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DTSTART:19700308T020000
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DTSTAMP:20260522T150115Z
LOCATION:C140/142
DTSTART;TZID=America/Chicago:20181115T153000
DTEND;TZID=America/Chicago:20181115T160000
UID:submissions.supercomputing.org_SC18_sess219_pap239@linklings.com
SUMMARY:Phase Asynchronous AMR Execution for Productive and Performant Ast
 rophysical Flows
DESCRIPTION:Muhammad Nufail Farooqi (Koc University); Tan Nguyen, Weiqun Z
 hang, Ann S. Almgren, and John Shalf (Lawrence Berkeley National Laborator
 y); and Didem Unat (Koc University)\n\nAdaptive Mesh Refinement (AMR) is a
 n approach to solving PDEs that reduces the computational and memory requi
 rements at the expense of increased communication. Although adopting async
 hronous execution can overcome communication issues, manually restructurin
 g an AMR application to realize asynchrony is extremely complicated and hi
 nders readability and long-term maintainability. To balance performance ag
 ainst productivity, we design a user-friendly API and adopt phase asynchro
 nous execution model where all subgrids at an AMR level can be computed as
 ynchronously. \n\nWe apply the phase asynchrony to transform a real-world 
 AMR application, CASTRO, which solves multicomponent compressible hydrodyn
 amic equations for astrophysical flows. We evaluate the performance and pr
 ogramming effort required to use our carefully designed API and execution 
 model for transitioning large legacy codes from synchronous to asynchronou
 s execution up to 278,528 Intel-KNL cores. CASTRO is about 100K lines of c
 ode but less than 0.2% code changes are required to achieve significant pe
 rformance improvement.\n\nTag: Algorithms, Applications, Computational Phy
 sics, Scientific Computing\n\nRegistration Category: Tech Program Reg Pass
 \n\nSession Chair: Kirk E. Jordan (IBM)\n\n
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