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DTSTART;TZID=America/Chicago:20181111T113000
DTEND;TZID=America/Chicago:20181111T115200
UID:submissions.supercomputing.org_SC18_sess149_ws_mchpc107@linklings.com
SUMMARY:Understanding Application Recomputability without Crash Consistenc
 y in Non-Volatile Memory
DESCRIPTION:Jie Ren, Kai Wu, and Dong Li (University of California, Merced
 )\n\nEmerging non-volatile memory (NVM) is promising to be used as main me
 mory, because of its good performance, density, and energy efficiency.  Le
 veraging the non-volatility of NVM as main memory, we can recover data obj
 ects and resume application computation (recomputation) after application 
 crashes.  The existing work studies how to ensure that data objects stored
  in NVM can be recovered to a consistent version during system recovery, a
  property referred to as crash consistency. However, enabling crash consis
 tency often requires program modification and brings large runtime overhea
 d. \n\nIn this paper, we use a different view to examine application recom
 putation in NVM. Without taking care of consistency of data objects, we ai
 m to understand if the application can be recomputable, given possible inc
 onsistent data objects in NVM. We introduce a PIN-based simulation tool, N
 VC, to study application recomputability in NVM without crash consistency.
  The tool allows the user to randomly trigger application crash and then p
 erform postmortem analysis (i.e., the analysis on data consistency) on dat
 a values in caches and memory. We use NVC to study a set of applications. 
 We reveal that some applications are inherently tolerant to crash consiste
 ncy. We perform a detailed analysis of the reasons. We study an optimizati
 on technique to accelerate the simulation performance of NVC. The techniqu
 e allows us to use NVC to study data-intensive applications with large dat
 a sets.\n\nTag: Memory, NVRAM, Parallel Programming Languages, Libraries, 
 and Models\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs: 
 Ron Brightwell (Sandia National Laboratories); Maya Gokhale (Lawrence Live
 rmore National Laboratory (LLNL)); Xian-He Sun (Illinois Institute of Tech
 nology); and Yonghong Yan (University of North Carolina, Charlotte)\n\n
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