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DTSTART:19700308T020000
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DTSTAMP:20210402T160552Z
LOCATION:Track 3
DTSTART;TZID=America/New_York:20201119T130000
DTEND;TZID=America/New_York:20201119T143000
UID:submissions.supercomputing.org_SC20_sess164@linklings.com
SUMMARY:GPU-Based Tools and Modeling
DESCRIPTION:Paper\n\nAn Efficient and Non-Intrusive GPU Scheduling Framewo
 rk for Deep Learning Training Systems\n\nWang, Gonzalez, Zhou, Williams, F
 riedman...\n\nWe propose an efficient, non-intrusive GPU scheduling framew
 ork that employs a combination of an adaptive GPU scheduler and an elastic
  GPU allocation mechanism to reduce the completion time of DL training wor
 kloads and improve resource utilization. Specifically, the adaptive GPU sc
 heduler includes a...\n\n---------------------\nGVPROF: A Value Profiler f
 or GPU-Based Clusters\n\nZhou, Hao, Mellor-Crummey, Meng, Liu\n\nGPGPUs ar
 e widely used in high-performance computing systems to accelerate scientif
 ic and machine learning workloads.  Developing efficient GPU kernels is cr
 itically important to obtain bare-metal performance on GPU-based clusters.
  In this paper, we describe the design and implementation of GVProf, ...\n
 \n---------------------\nGPU-Trident: Efficient Modeling of Error Propagat
 ion in GPU Programs\n\nAnwer, Li, Pattabiraman, Sullivan, Tsai...\n\nFault
  injection (FI) techniques are typically used to determine the reliability
  profiles of programs under soft errors. These techniques, however, are hi
 ghly resource- and time-intensive. Prior research developed a model, TRIDE
 NT to analytically predict Silent Data Corruption ((SDC); i.e., incorrect.
 ..\n\n\nTag: Accelerators, FPGA, and GPUs, Machine Learning, Deep Learning
  and Artificial Intelligence, Performance/Productivity Measurement and Eva
 luation, Reliability and Resiliency\n\nRegistration Category: Tech Program
  Reg Pass
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