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DTSTART;TZID=Europe/Stockholm:20260701T140000
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UID:submissions.pasc-conference.org_PASC26_sess164@linklings.com
SUMMARY:MS5G - Computational Storage for Scientific Computing
DESCRIPTION:Access the recording\n\nOrganizer(s): Jakob Luettgau (INRIA), 
 Kira Duwe (CERN), and Michael Kuhn (Otto-von-Guericke-Universität Magdebur
 g)\n\nThe exponential growth of data handled by HPC systems is driven by g
 rowing simulation fidelity and an explosion of affordable, high-resolution
  sensing devices. The rise of machine learning applications and other larg
 e-scale data analysis tasks imposes workload patterns on existing storage 
 solutions that often lead to large, avoidable data copies and transfers, a
 s well as contention near storage devices and in the network. This creates
  challenges for the reproducibility of science and thus limits trust in sc
 ience. Computational storage is a promising solution to lower barriers to 
 public data access and reduce the time and cost for many workloads that re
 quire search across or aggregation of large volumes of data. The session w
 ill feature four speakers from academia, government labs, and industry to 
 give their perspectives on the challenges and opportunities of computation
 al storage for scientific computing and how the technology may help to inc
 rease trust in science despite the data deluge.\n\nOpportunities for Compu
 tational Storage in Scientific Computing\n\nThis talk will give an overvie
 w of computational storage as a promising technology both to lower barrier
 s to public data access while also reducing the time and cost for many exi
 sting workloads that require search across or aggregation of large volumes
  of data. The talk will give an overview of diff...\n\n\nJakob Luettgau (I
 NRIA)\n---------------------\nIn-Situ Data Analysis Meets Computational St
 orage Devices\n\nSimulations from domains such as climate science produce 
 increasing amounts of data as we approach Exascale. The current workflow f
 or generating knowledge from the data relies on post-mortem analysis, whic
 h requires storing the raw data. The amount of storage space needed to fac
 ilitate this analysis...\n\n\nNiclas Schroeter (DKRZ, Otto-von-Guericke-Un
 iversitat Magdeburg)\n---------------------\nPower Efficiency as Seen from
  the Storage Perspective\n\nWith computational resources increasingly comm
 oditized, performance optimization now prioritizes power efficiency. Effec
 tive energy optimization, however, necessitates preliminary empirical obse
 rvations. If power monitoring is well established for compute nodes, stora
 ge servers tend to be overlooked...\n\n\nJean-Thomas Acquaviva (DDN)\n----
 -----------------\nPanel Discussion\n\ntbd\n\n\nFrançois Tessier (INRIA)\n
 \nDomain: Computational Methods and Applied Mathematics\n\nSession Chair: 
 François Tessier (INRIA)
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