{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T05:16:18Z","timestamp":1740028578919,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014]]},"abstract":"<jats:p>Due to their high parallelism graphics processing units (GPUs) and GPU-based clusters have gained popularity in high-performance computing. However, data transfer in GPU-based clusters remains a challenging problem, due to the disjoint memory of GPU and host. New technologies, such as GPUDirect RDMA, improve data transfer among multiple GPUs, but they require many manual interventions from programmers to reach optimal performance.<\/jats:p>","DOI":"10.3233\/978-1-61499-381-0-461","type":"book-chapter","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T15:30:51Z","timestamp":1739979051000},"source":"Crossref","is-referenced-by-count":0,"title":["GPI2 for GPUs: A PGAS framework for efficient communication in hybrid clusters"],"prefix":"10.3233","author":[{"family":"Oden Lena","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Advances in Parallel Computing","Parallel Computing: Accelerating Computational Science and Engineering (CSE)"],"original-title":[],"deposited":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T15:33:49Z","timestamp":1739979229000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISSNISBN&issn=0927-5452&volume=25&spage=461"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-381-0-461","relation":{},"ISSN":["0927-5452"],"issn-type":[{"value":"0927-5452","type":"print"}],"subject":[],"published":{"date-parts":[[2014]]}}}