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DTSTAMP:20210402T160544Z
LOCATION:Poster Module
DTSTART;TZID=America/New_York:20201119T083000
DTEND;TZID=America/New_York:20201119T170000
UID:submissions.supercomputing.org_SC20_sess337_rpost156@linklings.com
SUMMARY:Towards Optimizing Memory Mapping of Persistent Memory in UMap
DESCRIPTION:Posters, Research Posters\n\nTowards Optimizing Memory Mapping
  of Persistent Memory in UMap\n\nYoussef, Iwabuchi, Feng, Gokhale, Pearce\
 n\nThe exponential growth in data set sizes across multiple domains create
 s challenges in terms of storing data efficiently as well as performing sc
 alable computations on such data. Memory mapping files on different storag
 e types offer a uniform interface as well as programming productivity for 
 applications that perform in-place or out-of-core computations. Multi-thre
 aded applications, however, incur an I/O contention on mapped files, hampe
 ring their scalability. Also, many applications handle sparse data structu
 res, rendering storage efficiency a desirable feature. To address these ch
 allenges, we present SparseStore, a tool for transparently partitioning a 
 memory-mapped persistent region into multiple files with dynamic and spars
 e allocation. We provide SparseStore as a part of UMap, a user-space page 
 management tool for memory mapping. Our experiments demonstrated that usin
 g UMap with SparseStore yielded up to 12x speedup compared to system-level
  mmap, and up to 2x speedup compared to the default UMap that maps a singl
 e file.\n\nRegistration Category: Tech Program Reg Pass, Exhibits Reg Pass
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