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GraphR: Accelerating Graph Processing Using ReRAM by Song, Linghao; Zhuo, Youwei; Qian, Xuehai; Li, Hai; Chen, Yiran is a scholarly article available to read on EtoBox.

What is GraphR: Accelerating Graph Processing Using ReRAM about?

This paper presents GRAPHR, the first ReRAM-based graph processing accelerator. GRAPHR follows the principle of near-data processing and explores the opportunity of performing massive parallel analog operations with low hardware and energy cost. The analog computation is suit- able for graph processing because: 1) The algorithms are iterative and could inherently tolerate the imprecision; 2) Both probability calculation (e.g., PageRank and Collaborative Filtering) and typical graph algorithms involving integers (e.g., BFS/SSSP) are resilient to errors. The key insight of GRAPHR is that if a vertex program of a graph algorithm can be expressed in sparse matrix vector multiplication (SpMV), it can be efficiently performed by ReRAM crossbar. We show that this assumption is generally true for a large set of graph algorithms. GRAPHR is a novel accelerator architecture consisting of two components: memory ReRAM and graph engine (GE). The core graph computations are performed in sparse matrix format in GEs (ReRAM crossbars). The vector/matrix-based graph computation is not new, but ReRAM offers the unique opportunity to realize the massive parallelism with unprecedented energy efficiency

Author

Song, Linghao; Zhuo, Youwei; Qian, Xuehai; Li, Hai; Chen, Yiran

Published

2017

Language

EN