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CC 2017
Sun 5 - Mon 6 February 2017 Austin, Texas, United States
Sun 5 Feb 2017 11:20 - 11:45 at 404 - Concurrency & Parallelism Chair(s): Sebastian Hack

While GPUs play an increasingly important role in today's
high-performance computers, optimizing GPU performance continues to
impose large burdens upon programmers. A major challenge in
optimizing codes for GPUs stems from the two levels of hardware
parallelism, blocks and threads; each of these levels has
significantly different characteristics, requiring different
optimization strategies.

In this paper, we propose a novel compiler optimization algorithm for
GPU parallelism. Our approach is based on the polyhedral model, which
has enabled significant advances in program analysis and
transformation compared to traditional AST-based frameworks. We
extend polyhedral schedules to enable two-level parallelization
through the idea of superposition, which integrates separate
schedules for block-level and thread-level parallelism. Our
experimental results demonstrate that our proposed compiler
optimization framework can deliver 1.8$\times$ and 2.1$\times$
geometric mean improvements on NVIDIA Tesla M2050 and K80 GPUs,
compared to a state-of-the-art polyhedral parallel code generator
(PPCG) for GPGPUs.

Sun 5 Feb
Times are displayed in time zone: (GMT-06:00) Saskatchewan, Central America change

10:30 - 12:10: Research Papers - Concurrency & Parallelism at 404
Chair(s): Sebastian HackSaarland University
CC-2017-papers10:30 - 10:55
Robin MorissetENS, France, Francesco Zappa NardelliInria, France
CC-2017-papers10:55 - 11:20
Swarnendu BiswasUniversity of Texas at Austin, Man CaoOhio State University, Minjia ZhangOhio State University, Michael D. BondOhio State University, Benjamin P. WoodWellesley College, USA
CC-2017-papers11:20 - 11:45
Jun ShirakoRice University, USA, Akihiro HayashiRice University, USA, Vivek SarkarRice University, USA
CC-2017-papers11:45 - 12:10