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DTSTAMP:20181221T160903Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181115T083000
DTEND;TZID=America/Chicago:20181115T170000
UID:submissions.supercomputing.org_SC18_sess324_post158@linklings.com
SUMMARY:Tensor-Optimized Hardware Accelerates Fused Discontinuous Galerkin
  Simulations
DESCRIPTION:Poster\nTech Program Reg Pass, Exhibits Reg Pass\n\nTensor-Opt
 imized Hardware Accelerates Fused Discontinuous Galerkin Simulations\n\nBr
 euer, Heinecke, Cui\n\nIn recent years the compute/memory balance of proce
 ssors has been continuously shifting towards compute. The rise of Deep Lea
 rning, based on matrix multiplications, accelerated this path, especially 
 in terms of single precision and lower precision compute. An important res
 earch question is if this development can be leveraged for traditional HPC
 . We demonstrate that a high-order discontinuous Galerkin solver for seism
 ic wave propagation can execute in single precision without loss of modeli
 ng accuracy. Additionally, we extended its kernels to support the Intel Kn
 ights Mill CPU with 14 TFLOPS of single precision deep-learning performanc
 e. This allows us to harvest the hardware’s special compute capabilities, 
 even in an application with sparse linear algebra kernels. On cluster-leve
 l, Knights Mill can obtain the same application performance as the latest 
 top-bin dual-socket Intel Xeon Platinum nodes, while consuming lower power
 . Compared to the HPC-focused Knights Landing processor, scenario-dependen
 t speed-ups of up to 1.6× are possible.
URL:https://sc18.supercomputing.org/presentation/?id=post158&sess=sess324
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