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DTSTAMP:20260522T150124Z
LOCATION:C141/143/149
DTSTART;TZID=America/Chicago:20181114T103000
DTEND;TZID=America/Chicago:20181114T110000
UID:submissions.supercomputing.org_SC18_sess205_pap203@linklings.com
SUMMARY:Runtime Data Management on Non-Volatile Memory-Based Heterogeneous
  Memory for Task-Parallel Programs
DESCRIPTION:Kai Wu, Jie Ren, and Dong Li (University of California, Merced
 )\n\nNon-volatile memory (NVM) provides a scalable solution to replace DRA
 M as main memory. Because of relatively high latency and low bandwidth of 
 NVM (comparing with DRAM), NVM often pairs with DRAM to build a  heterogen
 eous main memory system (HMS). Deciding data placement on NVM-based HMS is
  critical to enable future NVM-based HPC. In this paper, we study task-par
 allel programs and introduce a runtime system to address the data placemen
 t problem on NVM-based HMS. Leveraging semantics and execution mode of tas
 k-parallel programs, we efficiently characterize memory access patterns of
  tasks and reduce data movement overhead. We also introduce a performance 
 model to predict performance for tasks with various data placements on HMS
 . Evaluating with a set of HPC benchmarks, we show that our runtime system
  achieves higher performance than a conventional HMS-oblivious runtime (24
 % improvement on average) and two state-of-the-art HMS-aware solutions (16
 % and 11% improvement on average, respectively).\n\nTag: GPUs, Memory, NVR
 AM, Performance, System Software, Tools\n\nRegistration Category: Tech Pro
 gram Reg Pass\n\nSession Chair: Rosa M. Badia (Barcelona Supercomputing Ce
 nter (BSC); Polytechnic University of Catalonia, Spain)\n\n
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