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BEGIN:VEVENT
DTSTAMP:20210916T132452Z
LOCATION:Ernesto Bertarelli
DTSTART;TZID=Europe/Stockholm:20210707T150000
DTEND;TZID=Europe/Stockholm:20210707T153000
UID:submissions.pasc-conference.org_PASC21_sess144_msa297@linklings.com
SUMMARY:Automatic Differentiation of C++ Codes on Emerging Manycore Archit
 ectures with Sacado
DESCRIPTION:Minisymposium\n\nAutomatic Differentiation of C++ Codes on Eme
 rging Manycore Architectures with Sacado\n\nPhipps, Pawlowski, Trott\n\nAu
 tomatic differentiation (AD) is a well-known technique for evaluating anal
 ytic derivatives of calculations implemented on a computer, with numerous 
 software tools available for incorporating AD technology into complex appl
 ications.  However, a growing challenge for AD is the efficient differenti
 ation of parallel computations implemented on emerging manycore computing 
 architectures such as multicore CPUs, GPUs, and accelerators as these devi
 ces become more pervasive.  In this work, we explore forward-mode, operato
 r overloading-based differentiation of C++ codes on these architectures us
 ing the widely available Sacado AD software package.  In particular, we le
 verage Kokkos, a C++ tool providing APIs for implementing parallel computa
 tions that is portable to a wide variety of emerging architectures.  We de
 scribe the challenges that arise when differentiating code for these archi
 tectures using Kokkos, and two approaches for overcoming them that ensure 
 optimal memory access patterns as well as expose additional dimensions of 
 fine-grained parallelism in the derivative calculation.  We describe the r
 esults of several computational experiments that demonstrate the performan
 ce of the approach on several contemporary CPU and GPU architectures.  We 
 then conclude with applications of these techniques to the simulation of d
 iscretized systems of partial differential equations.\n\nDomain: CS and Ma
 th, Emerging Applications, Physics, Engineering
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