GRAM: graph processing in a ReRAM-based computational memory
GRAM: graph processing in a ReRAM-based computational memory
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DOI：
10.1145/3287624.3287711
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发表时间：
2019-01
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影响因子：
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通讯作者：
Minxuan Zhou;M. Imani;Saransh Gupta;Yeseong Kim;Tajana Simunic
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文献类型：
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作者：
Minxuan Zhou;M. Imani;Saransh Gupta;Yeseong Kim;Tajana Simunic
The performance of graph processing for real-world graphs is limited by inefficient memory behaviours in traditional systems because of random memory access patterns. Offloading computations to the memory is a promising strategy to overcome such challenges. In this paper, we exploit the resistive memory (ReRAM) based processing-in-memory (PIM) technology to accelerate graph applications. The proposed solution, GRAM, can efficiently executes vertex-centric model, which is widely used in large-scale parallel graph processing programs, in the computational memory. The hardware-software co-design used in GRAM maximizes the computation parallelism while minimizing the number of data movements. Based on our experiments with three important graph kernels on seven real-world graphs, GRAM provides 122.5X and 11.1x speedup compared with an in-memory graph system and optimized multithreading algorithms running on a multi-core CPU. Compared to a GPU-based graph acceleration library and a recently proposed PIM accelerator, GRAM improves the performance by 7.1X and 3.8X respectively.
