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DTSTART;TZID=America/Denver:20231113T113000
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UID:submissions.supercomputing.org_SC23_sess445_ws_scalah106@linklings.com
SUMMARY:Massively Distributed Finite-Volume Flux Computation
DESCRIPTION:Ryuichi Sai (TotalEnergies EP Research & Technology US, LLC); 
 Mathias Jacquelin (Cerebras Systems); Francois Hamon and Mauricio Araya-Po
 lo (TotalEnergies EP Research & Technology US, LLC); and Randolph R. Settg
 ast (Lawrence Livermore National Laboratory (LLNL))\n\nDesigning large-sca
 le geological carbon capture and storage projects and ensuring safe long-t
 erm CO2 containment - as a climate change mitigation strategy - requires f
 ast and accurate numerical simulations. These simulations involve solving 
 complex PDEs governing subsurface fluid flow using implicit finite-volume 
 schemes widely based on Two-Point Flux Approximation (TPFA). This task is 
 computationally and memory expensive, especially when performed on highly 
 detailed geomodels. In most current HPC architectures, memory hierarchy an
 d data management mechanisms are insufficient to overcome the challenges o
 f large scale numerical simulations. Therefore, it is crucial to design al
 gorithms that can exploit alternative and more balanced paradigms, such as
  dataflow and in-memory computing. This work introduces an algorithm for T
 PFA computations that exploits a dataflow architecture, such as Cerebras C
 S-2, which helps to significantly minimize memory bottlenecks. Our impleme
 ntation achieves two orders of magnitude speedup compared to multiple refe
 rence implementations running on latest generations of NVIDIA GPUs.\n\nTag
 : Algorithms, Heterogeneous Computing, Large Scale Systems\n\nRegistration
  Category: Workshop Reg Pass\n\nSession Chairs: Vassil Alexandrov (Hartree
  Centre, STFC); Jack Dongarra (University of Tennessee, Knoxville; Oak Rid
 ge National Laboratory (ORNL)); Christian Engelmann (Oak Ridge National La
 boratory (ORNL)); Al Geist (Oak Ridge National Laboratory (ORNL)); and Die
 ter A. Kranzlmueller (Ludwig-Maxmilians-Universität München, Leibniz Super
 computing Centre (LRZ))\n\n
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