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UID:submissions.supercomputing.org_SC20_sess164_pap359@linklings.com
SUMMARY:GVPROF: A Value Profiler for GPU-Based Clusters
DESCRIPTION:Paper\n\nGVPROF: A Value Profiler for GPU-Based Clusters\n\nZh
 ou, Hao, Mellor-Crummey, Meng, Liu\n\nGPGPUs are widely used in high-perfo
 rmance computing systems to accelerate scientific and machine learning wor
 kloads.  Developing efficient GPU kernels is critically important to obtai
 n bare-metal performance on GPU-based clusters. In this paper, we describe
  the design and implementation of GVProf, the first value profiler that pi
 npoints value-related inefficiencies in applications running on NVIDIA GPU
 -based clusters. The novelty of GVProf resides in its ability to detect te
 mporal and spatial value redundancies, which provide useful information to
  guide code optimization. GVProf can monitor production multi-node multi-G
 PU executions in clusters. Our experiments with well-known GPU benchmarks 
 and HPC applications show that GVProf incurs acceptable overhead and scale
 s to large executions. Using GVProf, we optimized several HPC and machine 
 learning workloads on one NVIDIA V100 GPU. In one case study of LAMMPS, op
 timizations based on information from GVProf led to whole-program speedups
  ranging from 1.37x on a single GPU to 1.08x on 64 GPUs.\n\nTag: Accelerat
 ors, FPGA, and GPUs, Machine Learning, Deep Learning and Artificial Intell
 igence, Performance/Productivity Measurement and Evaluation, Reliability a
 nd Resiliency\n\nRegistration Category: Tech Program Reg Pass
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