GraphIte: Accelerating Iterative Graph Algorithms on ReRAM Architectures via Approximate Computing
GraphIte: Accelerating Iterative Graph Algorithms on ReRAM Architectures via Approximate Computing
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DOI：
10.23919/date56975.2023.10137001
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发表时间：
2023-04
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影响因子：
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通讯作者：
Dwaipayan Choudhury;A. Kalyanaraman;P. Pande
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文献类型：
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作者：
Dwaipayan Choudhury;A. Kalyanaraman;P. Pande
基于ReRAM的内存处理（PIM）为计算近数据提供了一个有前途的范例，使其成为受稀疏性和不规则内存访问影响的图形应用程序的有吸引力的选择平台。然而，基于ReRAM的图形加速器的性能受到两个关键挑战的限制-显著的存储要求（特别是由于图形邻接矩阵的浪费的零单元存储），以及基于ReRAM的处理元件之间的大量片上流量。在本文中，我们提出了GraphIte，这是一个基于近似计算的框架，用于加速基于ReRAM架构的迭代图应用程序。GraphIte使用稀疏化和近似更新来显著减少ReRAM存储和数据移动。我们对PageRank和社区检测的实验表明，我们提出的架构比最先进的基于ReRAM的图形加速器的性能更好，执行时间减少了83.4%，同时对于一系列图形输入和工作负载，消耗的能量减少了87.9%。
ReRAM-based Processing-in-Memory (PIM) offers a promising paradigm for computing near data, making it an attractive platform of choice for graph applications that suffer from sparsity and irregular memory access. However, the performance of ReRAM-based graph accelerators is limited by two key challenges - significant storage requirements (particularly due to wasted zero cell storage of a graph's adjacency matrix), and significant amount of on-chip traffic between ReRAM-based processing elements. In this paper we present, GraphIte, an approximate computing-based framework for accelerating iterative graph applications on ReRAM-based architectures. GraphIte uses sparsification and approximate updates to achieve significant reductions in ReRAM storage and data movement. Our experiments on PageRank and community detection show that our proposed architecture outperforms a state-of-the-art ReRAM-based graph accelerator by up to 83.4% reduction in execution time while consuming up to 87.9% less energy for a range of graph inputs and workloads.