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UID:b31d6c09-7762-4fe6-91b0-b8a916845351@support.access-ci.org
DTSTAMP:20260911T231602Z
DTSTART:20261112T190000Z
DTEND:20261112T203000Z
SUMMARY:High Throughput and Many Task Computing (COMPLECS)
DESCRIPTION:SummaryNot all computational problems utilize the types of para
 llel applications traditionally designed to run on high-performance comput
 ing (HPC) systems. Today, many workloads running on these systems often re
 quire a modest amount of computing resources for any given job or task. Fo
 r specific research workloads, however, a more important consideration is 
 actually how much aggregate compute power can be consistently and reliably
  leveraged against a problem over time. These high-throughput computing (
 HTC) workloads aim to solve larger problems over extended periods by compl
 eting numerous smaller computational subtasks. For example, these often in
 volve significant parameter sweeps over simulation input parameters or reg
 ular processing and analysis of data collected from specialized instrument
 s. In some cases, these problems are also composed of numerous district co
 mputational subtasks linked together in highly structured, complex workflo
 ws, which can become a challenge in and of themselves to design and manage
  effectively. If your research problem can leverage a high-throughput or m
 any-task computing (MTC) model, then it is vital to learn how to build and
  run these types of workflows safely and effectively on HPC systems.In thi
 s third part of our series on Batch Computing, we introduce you to high-th
 roughput and many-task computing using the Slurm Workload Manager. In part
 icular, you will learn how to use Slurm job arrays and job dependencies, w
 hich can be used to create these more structured computational workflows. 
 We will also highlight some of the problems you’ll likely come across wh
 en you start running HTC and/or MTC workloads on HPC systems. This will in
 clude a discussion on job bundling strategies — what they are and when t
 o use them. Additional topics about high-throughput and many-task computin
 g workflows will be covered as time permits.InstructorMarty Kandes is a Se
 nior Computational and Data Science Research Specialist at the San Diego S
 upercomputer Center (SDSC). As part of the High-Performance Computing (HPC
 ) User Services Group within the Data-Enabled Scientific Computing Divisio
 n, he provides technical user support and services to the national researc
 h community leveraging the Advanced Cyberinfrastructure (CI) and HPC resou
 rces designed, built and operated by SDSC on behalf of the U.S. National S
 cience Foundation (NSF). Marty is also a member of the National Artificial
  Intelligence (AI) Research Institute for Intelligent CI with Computationa
 l Learning in the Environment (ICICLE). His current research interests inc
 lude problems in distributed AI inference over wireless networks, data pri
 vacy in natural language processing, and secure interactive computing. He 
 also contributes to many of the education, outreach, and training initiati
 ves at SDSC, including serving as a Co-PI for the COMPrehensive Learning f
 or end-users to Effectively utilize CyberinfraStructure (COMPLECS) CyberTr
 aining program and as mentor for the Research Experience for High School S
 tudents (REHS) program. Marty received his Ph.D. in Computational Science 
 from the Computational Science Research Center (CSRC) at San Diego State U
 niversity (SDSU), where he studied quantum systems in rotating frames of r
 eference through the use of numerical simulations. He also holds an M.S. i
 n Physics from SDSU and dual B.S. degrees in Applied Mathematics and Physi
 cs from the University of Michigan, Ann Arbor.See a full list of SDSC's up
 coming training and events here.--- COMPLECS (COMPrehensive Learning for 
 end-users to Effectively utilize CyberinfraStructure) is a new SDSC progra
 m where training will cover non-programming skills needed to effectively u
 se supercomputers. Topics include parallel computing concepts, Linux tools
  and bash scripting, security, batch computing, how to get help, data mana
 gement and interactive computing. Each session offers 1 hour of instructio
 n followed by a 30-minute Q&A. COMPLECS is supported by NSF award 2320934
 .
URL:https://support.access-ci.org/events/9086
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