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Minnow: Lightweight Offload Engines for Worklist Management and Worklist-Directed Prefetching

Published:19 March 2018Publication History
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Abstract

The importance of irregular applications such as graph analytics is rapidly growing with the rise of Big Data. However, parallel graph workloads tend to perform poorly on general-purpose chip multiprocessors (CMPs) due to poor cache locality, low compute intensity, frequent synchronization, uneven task sizes, and dynamic task generation. At high thread counts, execution time is dominated by worklist synchronization overhead and cache misses. Researchers have proposed hardware worklist accelerators to address scheduling costs, but these proposals often harden a specific scheduling policy and do not address high cache miss rates. We address this with Minnow, a technique that augments each core in a CMP with a lightweight Minnow accelerator. Minnow engines offload worklist scheduling from worker threads to improve scalability. The engines also perform worklist-directed prefetching, a technique that exploits knowledge of upcoming tasks to issue nearly perfectly accurate and timely prefetch operations. On a simulated 64-core CMP running a parallel graph benchmark suite, Minnow improves scalability and reduces L2 cache misses from 29 to 1.2 MPKI on average, resulting in 6.01x average speedup over an optimized software baseline for only 1% area overhead.

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  1. Minnow: Lightweight Offload Engines for Worklist Management and Worklist-Directed Prefetching

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            • Published in

              cover image ACM SIGPLAN Notices
              ACM SIGPLAN Notices  Volume 53, Issue 2
              ASPLOS '18
              February 2018
              809 pages
              ISSN:0362-1340
              EISSN:1558-1160
              DOI:10.1145/3296957
              Issue’s Table of Contents
              • cover image ACM Conferences
                ASPLOS '18: Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems
                March 2018
                827 pages
                ISBN:9781450349116
                DOI:10.1145/3173162

              Copyright © 2018 ACM

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              New York, NY, United States

              Publication History

              • Published: 19 March 2018

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