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DTSTAMP:20211207T055338Z
LOCATION:220-221
DTSTART;TZID=America/Chicago:20211117T133000
DTEND;TZID=America/Chicago:20211117T150000
UID:submissions.supercomputing.org_SC21_sess147@linklings.com
SUMMARY:Tools and Modeling
DESCRIPTION:Paper\n\nPilgrim: Scalable and (Near) Lossless MPI Tracing\n\n
 Wang, Balaji, Snir\n\nTraces of MPI communications are used by many perfor
 mance analysis and visualization tools. Storing exhaustive traces of large
  scale MPI applications is infeasible, due to their large volume.  Aggrega
 ted or lossy MPI traces are smaller, but provide much less information. In
  this paper, we present Pi...\n\n---------------------\nHybrid, Scalable, 
 Trace-Driven Performance Modeling of GPGPUs\n\nArafa, Badawy, ElWazir, Bar
 ai, Eker...\n\nIn this paper, we present PPT-GPU, a scalable performance p
 rediction toolkit for GPUs. PPT-GPU achieves scalability through a hybrid 
 high-level modeling approach where some computations are extrapolated and 
 multiple parts of the model are parallelized. The tool primary prediction 
 models use pre-coll...\n\n---------------------\nG-SEPM: Building an Accur
 ate and Efficient Soft Error Prediction Model for GPGPUs\n\nYue, Wei, Li, 
 Zhao, Jiang...\n\nAs GPUs become ubiquitous in large-scale HPC systems, en
 suring the reliable execution of such systems in the presence of soft erro
 rs is increasingly essential. To assess GPGPU programs' resilience toward 
 soft errors, researchers rely on Random Fault Injection (FI) method. Howev
 er, it is prohibitivel...\n\n\nTag: Performance\n\nRegistration Category: 
 Tech Program Reg Pass
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