%0 Journal Article
%T Acceleration of tensor
%A Ali Karakus
%A Kasia £¿wirydowicz
%A Noel Chalmers
%A Tim Warburton
%J The International Journal of High Performance Computing Applications
%@ 1741-2846
%D 2019
%R 10.1177/1094342018816368
%X This article is devoted to graphics processing unit (GPU) kernel optimization and performance analysis of three tensor-product operations arising in finite element methods. We provide a mathematical background to these operations and implementation details. Achieving close to peak performance for these operators requires extensive optimization because of the operators¡¯ properties: low arithmetic intensity, tiered structure, and the need to store intermediate results during the kernel execution. We give a guided overview of optimization strategies and we present a performance model that allows us to compare the efficacy of these optimizations against an empirically calibrated roofline
%K Finite element method
%K elliptic problem
%K hexahedral elements
%K matrix¨Cvector product
%K GPU tensor operations
%K NVIDIA Tesla P100
%U https://journals.sagepub.com/doi/full/10.1177/1094342018816368