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DTSTART;TZID=America/New_York:20241119T103000
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UID:submissions.supercomputing.org_SC24_sess380@linklings.com
SUMMARY:Workflow Characterization and Optimization
DESCRIPTION:DFTracer: An Analysis-Friendly Data Flow Tracer for AI-Driven 
 Workflows\n\nModern HPC workflows involve intricate coupling of simulation
 , data analytics, and artificial intelligence (AI) applications to improve
  time to scientific insight. However, current tools are not designed to wo
 rk with an AI-based I/O software stack that requires tracing at multiple l
 evels of the appl...\n\n\nHariharan Devarajan (Lawrence Livermore National
  Laboratory (LLNL), Illinois Institute of Technology); Loic Pottier (Lawre
 nce Livermore National Laboratory (LLNL)); Kaushik Velusamy and Huihuo Zhe
 ng (Argonne National Laboratory (ANL)); Izzet Yildirim (Illinois Institute
  of Technology); Olga Kogiou and Weikuan Yu (Florida State University); An
 thony Kougkas and Xian-He Sun (Illinois Institute of Technology); and Jae-
 Seung Yeom and Kathryn Mohror (Lawrence Livermore National Laboratory (LLN
 L))\n---------------------\nEfficient Weighted Graph Matching on GPUs\n\nW
 eighted matching identifies a maximal subset of edges in a graph with no c
 ommon vertices. As a prototypical graph problem, matching has numerous app
 lications in multi-level graph algorithms and machine learning. However, c
 hallenges arise in developing efficient, parallel graph matching methods o
 n c...\n\n\nMichael Mandulak (Rensselaer Polytechnic Institute (RPI)); Say
 an Ghosh, S. M. Ferdous, and Mahantesh Halappanavar (Pacific Northwest Nat
 ional Laboratory (PNNL)); and George Slota (Rensselaer Polytechnic Institu
 te (RPI))\n---------------------\nLLM-Pilot: Characterize and Optimize Per
 formance of your LLM Inference Services\n\nAs Large Language Models (LLMs)
  are rapidly growing in popularity, LLM inference services must be able to
  serve requests from thousands of users while satisfying performance requi
 rements. The performance of an LLM inference service is largely determined
  by the hardware onto which it is deployed, but...\n\n\nMalgorzata Lazuka 
 (IBM Zurich Research Laboratory, ETH Zürich) and Andreea Anghel and Thomas
  Parnell (IBM Zurich Research Laboratory)\n\nTag: Algorithms, Artificial I
 ntelligence/Machine Learning, Data Movement and Memory, Graph Algorithms\n
 \nRegistration Category: Tech Program Reg Pass\n\nSession Chair: Jay Lofst
 ead (Sandia National Laboratories, University of New Mexico)
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