Parametric GPU code generation for affine loop programs
Document Type
Conference Proceeding
Publication Date
1-1-2014
Abstract
Partitioning a parallel computation into finite-sized chunks for effective mapping onto a parallel machine is a critical concern for source-to-source compilation. In the context of OpenCL and CUDA, this translates to the definition of a uniform hyper-rectangular partitioning of the parallel execution space where each partition is subject to a fine-grained distribution of resources that has a direct yet hard to estimate impact on performance. This paper develops the first compilation scheme for generating parametrically tiled codes for affine loop programs on GPUs, which facilitates run-time exploration of partitioning parameters as a fast and portable way of finding the ones that yield maximum performance. Our approach is based on a parametric tiling scheme for producing wavefronts of parallel rectangular partitions of parametric size and a novel runtime system that manages wavefront execution and local memory usage dynamically through an inspector-executor mechanism. An experimental evaluation demonstrates the effectiveness of our approach for wavefront as well as rectangularly-parallel partitionings.
Publication Source (Journal or Book title)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
First Page
136
Last Page
151
Recommended Citation
Konstantinidis, A., Kelly, P., Ramanujam, J., & Sadayappan, P. (2014). Parametric GPU code generation for affine loop programs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8664, 136-151. https://doi.org/10.1007/978-3-319-09967-5_8