3857

Twin peaks: a software platform for heterogeneous computing on general-purpose and graphics processors

Jayanth Gummaraju, Laurent Morichetti, Michael Houston, Ben Sander, Benedict R. Gaster, Bixia Zheng
Advanced Micro Devices, Sunnyvale, CA, USA
In Proceedings of the 19th international conference on Parallel architectures and compilation techniques (2010), pp. 205-216.

@conference{gummaraju2010twin,

   title={Twin peaks: a software platform for heterogeneous computing on general-purpose and graphics processors},

   author={Gummaraju, J. and Morichetti, L. and Houston, M. and Sander, B. and Gaster, B.R. and Zheng, B.},

   booktitle={Proceedings of the 19th international conference on Parallel architectures and compilation techniques},

   pages={205–216},

   year={2010},

   organization={ACM}

}

Source Source   

1644

views

Modern processors are evolving into hybrid, heterogeneous processors with both CPU and GPU cores used for general purpose computation. Several languages such as Brook, CUDA, and more recently OpenCL are being developed to fully harness the potential of these processors. These languages typically involve the control code running on the CPU and the performance-critical, data-parallel kernel code running on the GPUs. In this paper, we present Twin Peaks, a software platform for heterogeneous computing that executes code originally targeted for GPUs efficiently on CPUs as well. This permits a more balanced execution between the CPU and GPU, and enables portability of code between these architectures and to CPU-only environments. We propose several techniques in the runtime system to efficiently utilize the caches and functional units present in CPUs. Using OpenCL as a canonical language for heterogeneous computing, and running several experiments on real hardware, we show that our techniques enable GPGPU-style code to execute efficiently on multicore CPUs with minimal runtime overhead. These results also show that for maximum performance, it is beneficial for applications to utilize both CPUs and GPUs as accelerator targets.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2024 hgpu.org

All rights belong to the respective authors

Contact us: