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
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.
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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
}
