BibTeX

@inproceedings{AVOSCWTGLE19,
	author	 = {Nabeeh Jum'ah and Julian Kunkel},
	title	 = {{Automatic Vectorization of Stencil Codes with the GGDML Language Extensions}},
	year	 = {2019},
	booktitle	 = {{Workshop on Programming Models for SIMD/Vector Processing
(WPMVP'19), February 16, 2019, Washington, DC, USA}},
	editor	 = {},
	publisher	 = {ACM},
	address	 = {New York, NY, USA},
	series	 = {WPMVP},
	pages	 = {1--7},
	conference	 = {WPMVP-2019},
	organization	 = {PPoPP 2019},
	location	 = {Washington DC, USA},
	isbn	 = {978-1-4503-6291-7/19/02},
	doi	 = {https://doi.org/10.1145/3303117.3306160},
	abstract	 = {Partial differential equation (PDE) solvers are important for many applications. PDE solvers execute kernels which apply stencil operations over 2D and 3D grids. As PDE solvers and stencil codes are widely used in performance critical applications, they must be well optimized. Stencil computations naturally depend on neighboring grid elements. Therefore, data locality must be exploited to optimize the code and to better use the memory band- width – at the same time, vector processing capabilities of the processor must be utilized. In this work, we investigate the effectiveness of using high-level language extensions to exploit SIMD and vectorization features of multi-core processors and vector engines. We write a prototype application using the GGDML high-level language extensions, and translate the high-level code with different configurations to investigate the efficiency of the language extensions and the source-to-source translation process to exploit the vector units of the multi-core processors and the vector engines. The conducted experiments demonstrate the effectiveness of the language extensions and the translation tool to generate vectorized codes, which makes use of the natural data locality of stencil computations.},
	url	 = {https://ppopp19.sigplan.org/home/WPMVP-2019},
}