Jianlong Zhong
ABSTRACT
Graphs are the de facto data structures for many applications, and efficient graph processing is a must for the application performance. GPUs have an order of magnitude higher computational power and memory bandwidth compared to CPUs and have been adopted to accelerate several common graph algorithms. However, it is difficult to write correct and efficient GPU programs and even more difficult for graph processing due to the irregularities of graph structures. To address those difficulties, we propose a programming framework named Medusa to simplify graph processing on GPUs. Medusa offers a small set of APIs, based on which developers can define their application logics by writing sequential code without awareness of GPU architectures. The Medusa runtime system automatically executes the developer defined APIs in parallel on the GPU, with a series of graph-centric optimizations. This poster gives an overview of Medusa, and presents some preliminary results.
- V. Agarwal, F. Petrini, D. Pasetto, and D. A. Bader. Scalable graph exploration on multicore processors. In SC, pages 1--11, 2010. Google Scholar
Digital Library
- GTGraph Generator. http://www.cc.gatech.edu/kamesh/GTgraph/.Google Scholar
- P. Harish and P. J. Narayanan. Accelerating large graph algorithms on the GPU using CUDA. In HiPC, pages 197--208, 2007. Google ScholarDigital Library
- B. He, W. Fang, Q. Luo, N. K. Govindaraju, and T. Wang. Mars: a MapReduce framework on graphics processors. In PACT, pages 260--269, 2008. Google ScholarDigital Library
- S. Hong, S. K. Kim, T. Oguntebi, and K. Olukotun. Accelerating CUDA graph algorithms at maximum warp. In PPoPP, pages 267--276, 2011. Google ScholarDigital Library
- Stanford Large Network Dataset Collections. http://snap.stanford.edu/data/index.html.Google Scholar
- J. Zhong and B. He. Gviewer: Gpu-accelerated graph visualization and mining. In SocInfo, pages 304--307, 2011. Google ScholarDigital Library
- J. Zhong, B. He, and G. Cong. Medusa: A unified framework for graph computation and visualization on graphics processors. Technical Report NTU-PDCC, Feb 2011. URL http://pdcc.ntu.edu.sg/.Google Scholar
Index Terms
- An overview of Medusa: simplified graph processing on GPUs
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