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DTSTAMP:20260422T000713Z
LOCATION:501-502
DTSTART;TZID=America/Denver:20231113T163000
DTEND;TZID=America/Denver:20231113T164500
UID:submissions.supercomputing.org_SC23_sess440_ws_ai4s102@linklings.com
SUMMARY:Autotuning Apache TVM-Based Scientific Applications Using Bayesian
  Optimization
DESCRIPTION:Xingfu Wu (Argonne National Laboratory (ANL), University of Ch
 icago); Praveen Paramasivam (University of South Dakota); and Valerie Tayl
 or (Argonne National Laboratory (ANL))\n\nApache TVM (Tensor Virtual Machi
 ne), an open source machine learning compiler framework designed to optimi
 ze computations across various hardware platforms, provides an opportunity
  to improve the performance of dense matrix factorizations such as LU (Low
 er Upper) decomposition and Cholesky decomposition on GPUs, FPGAs, ASICs, 
 and AI accelerators. In this paper, we propose a new TVM autotuning framew
 ork using Bayesian Optimization and use the TVM tensor expression language
  to implement linear algebra kernels such as LU, Cholesky, and 3mm.  We us
 e these scientific computation kernels to evaluate the effectiveness of ou
 r methods on a GPU cluster, called Swing, at Argonne National Laboratory. 
 We compare the proposed autotuning framework with the TVM autotuning frame
 work AutoTVM with four tuners and find that our framework outperforms Auto
 TVM in most cases.\n\nTag: Artificial Intelligence/Machine Learning\n\nReg
 istration Category: Workshop Reg Pass\n\nSession Chairs: Murali Emani (Arg
 onne National Laboratory (ANL)); Gokcen Kestor (Barcelona Supercomputing C
 enter (BSC); University of California, Merced); and Dong Li (University of
  California, Merced)\n\n
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