A couple weeks ago, a GPU-enabled code appeared on MATLAB Central entitled, “A CUDA accelerated Beam Propagation Method [BPM] Solver using the Parallel Computing Toolbox.” In this post, we share a video which showcases how Jacket is much better than PCT at GPU computing, by analyzing performance on this Beam Propagation Method code.
To reproduce these results, download the source code here: CUDA_BPM_NOV_04_2010_AccelerEyes
These benchmarks were run on an NVIDIA Tesla C2070 GPU versus a quad-core Intel CPU. MATLAB + PCT R2010B were used for the PCT-GPU experiments. MATLAB + Jacket 1.6 (prerelease) were used for the Jacket-GPU experiments.
Take Home Message
Due to Jacket’s extensive library of GPU functions and its optimized GPU runtime, it performs 3.5X faster than the CPU and 2X faster than PCT-GPU. The total time to do the Jacket programming was <5 minutes. To learn more about how Jacket compares to alternatives, continue reading.
The AccelerEyes Promise
Jacket programmers will always have access to the broadest, fastest GPU computing technology – that is the AccelerEyes Promise. As parallel computing continues to evolve, Jacket programmers will continue to enjoy ever improving speeds, without having to change any Jacket-based codes. Write your code once, and let Jacket carry your code through the coming hardware evolution.