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UID:submissions.supercomputing.org_SC23_sess291_rpost226@linklings.com
SUMMARY:Performant Low-Order Matrix-Free Finite Element Kernels on GPE Arc
 hitectures
DESCRIPTION:Randolph Settgast, William Tobin, Nicola Castelletto, and Yoha
 nn Dudouit (Lawrence Livermore National Laboratory (LLNL)); Sergey Klevtso
 v (Stanford University); and Ben Corbett (Lawrence Livermore National Labo
 ratory (LLNL))\n\nNumerical methods such as the Finite Element Method (FEM
 ) have successfully leveraged the computational power of GPU accelerators.
  However, much of the effort around FEM on GPU’s has been focused on high 
 order discretizations due to their higher arithmetic intensity and order o
 f accuracy. For applications such as the simulation of geologic reservoirs
 , high levels of heterogeneity results in high-resolution grids characteri
 zed by highly discontinuous (cell-wise) material property fields. Addition
 ally, the significant uncertainties typical of geologic reservoirs reduces
  the benefits of high order accuracy, and low order methods are typically 
 employed. In this study, we present a strategy for implementing highly per
 formant low-order matrix-free FEM operator kernels in the context of the c
 onjugate gradient method. Performance results of the operator kernel are p
 resented and are shown to compare favorably to matrix-based SpMV operators
  on V100, A100, and MI250X GPUs.\n\nTag: Artificial Intelligence/Machine L
 earning, Architecture and Networks, Heterogeneous Computing, I/O and File 
 Systems, Performance Measurement, Modeling, and Tools, Post-Moore Computin
 g, Programming Frameworks and System Software, Quantum Computing\n\nRegist
 ration Category: Tech Program Reg Pass, Exhibits Reg Pass\n\n
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