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DTSTAMP:20210402T160552Z
LOCATION:Track 5
DTSTART;TZID=America/New_York:20201118T130000
DTEND;TZID=America/New_York:20201118T143000
UID:submissions.supercomputing.org_SC20_sess162@linklings.com
SUMMARY:Simulation, Modeling, and Benchmarks
DESCRIPTION:Paper\n\nCost-Aware Prediction of Uncorrected DRAM Errors in t
 he Field\n\nBoixaderas, Zivanovic, Moré, Bartolome, Vicente...\n\nThis pap
 er presents and evaluates a method to predict DRAM uncorrected errors, a l
 eading cause of hardware failures in large-scale HPC clusters. The method 
 uses a random forest classifier, which was trained and evaluated using err
 or logs from two years of production of the MareNostrum 3 supercompute...\
 n\n---------------------\nTask Bench: A Parameterized Benchmark for Evalua
 ting Parallel Runtime Performance\n\nSlaughter, Wu, Fu, Brandenburg, Garci
 a...\n\nWe present Task Bench, a parameterized benchmark designed to explo
 re the performance of distributed programming systems under a variety of a
 pplication scenarios. Task Bench dramatically lowers the barrier to benchm
 arking and comparing multiple programming systems by making the implementa
 tion for a g...\n\n---------------------\nSmart-PGSim: Using Neural Networ
 k to Accelerate AC-OPF Power Grid Simulation\n\nDong, Xie, Kestor, Li\n\nT
 he optimal power flow (OPF) problem is one of the most important optimizat
 ion problems for the operation of the power grid. It calculates the optimu
 m scheduling of the committed generation units. In this paper, we develop 
 a neural network approach to the problem of accelerating the current optim
 al ...\n\n\nTag: Machine Learning, Deep Learning and Artificial Intelligen
 ce, Requirements, Performance, and Benchmarks, Reliability and Resiliency\
 n\nRegistration Category: Tech Program Reg Pass
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