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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20260522T150118Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181113T083000
DTEND;TZID=America/Chicago:20181113T170000
UID:submissions.supercomputing.org_SC18_sess322_post158@linklings.com
SUMMARY:Tensor-Optimized Hardware Accelerates Fused Discontinuous Galerkin
  Simulations
DESCRIPTION:Alexander Breuer (University of California, San Diego); Alexan
 der Heinecke (Intel Corporation); and Yifeng Cui (San Diego Supercomputer 
 Center)\n\nIn recent years the compute/memory balance of processors has be
 en continuously shifting towards compute. The rise of Deep Learning, based
  on matrix multiplications, accelerated this path, especially in terms of 
 single precision and lower precision compute. An important research questi
 on is if this development can be leveraged for traditional HPC. We demonst
 rate that a high-order discontinuous Galerkin solver for seismic wave prop
 agation can execute in single precision without loss of modeling accuracy.
  Additionally, we extended its kernels to support the Intel Knights Mill C
 PU with 14 TFLOPS of single precision deep-learning performance. This allo
 ws us to harvest the hardware’s special compute capabilities, even in an a
 pplication with sparse linear algebra kernels. On cluster-level, Knights M
 ill can obtain the same application performance as the latest top-bin dual
 -socket Intel Xeon Platinum nodes, while consuming lower power. Compared t
 o the HPC-focused Knights Landing processor, scenario-dependent speed-ups 
 of up to 1.6× are possible.\n\nRegistration Category: Tech Program Reg Pas
 s, Exhibits Reg Pass\n\n
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