{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T19:06:55Z","timestamp":1779131215764,"version":"3.51.4"},"reference-count":65,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2026,5,18]]},"abstract":"<jats:p>\n                    \\noindent\n                    <jats:italic toggle=\"yes\">Hypergraph pattern mining<\/jats:italic>\n                    (HPM) is a key analytical primitive for discovering higher-order relationships in complex data. Despite decades of algorithmic progress, existing systems remain far from saturating available compute resources, as the memory-bound and irregular nature of hypergraph workloads severely constrains sustained throughput. Our empirical characterization shows that even\n                    <jats:italic toggle=\"yes\">state-of-the-art<\/jats:italic>\n                    (SOTA) HPM systems achieve only a small fraction of their theoretical peak performance on real workloads. In this paper, we propose Octopus, the first full-stack hardware-software co-designed system for accelerating HPM on practical\n                    <jats:italic toggle=\"yes\">processing-in-memory<\/jats:italic>\n                    (PIM) hardware. Octopus targets UPMEM, an emerging commercially available PIM platform that integrates thousands of lightweight in-memory compute units within standard DRAM modules. To fully exploit UPMEM's massive parallelism and bandwidth potential while addressing its stringent architectural constraints, Octopus introduces two tightly integrated components: (i) an inter-DPU coordination framework that orchestrates compact data partitioning and balanced workload distribution across thousands of DPUs, and (ii) an intra-DPU mining engine that enables efficient hyperedge-level candidate generation and asynchronous multithreaded execution. We evaluate Octopus on a real UPMEM platform using diverse real-world hypergraph workloads. Experimental results demonstrate up to 55.37\u00d7, 19.80\u00d7, 1033.89\u00d7, and 795.08\u00d7 speedups over SOTA solutions HGMatch, OHMiner, Pangolin, and PimPam, respectively.\n                  <\/jats:p>","DOI":"10.1145\/3802094","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T18:19:16Z","timestamp":1779128356000},"page":"1-27","source":"Crossref","is-referenced-by-count":0,"title":["Octopus: Efficient Hypergraph Pattern Mining with Practical Processing-in-Memory Architecture"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1845-0160","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"first","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1981-9721","authenticated-orcid":false,"given":"Deting","family":"Chen","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, School of Software Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3927-1102","authenticated-orcid":false,"given":"Yu","family":"Huang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, School of Software Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2180-5428","authenticated-orcid":false,"given":"Chaoqiang","family":"Liu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3319-254X","authenticated-orcid":false,"given":"Haifeng","family":"Liu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1876-6931","authenticated-orcid":false,"given":"Jianhui","family":"Yue","sequence":"additional","affiliation":[{"name":"Michigan Technological University, Houghton, Michigan, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6302-813X","authenticated-orcid":false,"given":"Xiaofei","family":"Liao","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3934-7605","authenticated-orcid":false,"given":"Hai","family":"Jin","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the ACM International Conference on Web Search and Data Mining. 305-314","author":"Li Lei","year":"2013","unstructured":"Lei Li and Tao Li. 2013. 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