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GPUnet: Networking Abstractions for GPU Programs

Thursday, August 7, 2014 - 12:45pm
Authors: 

Sangman Kim, Seonggu Huh, Yige Hu, Xinya Zhang, and Emmett Witchel, The University of Texas at Austin; Amir Wated and Mark Silberstein, Technion—Israel Institute of Technology

Abstract: 

Despite the popularity of GPUs in high-performance and scientific computing, and despite increasingly generalpurpose hardware capabilities, the use of GPUs in network servers or distributed systems poses significant challenges.

GPUnet is a native GPU networking layer that provides a socket abstraction and high-level networking APIs for GPU programs. We use GPUnet to streamline the development of high-performance, distributed applications like in-GPU-memory MapReduce and a new class of low-latency, high-throughput GPU-native network services such as a face verification server.

Sangman Kim, The University of Texas at Austin

Seonggu Huh, The University of Texas at Austin

Xinya Zhang, The University of Texas at Austin

Yige Hu, The University of Texas at Austin

Amir Wated, Technion—Israel Institute of Technology

Emmett Witchel, The University of Texas at Austin

Mark Silberstein, Technion—Israel Institute of Technology

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BibTeX
@inproceedings {186165,
author = {Sangman Kim and Seonggu Huh and Xinya Zhang and Yige Hu and Amir Wated and Emmett Witchel and Mark Silberstein},
title = {{GPUnet}: Networking Abstractions for {GPU} Programs},
booktitle = {11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14)},
year = {2014},
isbn = { 978-1-931971-16-4},
address = {Broomfield, CO},
pages = {201--216},
url = {https://www.usenix.org/conference/osdi14/technical-sessions/presentation/kim},
publisher = {USENIX Association},
month = oct,
}
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