GPU-STREAM

Measure memory transfer rates to/from global device memory on GPUs. This benchmark is similar in spirit, and based on, the STREAM benchmark [1] for CPUs.

Unlike other GPU memory bandwidth benchmarks this does not include the PCIe transfer time.

Usage

Build the OpenCL and CUDA binaries with make (CUDA version requires CUDA >= v6.5)

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

Assuming you have a recent Android NDK available, you can use the toolchain that it provides to build GPU-STREAM. You should first use the NDK to generate a standalone toolchain:

# Select a directory to install the toolchain to
ANDROID_NATIVE_TOOLCHAIN=/path/to/toolchain

${NDK}/build/tools/make-standalone-toolchain.sh \
  --platform=android-14 \
  --toolchain=arm-linux-androideabi-4.8 \
  --install-dir=${ANDROID_NATIVE_TOOLCHAIN}

Make sure that the OpenCL headers and library (libOpenCL.so) are available in ${ANDROID_NATIVE_TOOLCHAIN}/sysroot/usr/.

You should then be able to build GPU-STREAM:

make CXX=${ANDROID_NATIVE_TOOLCHAIN}/bin/arm-linux-androideabi-g++

Copy the executable and OpenCL kernels to the device:

adb push gpu-stream-ocl /data/local/tmp
adb push ocl-stream-kernels.cl /data/local/tmp

Run GPU-STREAM from an adb shell:

adb shell
cd /data/local/tmp

# Use float if device doesn't support double, and reduce array size
./gpu-stream-ocl --float -n 6 -s 10000000

Results

Sample results can be found in the results subdirectory. If you would like to submit updated results, please submit a Pull Request.

[1]: McCalpin, John D., 1995: "Memory Bandwidth and Machine Balance in Current High Performance Computers", IEEE Computer Society Technical Committee on Computer Architecture (TCCA) Newsletter, December 1995.