-stream.cpp Normal file
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@ -0,0 +1,398 @@
#include "hip_runtime.h"
/*=============================================================================
*------------------------------------------------------------------------------
* 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);
int getDriver(void);
// Code to check CUDA errors
void check_cuda_error(void)
{
hipError_t err = hipGetLastError();
if (err != hipSuccess)
{
std::cerr
<< "Error: "
<< hipGetErrorString(err)
<< std::endl;
exit(err);
}
}
template <typename T>
__global__ void copy(hipLaunchParm lp, const T * a, T * c)
{
const int i = hipBlockDim_x * hipBlockIdx_x + hipThreadIdx_x;
c[i] = a[i];
}
template <typename T>
__global__ void mul(hipLaunchParm lp, T * b, const T * c)
{
const T scalar = 3.0;
const int i = hipBlockDim_x * hipBlockIdx_x + hipThreadIdx_x;
b[i] = scalar * c[i];
}
template <typename T>
__global__ void add(hipLaunchParm lp, const T * a, const T * b, T * c)
{
const int i = hipBlockDim_x * hipBlockIdx_x + hipThreadIdx_x;
c[i] = a[i] + b[i];
}
template <typename T>
__global__ void triad(hipLaunchParm lp, T * a, const T * b, const T * c)
{
const T scalar = 3.0;
const int i = hipBlockDim_x * hipBlockIdx_x + hipThreadIdx_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;
hipGetDeviceCount(&count);
check_cuda_error();
if (deviceIndex >= count)
throw std::runtime_error("Chosen device index is invalid");
hipSetDevice(deviceIndex);
check_cuda_error();
// Print out device name
std::cout << "Using CUDA device " << getDeviceName(deviceIndex) << std::endl;
// Print out device CUDA driver version
std::cout << "Driver: " << getDriver() << std::endl;
// Check buffers fit on the device
hipDeviceProp_t props;
hipGetDeviceProperties(&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;
hipMalloc(&d_a, ARRAY_SIZE*DATATYPE_SIZE);
check_cuda_error();
hipMalloc(&d_b, ARRAY_SIZE*DATATYPE_SIZE);
check_cuda_error();
hipMalloc(&d_c, ARRAY_SIZE*DATATYPE_SIZE);
check_cuda_error();
// Copy host memory to device
hipMemcpy(d_a, h_a, ARRAY_SIZE*DATATYPE_SIZE, hipMemcpyHostToDevice);
check_cuda_error();
hipMemcpy(d_b, h_b, ARRAY_SIZE*DATATYPE_SIZE, hipMemcpyHostToDevice);
check_cuda_error();
hipMemcpy(d_c, h_c, ARRAY_SIZE*DATATYPE_SIZE, hipMemcpyHostToDevice);
check_cuda_error();
// Make sure the copies are finished
hipDeviceSynchronize();
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)
hipLaunchKernel(HIP_KERNEL_NAME(copy), dim3(ARRAY_SIZE/1024), dim3(1024), 0, 0, (float*)d_a, (float*)d_c);
else
hipLaunchKernel(HIP_KERNEL_NAME(copy), dim3(ARRAY_SIZE/1024), dim3(1024), 0, 0, (double*)d_a, (double*)d_c);
check_cuda_error();
hipDeviceSynchronize();
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)
hipLaunchKernel(HIP_KERNEL_NAME(mul), dim3(ARRAY_SIZE/1024), dim3(1024), 0, 0, (float*)d_b, (float*)d_c);
else
hipLaunchKernel(HIP_KERNEL_NAME(mul), dim3(ARRAY_SIZE/1024), dim3(1024), 0, 0, (double*)d_b, (double*)d_c);
check_cuda_error();
hipDeviceSynchronize();
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)
hipLaunchKernel(HIP_KERNEL_NAME(add), dim3(ARRAY_SIZE/1024), dim3(1024), 0, 0, (float*)d_a, (float*)d_b, (float*)d_c);
else
hipLaunchKernel(HIP_KERNEL_NAME(add), dim3(ARRAY_SIZE/1024), dim3(1024), 0, 0, (double*)d_a, (double*)d_b, (double*)d_c);
check_cuda_error();
hipDeviceSynchronize();
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)
hipLaunchKernel(HIP_KERNEL_NAME(triad), dim3(ARRAY_SIZE/1024), dim3(1024), 0, 0, (float*)d_a, (float*)d_b, (float*)d_c);
else
hipLaunchKernel(HIP_KERNEL_NAME(triad), dim3(ARRAY_SIZE/1024), dim3(1024), 0, 0, (double*)d_a, (double*)d_b, (double*)d_c);
check_cuda_error();
hipDeviceSynchronize();
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
hipMemcpy(h_a, d_a, ARRAY_SIZE*DATATYPE_SIZE, hipMemcpyDeviceToHost);
check_cuda_error();
hipMemcpy(h_b, d_b, ARRAY_SIZE*DATATYPE_SIZE, hipMemcpyDeviceToHost);
check_cuda_error();
hipMemcpy(h_c, d_c, ARRAY_SIZE*DATATYPE_SIZE, hipMemcpyDeviceToHost);
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
hipFree(d_a);
check_cuda_error();
hipFree(d_b);
check_cuda_error();
hipFree(d_c);
check_cuda_error();
}
std::string getDeviceName(int device)
{
struct hipDeviceProp_t prop;
hipGetDeviceProperties(&prop, device);
check_cuda_error();
return std::string(prop.name);
}
int getDriver(void)
{
int driver;
hipDriverGetVersion(&driver);
check_cuda_error();
return driver;
}
void listDevices(void)
{
// Get number of devices
int count;
hipGetDeviceCount(&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;
}
}

