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DTSTAMP:20210402T160547Z
LOCATION:Track 3
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UID:submissions.supercomputing.org_SC20_sess155@linklings.com
SUMMARY:AI for IT
DESCRIPTION:Paper\n\nRLScheduler: An Automated HPC Batch Job Scheduler Usi
 ng Reinforcement Learning\n\nZhang, Dai, He, Bao, Xie\n\nToday's high-perf
 ormance computing platforms are still dominated by batch jobs. Accordingly
 , effective batch job scheduling is crucial to obtain high system efficien
 cy. Existing batch job schedulers typically leverage heuristic priority fu
 nctions to prioritize and schedule jobs. Once configured by t...\n\n------
 ---------------\nAlita: Comprehensive Performance Isolation through Bias R
 esource Management for Public Clouds\n\nChen, Xue, Zhao, Chen, Wu...\n\nTh
 e tenants of public clouds share hardware resources on the same node, resu
 lting in the potential for performance interference (or malicious attacks)
 . A tenant is able to degrade the performance of its neighbors on the same
  node significantly through overuse of the shared memory bus, last level c
 ac...\n\n---------------------\nHPC I/O Throughput Bottleneck Analysis wit
 h Explainable Local Models\n\nIsakov, del Rosario, Madireddy, Balaprakash,
  Carns...\n\nWith the growing complexity of high-performance computing (HP
 C) systems, achieving high performance can be difficult because of I/O bot
 tlenecks. We analyze multiple years worth of Darshan logs from the Argonne
  Leadership Computing Facility's Theta supercomputer in order to understan
 d causes of poor ...\n\n\nTag: File Systems and I/O, Machine Learning, Dee
 p Learning and Artificial Intelligence, Performance/Productivity Measureme
 nt and Evaluation, Resource Management and Scheduling\n\nRegistration Cate
 gory: Tech Program Reg Pass
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