MODESTO: Data-centric Analytic Optimization of Complex Stencil programs on Heterogeneous Architectures

Tobias Gysi, Tobias Grosser, Torsten Hoefler

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Code transformations, such as loop tiling and loop fusion, are of key importance for the efficient implementation of stencil computations. However, their direct application to a large code base is costly and severely impacts program maintainability. While recently introduced domain-specific languages facilitate the application of such transformations, they typically still require manual tuning or auto-tuning techniques to select the transformations that yield optimal performance. In this paper, we introduce MODESTO, a model-driven stencil optimization framework, that for a stencil program suggests program transformations optimized for a given target architecture. Initially, we review and categorize data locality transformations for stencil programs and introduce a stencil algebra that allows the expression and enumeration of different stencil program implementation variants. Combining this algebra with a compile-time performance model, we show how to automatically tune stencil programs. We use our framework to model the STELLA library and optimize kernels used by the COSMO atmospheric model on multi-core and hybrid CPU-GPU architectures. Compared to naive and expert-tuned variants, the automatically tuned kernels attain a 2.0-3.1x and a 1.0-1.8x speedup respectively.
Original languageEnglish
Title of host publicationProceedings of the 29th ACM on International Conference on Supercomputing
Number of pages10
ISBN (Electronic)9781450335591
Publication statusPublished - 8 Jun 2015
Event2015 International Conference on Supercomputing - Newport Beach, United States
Duration: 8 Jun 201511 Jun 2015

Publication series



Conference2015 International Conference on Supercomputing
Abbreviated titleICS 2015
Country/TerritoryUnited States
CityNewport Beach
Internet address


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