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DTSTAMP:20181221T160908Z
LOCATION:D220
DTSTART;TZID=America/Chicago:20181112T090000
DTEND;TZID=America/Chicago:20181112T173000
UID:submissions.supercomputing.org_SC18_sess172@linklings.com
SUMMARY:8th Workshop on Python for High-Performance and Scientific Computi
 ng
DESCRIPTION:Workshop\nParallel Application Frameworks, Reproducibility, Sc
 ientific Computing, Workshop Reg Pass\n\nPanel: Interactivity in Supercomp
 uting\n\nScullin, Thomas\n\n---------------------\nPyHPC Lightning Talks\n
 \nSpotz\n\n---------------------\nKeynote: Better Scientific Software (BSS
 w)\n\nDubey, Hudson\n\n---------------------\nWorkshop Afternoon Break\n\n
 \n\n---------------------\nWorkshop Morning Break\n\n\n\n-----------------
 ----\nWorkshop Lunch (on your own)\n\n\n\n---------------------\nIntroduct
 ion - 8th Workshop on Python for High-Performance and Scientific Computing
 \n\nSchreiber, Scullin, Spotz, Thomas\n\nPython is an established, high-le
 vel programming language with a large community in academia and industry. 
 Scientists, engineers, and educators use Python for data science, high-per
 formance computing, and distributed computing. Since Python is extremely e
 asy to learn with a very clean syntax, it is ...\n\n---------------------\
 nAutoParallel: A Python Module for Automatic Parallelization and Distribut
 ed Execution of Affine Loop Nests\n\nRamon-Cortes, Amela, Ejarque, Clauss,
  Badia\n\nThe latest improvements in programming languages, programming mo
 dels, and frameworks have focused on abstracting the users from many progr
 amming issues. Among others, recent programming frameworks include simpler
  syntax, automatic memory management and garbage collection, simplifies co
 de re-usage th...\n\n---------------------\nManaging Python in HPC Environ
 ments\n\nGall, Indiviglio\n\nPython has seen a rapid adoption in the weath
 er and climate modeling science communities.  This swift rise has taken HP
 C system administrators by surprise, leading to inadequate support.  These
  trends, like those in other sciences, led to the development and widespre
 ad adoption of user managed binar...\n\n---------------------\nAcceleratin
 g the Signal Alignment Process in Time-Evolving Geometries Using Python\n\
 nRamakrishnaiah, Baker\n\nThis paper addresses the computational challenge
 s involved in postprocessing of signals received using multiple collectors
  (satellites). Multiple low cost, small sized satellites can be used as dy
 namic beamforming arrays (DBA) in remote sensing satellites. This usually 
 requires precise metrology and...\n\n---------------------\nPerformance, P
 ower, and Scalability Analysis of the Horovod Implementation of the CANDLE
  NT3 Benchmark on the Cray XC40 Theta\n\nWu, Taylor, Wozniak, Stevens, Bre
 ttin...\n\nTraining scientific deep learning models requires the large amo
 unt of computing power provided by HPC systems. In this paper, we use the 
 distributed deep learning framework Horovod to parallelize NT3, a Python b
 enchmark from the exploratory research project CANDLE (Cancer Distributed 
 Learning Enviro...\n\n---------------------\nData-Parallel Python for High
  Energy Physics Analyses\n\nPaterno, Green, Kowalkowski, Sehrish\n\nIn thi
 s paper, we explore features available in Python which are useful for data
  reduction tasks in High Energy Physics (HEP). High-level abstractions in 
 Python are convenient for implementing data reduction tasks. However, in o
 rder for such abstractions to be practical, the efficiency of their perf..
 .\n\n---------------------\nBalsam: Automated Scheduling and Execution of 
 Dynamic, Data-Intensive HPC Workflows\n\nSalim, Uram, Childers, Vishwanath
 , Papka...\n\nWe introduce the Balsam service to manage high-throughput ta
 sk scheduling and execution on supercomputing systems. Balsam allows users
  to populate a task database with a variety of tasks ranging from simple i
 ndependent tasks to dynamic multi-task workflows. With abstractions for th
 e local resource s...\n
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