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DTSTART;TZID=America/Denver:20231113T163000
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UID:submissions.supercomputing.org_SC23_sess453_ws_exampi116@linklings.com
SUMMARY:A Statistical Analysis of HPC Network Tuning
DESCRIPTION:Donald Kruse, Whit Schonbein, and Matthew G. F. Dosanjh (Sandi
 a National Laboratories)\n\nDistributed scientific applications run on a c
 omplex stack of soft- ware and network technologies. Each layer has config
 uration options for tuning performance. These can range from protocol thre
 sh- olds to algorithmic changes for collectives. Micro-benchmarks are a co
 mmon methodology to evaluate the communication stack and are relatively ea
 sy to tune. However they aren’t representative of application behavior. Pr
 oxy applications, however, offer a simplified, but realistic, representati
 on of the main computational and communicative methods in scientific progr
 ams. Since these proxy applications contain realistic message passing patt
 erns, the correlation between micro-benchmarks and proxy application perfo
 rmance is not obvious. We present a study of statistically analyzing the i
 mpacts of tuning. Our results show how tuned micro-benchmark performance c
 orrelates with tuned proxy application performance.\n\nTag: Exascale, Mess
 age Passing, Programming Frameworks and System Software\n\nRegistration Ca
 tegory: Workshop Reg Pass\n\nSession Chairs: Purushotham Bangalore (The Un
 iversity of Alabama); Amanda J. Bienz (University of New Mexico); Matthew 
 G. F. Dosanjh (Sandia National Laboratories); Ryan Grant (Queen's Universi
 ty, Canada; Power API); William Schonbein (Sandia National Laboratories); 
 and Anthony Skjellum (Tennessee Technological University, ASCEND-TNTECH)\n
 \n
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