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UID:submissions.supercomputing.org_SC23_sess291_rpost220@linklings.com
SUMMARY:Modeling Parallel Programs Using Large Language Models
DESCRIPTION:Daniel Nichols (University of Maryland); Aniruddha Marathe, Ha
 rshitha Menon, and Todd Gamblin (Lawrence Livermore National Laboratory (L
 LNL)); and Abhinav Bhatele (University of Maryland)\n\nIn the past year a 
 large number of large language model (LLM) based tools for software develo
 pment have been released.  These tools have the capability to assist devel
 opers with many of the difficulties that arise from the ever-growing compl
 exity in the software stack.  As we enter the exascale era, with a diverse
  set of emerging hardware and programming paradigms, developing, optimizin
 g, and maintaining parallel software is becoming burdensome for developers
 .  While LLM-based coding tools have been instrumental in revolutionizing 
 software development, mainstream models are not designed, trained, or test
 ed on High Performance Computing (HPC) problems.  We present a LLM fine-tu
 ned on HPC data and demonstrate its effectiveness in HPC code generation, 
 OpenMP parallelization, and performance modeling.\n\nTag: Artificial Intel
 ligence/Machine Learning, Architecture and Networks, Heterogeneous Computi
 ng, I/O and File Systems, Performance Measurement, Modeling, and Tools, Po
 st-Moore Computing, Programming Frameworks and System Software, Quantum Co
 mputing\n\nRegistration Category: Tech Program Reg Pass, Exhibits Reg Pass
 \n\n
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