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Compiler-based code generation and autotuning for geometric multigrid on GPU-accelerated supercomputers.

, , , , , and . Parallel Comput., (2017)

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Polyhedral Compiler Technology in Collaboration with Autotuning Important to Domain-Specific Frameworks for HPC., and . LCPC, volume 10136 of Lecture Notes in Computer Science, page 101-105. Springer, (2016)Towards making autotuning mainstream., , , , , , , , and . Int. J. High Perform. Comput. Appl., 27 (4): 379-393 (2013)Automating Compiler-Directed Autotuning for Phased Performance Behavior., , and . IPDPS Workshops, page 1362-1371. IEEE Computer Society, (2017)Compiler Optimizations and Attuning for Stencils and Geometric Multigrid.. University of Utah, USA, (2016)Exploiting reuse and vectorization in blocked stencil computations on CPUs and GPUs., , , , and . SC, page 52:1-52:44. ACM, (2019)FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference., , , , , , and . CoRR, (2021)Snowflake: A Lightweight Portable Stencil DSL., , , , , and . IPDPS Workshops, page 795-804. IEEE Computer Society, (2017)Compiler-based code generation and autotuning for geometric multigrid on GPU-accelerated supercomputers., , , , , and . Parallel Comput., (2017)Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications., , , , , , , , , and 18 other author(s). CoRR, (2018)SIMD code generation for stencils on brick decompositions., , , , and . PPoPP, page 423-424. ACM, (2018)