Data layout optimization for multi-valued containers in OpenCL
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.
data layout multi-valued opencl multi-gpu multi-component array of structures aos structure of arrays soa array of structures of arrays asa