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DTSTAMP:20181221T160730Z
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:Paper\nGPUs, Memory, NVRAM, Performance, System Software, Tool
 s, Tech Program Reg Pass\n\nRuntime Data Management on Non-Volatile Memory
 -Based Heterogeneous Memory for Task-Parallel Programs\n\nWu, Ren, Li\n\nN
 on-volatile memory (NVM) provides a scalable solution to replace DRAM as m
 ain memory. Because of relatively high latency and low bandwidth of NVM (c
 omparing with DRAM), NVM often pairs with DRAM to build a  heterogeneous m
 ain memory system (HMS). Deciding data placement on NVM-based HMS is criti
 cal to enable future NVM-based HPC. In this paper, we study task-parallel 
 programs and introduce a runtime system to address the data placement prob
 lem on NVM-based HMS. Leveraging semantics and execution mode of task-para
 llel 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. Eval
 uating with a set of HPC benchmarks, we show that our runtime system achie
 ves higher performance than a conventional HMS-oblivious runtime (24% impr
 ovement on average) and two state-of-the-art HMS-aware solutions (16% and 
 11% improvement on average, respectively).
URL:https://sc18.supercomputing.org/presentation/?id=pap203&sess=sess205
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