Architectural Support for Uniprocessor and Multiprocessor Active Memory Systems
IEEE Transactions on Computers
Optimizing data permutations for SIMD devices
Proceedings of the 2006 ACM SIGPLAN conference on Programming language design and implementation
Auto-vectorization of interleaved data for SIMD
Proceedings of the 2006 ACM SIGPLAN conference on Programming language design and implementation
CUDA Memory Optimizations for Large Data-Structures in the Gravit Simulator
ICPPW '09 Proceedings of the 2009 International Conference on Parallel Processing Workshops
SAMS multi-layout memory: providing multiple views of data to boost SIMD performance
Proceedings of the 24th ACM International Conference on Supercomputing
Copperhead: compiling an embedded data parallel language
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
Editorial: Special issue editorial: Accelerators for high-performance computing
Journal of Parallel and Distributed Computing
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Scientific data is mostly multi-valued, e.g., coordinates, velocities, moments or feature components, and it comes in large quantities. The data layout of such containers has an enormous impact on the achieved performance, however, layout optimization is very time-consuming and error-prone because container access syntax in standard programming languages is not sufficiently abstract. This means that changing the data layout of a container necessitates syntax changes in all parts of the code where the container is used. Object oriented languages allow to solve this problem by hiding the data layout behind a class interface. However, the additional coding effort is enormous in comparison to a simple structure. A clever coding pattern, previously presented by the author, significantly reduces the code overhead, however, it relies heavily on advanced C++ features, a language that is not supported on most accelerators. This paper develops a concise macro based solution that requires only support for structures and unions and can therefore be utilized in OpenCL, a widely supported programming language for parallel processors. This enables the development of high performance code without an a-priori commitment to a certain layout and includes the possibility to optimize it subsequently. This feature is used to identify the best data layouts for different processing patterns of multi-valued containers on a multi-GPU system.