{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:18:05Z","timestamp":1781194685720,"version":"3.54.1"},"reference-count":45,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,9,17]],"date-time":"2024-09-17T00:00:00Z","timestamp":1726531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Reconfigurable Technol. Syst."],"published-print":{"date-parts":[[2024,9,30]]},"abstract":"<jats:p>\n            Graph processing frameworks suffer performance degradation from under-utilization of available memory bandwidth, because graph traversal often exhibits poor locality. A prior work, ACTS [\n            <jats:xref ref-type=\"bibr\">24<\/jats:xref>\n            ], accelerates graph processing with FPGAs and High Bandwidth Memory (HBM). ACTS achieves locality by partitioning vertex-update messages (based on destination vertex IDs) generated online after active edges have been processed. This work introduces Dynamic-ACTS which builds on ideas in ACTS to support dynamic graphs. The key innovation is to use a hash table to find the edges to be updated. Compared to Gunrock, a GPU graph engine, Dynamic-ACTS achieves a geometric mean speedup of 1.5X, with a maximum speedup of 4.6X. Compared to GraphLily, an FPGA-HBM graph engine, Dynamic-ACTS achieves a geometric speedup of 3.6X, with a maximum speedup of 16.5X. Our results also showed a geometric mean power reduction of 50% and a mean reduction of energy-delay product of 88% over Gunrock. Compared to GraSU, an FPGA graph updating engine, Dynamic-ACTS achieves an average speedup of 15X.\n          <\/jats:p>","DOI":"10.1145\/3662002","type":"journal-article","created":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T12:00:03Z","timestamp":1714478403000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Dynamic-ACTS - A Dynamic Graph Analytics Accelerator For HBM-Enabled FPGAs"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9765-3475","authenticated-orcid":false,"given":"Oluwole","family":"Jaiyeoba","sequence":"first","affiliation":[{"name":"Computer Science, University of Virginia, Charlottesville, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8091-9302","authenticated-orcid":false,"given":"Kevin","family":"Skadron","sequence":"additional","affiliation":[{"name":"Computer Science, University of Virginia, Charlottesville, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,9,17]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA52012.2021.00054"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150412"},{"key":"e_1_3_1_4_2","unstructured":"Scott Beamer Krste Asanovi\u0107 and David Patterson. 2015. The GAP benchmark suite. (08 2015)."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2017.112"},{"key":"e_1_3_1_6_2","unstructured":"Maciej Besta Dimitri Stanojevic Johannes de Fine Licht Tal Ben-Nun and Torsten Hoefler. 2019. Graph processing on FPGAs: Taxonomy survey challenges. ArXiv abs\/1903.06697 (2019). Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:80628437"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","unstructured":"Avery C. 2011. \u201cGiraph: Large-scale graph processing infrastructure on Hadoop. 10.1145\/1582716.1582723","DOI":"10.1145\/1582716.1582723"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972740.43"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3431920.3439290"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","unstructured":"Guohao Dai Yuze Chi Yu Wang and Huazhong Yang. 2016. FPGP: Graph processing framework on FPGA a case study of breadth-first search. 105\u2013110. 10.1145\/2847263.2847339","DOI":"10.1145\/2847263.2847339"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","unstructured":"Guohao Dai Tianhao Huang Yuze Chi Ningyi Xu Yu Wang and Huazhong Yang. 2017. ForeGraph: Exploring large-scale graph processing on multi-FPGA architecture. 217\u2013226. 10.1145\/3020078.3021739","DOI":"10.1145\/3020078.3021739"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/2049662.2049663"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","unstructured":"Michael deLorimier Nachiket Kapre Nikil Mehta Dominic Rizzo Ian Eslick Raphael Rubin Tomas E. Uribe Thomas F. Jr. Knight and Andre DeHon. 2006. GraphStep: A system architecture for sparse-graph algorithms(FCCM\u201906). IEEE Computer Society USA 143\u2013151. 10.1109\/FCCM.2006.45","DOI":"10.1109\/FCCM.2006.45"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3314221.3314598"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/HPEC.2012.6408680"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2018.00042"},{"key":"e_1_3_1_17_2","unstructured":"Facebook. Key facts. Available at http:\/\/newsroom.fb.com\/content\/default.aspx?NewsAreaId=22 (May 2012). (n.d.)."},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2011.101911.00087"},{"key":"e_1_3_1_19_2","first-page":"17","article-title":"PowerGraph: Distributed graph-parallel computation on natural graphs","author":"Gonzalez Joseph","year":"2012","unstructured":"Joseph Gonzalez, Yucheng Low, Haijie Gu, Danny Bickson, and Carlos Guestrin. 