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DTSTAMP:20260422T000610Z
LOCATION:501-502
DTSTART;TZID=America/Denver:20231112T090000
DTEND;TZID=America/Denver:20231112T123000
UID:submissions.supercomputing.org_SC23_sess416@linklings.com
SUMMARY:High Performance Python for Science at Scale
DESCRIPTION:HPPSS – Invited Speaker\n\nTravis Oliphant (Quansight)\n------
 ---------------\nHPPSS – Panel Discussion\n\nPete Mendygral (Hewlett Packa
 rd Enterprise (HPE)), Travis Oliphant (Quansight), Torsten Hoefler (ETH Zu
 rich - Swiss Federal Institute of Technology), Siu Kwan Lam (Anaconda), Be
 n Zaitlen (NVIDIA Corporation), and Fernanda Foertter (Voltron Data)\n----
 -----------------\nHPPSS – Morning Break\n---------------------\nDragon Pr
 oxy Runtimes and Multi-System Workflows\n\nWe present a novel method for o
 btaining proxy access to remote instances of the Dragon distributed runtim
 e. Dragon is a composable distributed runtime for managing dynamic process
 es, high-performance communication objects, memory and data at scale that 
 is based on an abstraction of a distributed sys...\n\n\nNicholas Radcliffe
 , Kent Lee, and Pete Mendygral (Hewlett Packard Enterprise (HPE))\n-------
 --------------\nHPPSS – Introduction\n\nPete Mendygral (Hewlett Packard En
 terprise (HPE))\n---------------------\nHigh Performance Python for Scienc
 e at Scale\n\nThis workshop aims to connect researchers, developers, and P
 ython practitioners to share their experiences scaling Python applications
  and codes on supercomputers. The goal is to provide a platform for topica
 l discussion of best practices, hands-on demonstrations, and community eng
 agement via open-s...\n\n\nPete Mendygral (Hewlett Packard Enterprise (HPE
 )), Sam Foreman (Argonne National Laboratory (ANL)), and Daniel Margala (L
 awrence Berkeley National Laboratory (LBNL))\n---------------------\nMaxim
 izing Data Utility for HPC Python Workflow Execution\n\nLarge-scale HPC wo
 rkflows are increasingly implemented in dynamic languages such as Python, 
 which allow for more rapid development than traditional techniques. Howeve
 r, the cost of executing Python applications at scale is often dominated b
 y the distribution of common datasets and complex software d...\n\n\nThanh
  Son Phung (University of Notre Dame), Ben Clifford (CQX Limited), Kyle Ch
 ard (University of Chicago), and Douglas Thain (University of Notre Dame)\
 n---------------------\nWisdomWombat:  A Polyglot Dataflow CFD Code Using 
 Python and Dragon\n\nWe present a prototype solution for a polyglot comput
 ational fluid dynamics code using the Python Multiprocessing API and Drago
 n. The code uses an actor-based dataflow architecture with a directed grap
 h to explicitly express program execution including parallelization and as
 ynchronous communication....\n\n\nJulius Donnert (Hewlett Packard Enterpri
 se (HPE)), Lindsey Gordon (University of Minnesota), Christopher Nolting (
 Gustavus Adolphus College), and Peter Mendygral (Hewlett Packard Enterpris
 e (HPE))\n---------------------\nDemonstration of Portable Performance of 
 Scientific Machine Learning on High Performance Computing Systems\n\nWith 
 the largest datasets to date and a diverse set of discoveries to be made, 
 the current generation of scientific analyses are well poised to utilize a
 rtificial intelligence (AI) and machine learning (ML) on high performance 
 computing (HPC) resources.  Like never before, these workflows can be wri.
 ..\n\n\nKhalid Hossain, Riccardo Balin, Corey Adams, Thomas Uram, Kalyan K
 umaran, and Venkatram Vishwanath (Argonne National Laboratory (ANL)) and T
 anima Dey, Subrata Goswami, Janghaeng Lee, Rebecca Ramer, and Koichi Yamad
 a (Intel Corporation)\n---------------------\nHPPSS – Concluding Remarks\n
 \nPete Mendygral (Hewlett Packard Enterprise (HPE)), Sam Foreman (Argonne 
 National Laboratory (ANL)), and Daniel Margala (Lawrence Berkeley National
  Laboratory (LBNL))\n\nTag: Applications, Distributed Computing, Large Sca
 le Systems, Programming Frameworks and System Software, Runtime Systems\n\
 nRegistration Category: Workshop Reg Pass\n\nSession Chairs: Sam Foreman (
 Argonne National Laboratory (ANL)); Daniel Margala (Lawrence Berkeley Nati
 onal Laboratory (LBNL)); Pete Mendygral (Hewlett Packard Enterprise (HPE))
 ; Laurie A. Stephey (Lawrence Berkeley National Laboratory (LBNL), Nationa
 l Energy Research Scientific Computing Center (NERSC)); and Rollin Thomas 
 (Lawrence Berkeley National Laboratory (LBNL), National Energy Research Sc
 ientific Computing Center (NERSC))
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