Abstract
Processing-in-memory (PIM) proposes to move computational components inside memory units to alleviate the high cost of data movement in big data processing. This approach has been recently utilized to reach high performance and energy-efficiency in large-scale graph processing. This paper analyzes a state-of-the-art PIM accelerator for graph processing and identifies message queue management as a significant bottleneck for system efficiency. Two metrics were introduced for representing the waiting time and processor utilization. We then present a lightweight solution for reducing waiting time caused by the message queue while increasing resource utilization in the system. Our simulation results on a set of real-world graph examples indicate that the enhanced graph processing system achieves 40% reduction in the overall execution time and 15% system energy savings over the baseline PIM based accelerator.
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