Article

Autotuning Stencil-Based Computations on GPUs

Authors:

Azamat Mametjanov,

Daniel Lowell,

Ching-Chen Ma, and

Boyana NorrisAuthors Info & Claims

CLUSTER '12: Proceedings of the 2012 IEEE International Conference on Cluster Computing

September 2012

Pages 266 - 274

https://doi.org/10.1109/CLUSTER.2012.46

Published: 24 September 2012 Publication History

Abstract

Finite-difference, stencil-based discretization approaches are widely used in the solution of partial differential equations describing physical phenomena. Newton-Krylov iterative methods commonly used in stencil-based solutions generate matrices that exhibit diagonal sparsity patterns. To exploit these structures on modern GPUs, we extend the standard diagonal sparse matrix representation and define new matrix and vector data types in the PETSc parallel numerical toolkit. We create tunable CUDA implementations of the operations associated with these types after identifying a number of GPU-specific optimizations and tuning parameters for these operations. We discuss our implementation of GPU auto tuning capabilities in the Orio framework and present performance results for several kernels, comparing them with vendor-tuned library implementations.

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Lou MMuller S(2024)Automatic Static Analysis-Guided Optimization of CUDA KernelsProceedings of the 15th International Workshop on Programming Models and Applications for Multicores and Manycores10.1145/3649169.3649249(11-21)Online publication date: 3-Mar-2024
https://dl.acm.org/doi/10.1145/3649169.3649249
Lawson JGoli M(2021)Performance portability through machine learning guided kernel selection in SYCL librariesParallel Computing10.1016/j.parco.2021.102813107:COnline publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1016/j.parco.2021.102813
Stoltzfus LHagedorn BSteuwer MGorlatch SDubach C(2019)Tiling Optimizations for Stencil Computations Using Rewrite Rules in LiftACM Transactions on Architecture and Code Optimization10.1145/336885816:4(1-25)Online publication date: 26-Dec-2019
https://dl.acm.org/doi/10.1145/3368858
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Published In

CLUSTER '12: Proceedings of the 2012 IEEE International Conference on Cluster Computing

September 2012

630 pages

ISBN:9780769548074

Publisher

IEEE Computer Society

United States

Publication History

Published: 24 September 2012

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Lou MMuller S(2024)Automatic Static Analysis-Guided Optimization of CUDA KernelsProceedings of the 15th International Workshop on Programming Models and Applications for Multicores and Manycores10.1145/3649169.3649249(11-21)Online publication date: 3-Mar-2024
https://dl.acm.org/doi/10.1145/3649169.3649249
Lawson JGoli M(2021)Performance portability through machine learning guided kernel selection in SYCL librariesParallel Computing10.1016/j.parco.2021.102813107:COnline publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1016/j.parco.2021.102813
Stoltzfus LHagedorn BSteuwer MGorlatch SDubach C(2019)Tiling Optimizations for Stencil Computations Using Rewrite Rules in LiftACM Transactions on Architecture and Code Optimization10.1145/336885816:4(1-25)Online publication date: 26-Dec-2019
https://dl.acm.org/doi/10.1145/3368858
Chen YCheng SKuo THung CChen QXie XEsposito CHuang JPark JZhang Q(2019)Performance tuning case study on graphics processing unit-accelerated monte carlo simulations for proton therapyProceedings of the Conference on Research in Adaptive and Convergent Systems10.1145/3338840.3355638(1-6)Online publication date: 24-Sep-2019
https://dl.acm.org/doi/10.1145/3338840.3355638
Liu JTan GLuo YLi JMo ZSun N(2019)An Autotuning Protocol to Rapidly Build AutotunersACM Transactions on Parallel Computing10.1145/32915275:2(1-25)Online publication date: 4-Jan-2019
https://dl.acm.org/doi/10.1145/3291527
Garvey JAbdelrahman T(2018)A Strategy for Automatic Performance Tuning of Stencil Computations on GPUsScientific Programming10.1155/2018/60930542018Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1155/2018/6093054
Hagedorn BStoltzfus LSteuwer MGorlatch SDubach CKnoop JSchordan MJohnson TO'Boyle M(2018)High performance stencil code generation with LiftProceedings of the 2018 International Symposium on Code Generation and Optimization10.1145/3168824(100-112)Online publication date: 24-Feb-2018
https://dl.acm.org/doi/10.1145/3168824
Yuan LZhang YGuo PHuang SMohr BRaghavan P(2017)Tessellating stencilsProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3126908.3126920(1-13)Online publication date: 12-Nov-2017
https://dl.acm.org/doi/10.1145/3126908.3126920
Luo YTan GMo ZSun NBhuyan LChong FSarkar V(2015)FASTProceedings of the 29th ACM on International Conference on Supercomputing10.1145/2751205.2751214(187-196)Online publication date: 8-Jun-2015
https://dl.acm.org/doi/10.1145/2751205.2751214
White ALee S(2014)Derivation of optimal input parameters for minimizing execution time of matrix-based computations on a GPUParallel Computing10.1016/j.parco.2014.09.00240:10(628-645)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1016/j.parco.2014.09.002

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

Optimizing stencil application on multi-thread GPU architecture using stream programming model

Optimizing symmetric dense matrix-vector multiplication on GPUs

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