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BEGIN:VEVENT
DTSTAMP:20181221T160904Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181115T083000
DTEND;TZID=America/Chicago:20181115T170000
UID:submissions.supercomputing.org_SC18_sess324_post237@linklings.com
SUMMARY:Accelerating Wave-Propagation Algorithms with Adaptive Mesh Refine
 ment Using the Graphics Processing Unit (GPU)
DESCRIPTION:Poster\nTech Program Reg Pass, Exhibits Reg Pass\n\nAccelerati
 ng Wave-Propagation Algorithms with Adaptive Mesh Refinement Using the Gra
 phics Processing Unit (GPU)\n\nQin, LeVeque, Motley\n\nClawpack is a libra
 ry for solving nonlinear hyperbolic partial differential equations using h
 igh-resolution finite volume methods based on Riemann solvers and limiters
 . It supports Adaptive Mesh Refinement (AMR), which is essential in solvin
 g multi-scale problems. Recently, we added capabilities to accelerate the 
 code by using the Graphics Process Unit (GPU). Routines that manage CPU an
 d GPU AMR data and facilitate the execution of GPU kernels are added. Cust
 omized and CPU thread-safe memory managers are designed to reduce the over
 head of memory allocation and de-allocation. Some small GPU kernels are me
 rged into bigger kernels, which greatly reduces kernel launching overhead.
  A speed-up between 2 and 3 for the total running time is observed in an a
 coustics benchmark problem. Other Riemann solvers can easily be plugged in
  and get accelerated. The influence of some AMR parameters and challenges 
 introduced by AMR are also discussed.
URL:https://sc18.supercomputing.org/presentation/?id=post237&sess=sess324
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