BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Europe/Stockholm
X-LIC-LOCATION:Europe/Stockholm
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20190719T085754Z
LOCATION:HG F 3
DTSTART;TZID=Europe/Stockholm:20190613T164500
DTEND;TZID=Europe/Stockholm:20190613T184500
UID:submissions.pasc-conference.org_PASC19_sess161@linklings.com
SUMMARY:MS36 - Machine Learning for HPC
DESCRIPTION:Minisymposium\nComputer Science and Applied Mathematics\n\nMac
 hine Learning Near-Optimal Parameters for Small Matrix-Matrix Multiplicati
 on Kernels on GPUs\n\nJakobovits\n\nParameterized kernels are an important
  tool to achieve high performance in scientific computing. Auto-tuning all
  kernels with an exhaustive search in parameter space is often prohibitive
 ly expensive in time and compute resources. Here, we use machine learning 
 to derive a performance model from a sub...\n\n---------------------\nNeur
 al Code Comprehension: A Learnable Representation of Code Semantics\n\nBen
 -Nun\n\nIn the era of “Big Code”, research is being conducted 
 into automating the understanding of computer programs. Most of the curren
 t works base on techniques from Natural Language Processing and Deep Learn
 ing, which have been successful recently, attempting to process the code d
 irectly or u...\n\n---------------------\nMachine-Learning-Based Performan
 ce Modeling and Tuning for High-Performance Computing\n\nBalaprakash, Wild
 \n\nHeterogeneous nodes, many-core processors, deep memory hierarchies, en
 ergy efficiency demands, and performance variability make application and 
 system management on high-performance computing systems an increasingly da
 unting task. Current strategies provided by operating and runtime systems 
 are most...\n\n---------------------\nLearning to Optimize Machine Learnin
 g Workloads\n\nTomioka\n\nMore and more specialized hardware accelerators 
 targeting machine learning and computer vision are becoming available with
  different architectural advantages and constraints. How can we maximize d
 eveloper productivity while achieving high performance on a variety of har
 dware accelerators? We are dev...\n
END:VEVENT
END:VCALENDAR