This is my implementation of the famous STREAM benchmark suite in CUDA. The code was first implemented during my PhD in December 2013 and updated a year later. The version added to Github has been updated with C++11 features to make the code more readable.
The code can be compiled and built as follows:
cd stream-cuda
mkdir build
cd build
cmake ..
make -j4
The code can be executed as follows:
../bin/stream-cuda <number-of-iterations> <block-size>
The default array size is 0.5 GB, but this can be changed by modifying the code. The two inputs to the code are number of iterations and block size. The recommended number of iterations is 100. The recommended block size for Kepler is 192 and 64 for Pascal. A global block size of 1024 works well for both architectures.
Below is a collection of STREAM Triad results from different CUDA devices. These results are useful for example in connection with performance modeling. All experiments conducted with ECC turned on.
| Machine | Device | Result (GB/s) |
|---|---|---|
| Abel | NVIDIA Tesla K20x | 180.45 |
| EPIC | NVIDIA Tesla P100 | 557.23 |
| Stampede | NVIDIA Tesla K20m | 151.13 |
| Taurus | NVIDIA Tesla K20xm | 181.60 |
| Taurus | NVIDIA Tesla K80 | 175.62 |
| Wilkes | NVIDIA Tesla K20c | 151.02 |
Please report bugs and issues by creating pull requests.