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DTSTART;TZID=America/Chicago:20211118T133000
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UID:submissions.supercomputing.org_SC21_sess155@linklings.com
SUMMARY:Scalable I/O and Persistent Memory
DESCRIPTION:Paper\n\nHardware-Supported Remote Persistence for Distributed
  Persistent Memory\n\nDuan, Lu, Liu, Liao, Jin...\n\nThe advent of Persist
 ent Memory (PM) necessitates an evolution of Remote Direct Memory Access (
 RDMA) technologies for supporting  remote data persistence. Previous softw
 are-based solutions require remote CPU intervention and postpone the visib
 ility of remote persistence. In this paper, we design sev...\n\n----------
 -----------\nClairvoyant Prefetching for Distributed Machine Learning I/O\
 n\nDryden, Böhringer, Ben-Nun, Hoefler\n\nI/O is emerging as a major bottl
 eneck for machine learning training, especially in distributed environment
 s. Indeed, at large scale, I/O takes as much as 85% of training time. Addr
 essing this I/O bottleneck necessitates careful optimization, as optimal d
 ata ingestion pipelines differ between systems...\n\n---------------------
 \nOnline Optimization of File Transfers in High-Speed Networks\n\nArifuzza
 man, Arslan\n\nFile transfers in high-speed networks require network and I
 /O parallelism to reach high speeds, however, creating arbitrarily large n
 umbers of I/O and network threads overwhelms system resources and causes f
 airness issues. In this paper, we introduce Falcon that combines a novel u
 tility function wit...\n\n\nTag: Memory Systems, Networks\n\nRegistration 
 Category: Tech Program Reg Pass
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