GraphR：使用ReRAM加速图形处理
GraphR: Accelerating Graph Processing Using ReRAM
摘要：
本文介绍了第一个基于ReRAM的图形处理加速器GRAPHR。GRAPVR遵循近数据处理的原则，探索以低硬件和能源成本执行大规模并行模拟操作的机会。模拟计算适合于图形处理，因为：1）算法是迭代的，并且可以固有地容忍不精确性; 2）概率计算（例如，PageRank和协同过滤）和涉及整数的典型图形算法（例如，BFS/SSSP）对错误具有弹性。GRAPHR的关键见解是，如果图算法的顶点程序可以用稀疏矩阵向量乘法（SpMV）表示，则可以通过ReRAM crossbar有效地执行。我们表明，这一假设通常是真实的一个大的图算法。GRAPHR是一种新型的加速器架构，由两个组件组成：内存ReRAM和图形引擎（GE）。核心图计算在GE（ReRAM crossbar）中以稀疏矩阵格式执行。基于向量/矩阵的图计算并不新鲜，但ReRAM提供了一个独特的机会，以前所未有的能效和低硬件成本实现大规模并行。对于由GE处理的小的子图，执行并行操作的收益掩盖了由于稀疏性而造成的浪费。实验结果表明，GRAPHR实现了16.01倍（高达132.67倍）的加速比和33.82倍的几何平均节能相比，CPU基线系统。与GPU相比，GRAPHR实现了1.69倍至2.19倍的加速比，能耗降低了4.77倍至8.91倍。与基于PIM的架构相比，GRAPHR的加速比为1.16倍至4.12倍，能效为3.67倍至10.96倍。
英文摘要：
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 and low hardware cost. With small subgraphs processed by GEs, the gain of performing parallel operations overshadows the wastes due to sparsity. The experiment results show that GRAPHR achieves a 16.01x (up to 132.67x) speedup and a 33.82x energy saving on geometric mean compared to a CPU baseline system. Com- pared to GPU, GRAPHR achieves 1.69x to 2.19x speedup and consumes 4.77x to 8.91x less energy. GRAPHR gains a speedup of 1.16x to 4.12x, and is 3.67x to 10.96x more energy efficiency compared to PIM-based architecture.
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