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UID:submissions.supercomputing.org_SC25_sess211_ws_llvmf107@linklings.com
SUMMARY:Scabbard: LLVM Instrumentation-aided Race Checking in CPU/GPU Unif
 ied Memory for AMD GPUs
DESCRIPTION:Andrew Osterhout (University of Utah), Ignacio Laguna (Lawrenc
 e Livermore National Laboratory (LLNL)), and Ganesh Gopalakrishnan (Univer
 sity of Utah)\n\nData Races are bugs whose occurrence in shared memory par
 allel and concurrent systems can cause unexpected outcomes and undermine t
 he integrity of computational results. CPU/GPU systems that use Unified He
 terogeneous Memory—for simplified memory sharing between GPUs and CPUs—are
  highly prone to data races. Scabbard's design is based on instrumenting t
 he CPU/GPU codes by using LLVM's pass-plugin system to instrument the user
 's code to record writes, reads and synchronizations into trace files duri
 ng execution, and subsequently performing offline analysis to report races
 . The main contribution of this paper is in detailing our algorithms, impl
 ementation challenges and the learning process faced by newcomers to the L
 LVM and ROCm/Hip ecosystems to better the learning experience for new LLVM
  tool-builders in the CPU/GPU space.\n\nRecording: Livestreamed, Recorded\
 n\nRegistration Category: Technical Program Reg Pass, Workshop Reg Pass\n\
 nSession Chairs: James C. Brodman (Intel Corporation); Johannes de Fine Li
 cht (NextSilicon Inc, ETH Zürich); Ryan Kabrick (Tactical Computing Labora
 tories LLC, University of Delaware); and Alexis Perry-Holby (Los Alamos Na
 tional Laboratory (LANL))\n\n
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