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Archit. Code Optim."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>\n            Graph processing has become a central concern for many real-world applications and is well-known for its low compute-to-communication ratios and poor data locality. By integrating computing logic into memory, resistive random access memory (ReRAM) tackles the demand for high memory bandwidth in graph processing. Despite the years\u2019 research efforts, existing ReRAM-based graph processing approaches still face the challenges of\n            <jats:italic>redundant computation overhead<\/jats:italic>\n            . It is because the vertices of many subgraphs are ineffectively and repeatedly processed over the ReRAM crossbars for lots of iterations so as to update their states according to the vertices of other subgraphs regardless of the dependencies among the subgraphs. In this article, we propose\n            <jats:italic>ASGraph<\/jats:italic>\n            , a dependency-aware ReRAM-based graph processing accelerator that overcomes the aforementioned performance bottlenecks. Specifically, ASGraph dynamically constructs the subgraph based on the dependencies between vertices\u2019 states and then detects constructed subgraph that owns high value (it is likely that it has accumulated many state propagations from its neighbors and is able to affect more other neighbors) to be preferentially processed. In this way, it makes the vertex states propagate along the dependencies between vertices as much as possible to reduce the redundant computation. Besides, ASGraph employs a hybrid processing scheme to accelerate the state propagations of the tightly connected subgraph, thereby minimizing the redundant computations. Experimental results show that ASGraph achieves 25.5\u00d7 and 4.8\u00d7 speedup and 70.8\u00d7 and 2.2\u00d7 energy saving on average compared with the state-of-the-art ReRAM-based graph processing accelerators, that is, GraphR and GaaS-X, respectively.\n          <\/jats:p>","DOI":"10.1145\/3689335","type":"journal-article","created":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T08:46:28Z","timestamp":1730537188000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["An Efficient ReRAM-based Accelerator for Asynchronous Iterative Graph Processing"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4217-7886","authenticated-orcid":false,"given":"Jin","family":"Zhao","sequence":"first","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0718-8045","authenticated-orcid":false,"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4374-0293","authenticated-orcid":false,"given":"Donghao","family":"He","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9559-9800","authenticated-orcid":false,"given":"Qikun","family":"Li","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0470-9049","authenticated-orcid":false,"given":"Weihang","family":"Yin","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6559-6111","authenticated-orcid":false,"given":"Hui","family":"Yu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3273-5381","authenticated-orcid":false,"given":"Hao","family":"Qi","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 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