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DTSTART;TZID=America/New_York:20241117T122000
DTEND;TZID=America/New_York:20241117T122500
UID:submissions.supercomputing.org_SC24_sess736_ws_pdswwip108@linklings.co
 m
SUMMARY:A Global In-Memory Cache and Computation Tier for DAOS
DESCRIPTION:John Byrne, Clarete Crasta, Abhishek Dwaraki, David Emberson, 
 Harumi Kuno, Sekwon Lee, Sharad Singhal, Ramya Ahobala Rao, Shreyas Vinaya
 ka Basri K S, Amitha C, Chinmay Ghosh, Rishi Kesh Rajak, Sriram Ravishanka
 r, Porno Shome, Lance Evans, Sherin George, and Kevan Rehm (Hewlett Packar
 d Enterprise (HPE)); Myungjun (MJ) Son (Hewlett Packard Enterprise (HPE), 
 The Pennsylvania State University); Taeklim Kim (Hewlett Packard Enterpris
 e (HPE), The University of Texas at Austin); and Shiyue (Jason) Hou (Hewle
 tt Packard Enterprise (HPE), Northeastern University)\n\nThere are signifi
 cant differences between emerging AI and data analytics workloads and trad
 itional HPC workloads with regard to storage and programming frameworks. W
 e extend DAOS with a queryable global shared low-latency/high-bandwidth ca
 che and a resilient runtime that intercepts calls to popular analytics fra
 meworks and offloads them to worker processes running on the HPC system. T
 he result is a solution that offers bandwidth and latency benefits over va
 nilla DAOS and that enables ordinary programmers to interactively use popu
 lar programming frameworks like Python to solve huge problems on HPC syste
 ms without stranding resources.\n\nTag: Data Movement and Memory, I/O, Sto
 rage, Archive\n\nRegistration Category: Workshop Reg Pass\n\nSession Chair
 s: Suren Byna (The Ohio State University, Lawrence Berkeley National Labor
 atory (LBNL)); Dean Hildebrand (Google LLC); Anthony Kougkas (Illinois Ins
 titute of Technology, Argonne National Laboratory (ANL)); Jay Lofstead (Sa
 ndia National Laboratories, University of New Mexico); and Bing Xie (Micro
 soft Corporation)\n\n
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