2012. PowerGraph: Distributed graph-parallel computation on natural graphs. OSDI, 17\u201330.","journal-title":"OSDI"},{"key":"e_1_3_1_20_2","unstructured":"Google. Introducing the knowledge graph: things not strings . Available at http:\/\/insidesearch.blogspot.com\/2012\/05\/introducing-knowledge-graph-things-not.html (May 2012). (n.d.)."},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","unstructured":"Tae Jun Ham Lisa Wu Narayanan Sundaram Nadathur Satish and Margaret Martonosi. 2016. Graphicionado: A high-performance and energy-efficient accelerator for graph analytics. 1\u201313. 10.1109\/MICRO.2016.7783759","DOI":"10.1109\/MICRO.2016.7783759"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","unstructured":"Changwan Hong Aravind Sukumaran Rajam Israt Nisa Kunal Singh and Ponnuswamy Sadayappan. 2019. Adaptive sparse tiling for sparse matrix multiplication. 300\u2013314. 10.1145\/3293883.3295712","DOI":"10.1145\/3293883.3295712"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAD51958.2021.9643582"},{"key":"e_1_3_1_24_2","doi-asserted-by":"crossref","unstructured":"Oluwole Jaiyeoba and Kevin Skadron. 2019. GraphTinker: A high performance data structure for dynamic graph processing. In IEEE International Parallel & Distributed Processing Symposium (IPDPS).","DOI":"10.1109\/IPDPS.2019.00110"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543622.3573180"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956769"},{"key":"e_1_3_1_27_2","doi-asserted-by":"crossref","unstructured":"David R. Kincaid Thomas C. Oppe and David M. Young. 1989. ITPACKV 2D User\u2019s Guide.","DOI":"10.2172\/7093021"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1038\/nn.3837"},{"key":"e_1_3_1_29_2","article-title":"GraphLab: A new framework for parallel machine learning","author":"Low Yucheng","year":"2010","unstructured":"Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin, and Joseph Hellerstein. 2010. GraphLab: A new framework for parallel machine learning. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010 (06 2010).","journal-title":"Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/1582716.1582723"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","unstructured":"Eriko Nurvitadhi Gabriel Weisz Yu Wang Skand Hurkat Marie Nguyen James Hoe Jose Martinez and Carlos Guestrin. 2014. GraphGen: An FPGA framework for vertex-centric graph computation. 25\u201328. 10.1109\/FCCM.2014.15","DOI":"10.1109\/FCCM.2014.15"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/2517349.2522740"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","unstructured":"Linghao Song Yuze Chi Licheng Guo and Jason Cong. 2021. Serpens: A High Bandwidth Memory Based Accelerator for General-Purpose Sparse Matrix-Vector Multiplication. (2021). 10.48550\/ARXIV.2111.12555","DOI":"10.48550\/ARXIV.2111.12555"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3490422.3502357"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.14778\/2809974.2809983"},{"key":"e_1_3_1_36_2","unstructured":"Twitter. Goal. Available at http:\/\/blog.uk.twitter.com\/2012\/04\/goal.html(April 2012). (n.d.)."},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","unstructured":"Qinggang Wang Long Zheng Yu Huang Pengcheng Yao Chuangyi Gui Xiaofei Liao Hai Jin Wenbin Jiang and Fubing Mao. 2021. GraSU: A fast graph update library for FPGA-based dynamic graph processing. 149\u2013159. 10.1145\/3431920.3439288","DOI":"10.1145\/3431920.3439288"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","unstructured":"Yangzihao Wang Andrew Davidson Yuechao Pan Yuduo Wu Andy Riffel and John Owens. 2016. Gunrock: A high-performance graph processing library on the GPU. 1\u201312. 10.1145\/2851141.2851145","DOI":"10.1145\/2851141.2851145"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2017.2653818"},{"key":"e_1_3_1_40_2","unstructured":"Wikipedia. 2022. Circular shift \u2014 Wikipedia The Free Encyclopedia. http:\/\/en.wikipedia.org\/w\/index.php?title=Circular%20shift&oldid=1097704642 (2022). [Online; accessed 05-December-2022]."},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/1064830.1064834"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2017.8257937"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/1037187.1024415"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/FCCM.2016.35"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2019.2910068"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3203217.3203233"}],"container-title":["ACM Transactions on Reconfigurable Technology and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3662002","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3662002","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T23:43:40Z","timestamp":1750290220000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3662002"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,17]]},"references-count":45,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,9,30]]}},"alternative-id":["10.1145\/3662002"],"URL":"https:\/\/doi.org\/10.1145\/3662002","relation":{},"ISSN":["1936-7406","1936-7414"],"issn-type":[{"value":"1936-7406","type":"print"},{"value":"1936-7414","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,17]]},"assertion":[{"value":"2023-09-15","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-11","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}