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.

There are multiple implementations of this benchmark in a variety of programming models. Currently implemented are:

• OpenCL
• CUDA
• OpenACC
• OpenMP 3 and 4.5
• Kokkos
• RAJA
• SYCL

Website

uob-hpc.github.io/GPU-STREAM/

Usage

Drivers, compiler and software applicable to whichever implementation you would like to build against is required.

We have supplied a series of Makefiles, one for each programming model, to assist with building. The Makefiles contain common build options, and should be simple to customise for your needs too.

General usage is make -f <Model>.make Common compiler flags and names can be set by passing a COMPILERoption to Make, e.g.make COMPILER=GNU. Some models allow specifying a CPU or GPU style target, and this can be set by passing a TARGEToption to Make, e.g.make TARGET=GPU`.

Pass in extra flags via the EXTRA_FLAGS option.

Android (outdated instructions)

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.

Citing

You can view the Poster and Extended Abstract on GPU-STREAM presented at SC'15. Please cite GPU-STREAM via this reference:

Deakin T, Price J, Martineau M, McIntosh-Smith S. GPU-STREAM v2.0: Benchmarking the achievable memory bandwidth of many-core processors across diverse parallel programming models. 2016. Paper presented at P^3MA Workshop at ISC High Performance, Frankfurt, Germany.

Other GPU-STREAM publications:

Deakin T, McIntosh-Smith S. GPU-STREAM: Benchmarking the achievable memory bandwidth of Graphics Processing Units. 2015. Poster session presented at IEEE/ACM SuperComputing, Austin, United States.

[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.