15
results/cuda/nvidia-gtx-titan_x.txt Normal file
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@ -0,0 +1,15 @@
GPU-STREAM
Version: 1.0
Implementation: CUDA
Precision: double
Running kernels 10 times
Array size: 400.0 MB (=0.4 GB)
Total size: 1200.0 MB (=1.2 GB)
Using CUDA device GeForce GTX TITAN X
Driver: 7050
Function MBytes/sec Min (sec) Max Average
Copy 263155.587 0.00319 0.00319 0.00319
Mul 262943.430 0.00319 0.00319 0.00319
Add 268710.444 0.00468 0.00469 0.00469
Triad 268957.305 0.00468 0.00469 0.00468

15
results/hip/amd-fiji-nano.txt Normal file
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@ -0,0 +1,15 @@
GPU-STREAM
Version: 1.0
Implementation: CUDA
Precision: double
Running kernels 10 times
Array size: 400.0 MB (=0.4 GB)
Total size: 1200.0 MB (=1.2 GB)
Using CUDA device Fiji
Driver: 4
Function MBytes/sec Min (sec) Max Average
Copy 375822.410 0.00223 0.00225 0.00224
Mul 375086.879 0.00224 0.00227 0.00224
Add 425650.718 0.00296 0.00298 0.00297
Triad 424710.113 0.00296 0.00298 0.00298

22
results/hip/nvidia-gtx-titan_x.txt Normal file
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@ -0,0 +1,22 @@
GPU-STREAM
Version: 1.0
Implementation: HIP
GridSize: 52428800 work-items
GroupSize: 1024 work-items
Operations/Work-item: 1
Precision: double
Running kernels 10 times
Array size: 400.0 MB (=0.4 GB) 0 bytes padding
Total size: 1200.0 MB (=1.2 GB)
Using HIP device GeForce GTX TITAN X (compute_units=24)
Driver: 4
d_a=0x1306d80000
d_b=0x131fd80000
d_c=0x1338d80000
Function MBytes/sec Min (sec) Max Average
Copy 263042.207 0.00319 0.00320 0.00319
Mul 262972.033 0.00319 0.00320 0.00319
Add 268732.653 0.00468 0.00469 0.00469
Triad 268706.197 0.00468 0.00469 0.00469
GEOMEAN 265847.929