Graph-Oriented Code Transformation Approach for Register-Limited Stencils on GPUs

Abstract

Stencil kernels play an important role in many scientific and engineering disciplines. With the development of numerical algorithms and the increasing requirements of accuracy, register-limited stencils containing massive variables and operations are widely used. However, these register-limited stencils consume vast resources when executing on GPUs. The excessive use of registers reduces the number of active threads dramatically, and consequently leads to a serious performance decline. To improve the performance of these register-limited stencils, we propose a DDG (data-dependency-graph) oriented code transformation approach in this paper. By analyzing, deleting and transforming the original stencil program on GPUs, our graph-oriented code transformation approach explores for the best trade-off between the calculation amount and the parallelism degree, and further achieves better performance. The graph-oriented code transformation approach is evaluated using the Weighted Nearly Analytic Discrete stencil, and the experimental result shows that a speedup of 2.16X can be achieved when compared with the original fairly-optimized implementation. To the best of our knowledge, our study takes the first step towards balancing the calculation amount and parallelism degree of the extremely register-limited stencils on GPUs.