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Archit. Code Optim."],"published-print":{"date-parts":[[2021,12,31]]},"abstract":"<jats:p>Graph structures are a natural representation of important and pervasive data. While graph applications have significant parallelism, their characteristic pointer indirect loads to neighbor data hinder scalability to large datasets on multicore systems. A scalable and efficient system must tolerate latency while leveraging data parallelism across millions of vertices. Modern Out-of-Order (OoO) cores inherently tolerate a fraction of long latencies, but become clogged when running severely memory-bound applications. Combined with large power\/area footprints, this limits their parallel scaling potential and, consequently, the gains that existing software frameworks can achieve. Conversely, accelerator and memory hierarchy designs provide performant hardware specializations, but cannot support diverse application demands.<\/jats:p>\n          <jats:p>To address these shortcomings, we present GraphAttack, a hardware-software data supply approach that accelerates graph applications on in-order multicore architectures. GraphAttack proposes compiler passes to (1)\u00a0identify idiomatic long-latency loads and (2)\u00a0slice programs along these loads into data Producer\/ Consumer threads to map onto pairs of parallel cores. Each pair shares a communication queue; the Producer asynchronously issues long-latency loads, whose results are buffered in the queue and used by the Consumer. This scheme drastically increases memory-level parallelism (MLP) to mitigate latency bottlenecks. In equal-area comparisons, GraphAttack outperforms OoO cores, do-all parallelism, prefetching, and prior decoupling approaches, achieving a 2.87\u00d7 speedup and 8.61\u00d7 gain in energy efficiency across a range of graph applications. These improvements scale; GraphAttack achieves a 3\u00d7 speedup over 64 parallel cores. Lastly, it has pragmatic design principles; it enhances in-order architectures that are gaining increasing open-source support.<\/jats:p>","DOI":"10.1145\/3469846","type":"journal-article","created":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T16:12:01Z","timestamp":1630685521000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["GraphAttack"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0764-0778","authenticated-orcid":false,"given":"Aninda","family":"Manocha","sequence":"first","affiliation":[{"name":"Princeton University, Princeton, NJ"}]},{"given":"Tyler","family":"Sorensen","sequence":"additional","affiliation":[{"name":"UC Santa Cruz, Santa Cruz, CA"}]},{"given":"Esin","family":"Tureci","sequence":"additional","affiliation":[{"name":"Princeton University, Princeton, NJ"}]},{"given":"Opeoluwa","family":"Matthews","sequence":"additional","affiliation":[{"name":"Princeton University, Princeton, NJ"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4955-7235","authenticated-orcid":false,"given":"Juan L.","family":"Arag\u00f3n","sequence":"additional","affiliation":[{"name":"Universidad de Murcia, Murcia, Spain"}]},{"given":"Margaret","family":"Martonosi","sequence":"additional","affiliation":[{"name":"Princeton University, Princeton, NJ"}]}],"member":"320","published-online":{"date-parts":[[2021,9,3]]},"reference":[{"volume-title":"RISC-V Ariane CVA6. 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