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Distributed memory machines equipped with CPUs and GPUs (hybrid computingnodes) are hard to program because of the multiple layers of memory andheterogeneous computing configurations. In this paper, we introduce a regiontemplate abstraction for the efficient management of common data types used inanalysis of large datasets of high resolution images on clusters of hybridcomputing nodes. The region template provides a generic container template forcommon data structures, such as points, arrays, regions, and object sets,within a spatial and temporal bounding box. The region template abstractionenables different data management strategies and data I/O implementations,while providing a homogeneous, unified interface to the application for datastorage and retrieval. The execution of region templates applications iscoordinated by a runtime system that supports efficient execution in hybridmachines. Region templates applications are represented as hierarchicaldataflow in which each computing stage may be represented as another dataflowof finer-grain tasks. A number of optimizations for hybrid machines areavailable in our runtime system, including performance-aware scheduling formaximizing utilization of computing devices and techniques to reduce impact ofdata transfers between CPUs and GPUs. An experimental evaluation on astate-of-the-art hybrid cluster using a microscopy imaging study shows thatthis abstraction adds negligible overhead (about 3%) and achieves goodscalability.

Region Templates: Data Representation and Management for Large-Scale Image Analysis