Processing math: 33%
Accelerating Graph-Connected Component Computation With Emerging Processing-In-Memory Architecture | IEEE Journals & Magazine | IEEE Xplore

Accelerating Graph-Connected Component Computation With Emerging Processing-In-Memory Architecture


Abstract:

Computing the connected component (CC) of a graph is a basic graph computing problem, which has numerous applications like graph partitioning and pattern recognition. Exi...Show More

Abstract:

Computing the connected component (CC) of a graph is a basic graph computing problem, which has numerous applications like graph partitioning and pattern recognition. Existing methods for computing CC suffer from memory wall problems because of the frequent data transmission between CPU and memory. To overcome this challenge, in this article, we propose to accelerate CC computation with the emerging processing-in-memory (PIM) architecture through an algorithm–architecture co-design manner. The innovation lies in computing CC with bitwise logical operations (such as AND and OR), and the customized data flow management methods to accelerate computation and reduce energy consumption. As a proof of concept, experimental results with computational spin-transfer torque magnetic RAM (STT-MRAM) arrays demonstrate on average 19.8\times and 12.4\times speedups compared with the CPU and GPU implementations, and a 35.4 \times energy efficiency improvement over the CPU implementation. Moreover, we investigate the potential associations between graph computing and bitwise Boolean logic, which could help design more general in-memory graph computing accelerators in the future.
Page(s): 5333 - 5342
Date of Publication: 30 March 2022

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