Abstract
Active data structures support operations that may affect a large number of elements of an aggregate data structure. They are well suited for extremely fine grain parallel systems, including circuit parallelism. General purpose GPUs were designed to support regular graphics algorithms, but their intermediate level of granularity makes them potentially viable also for active data structures. We consider the characteristics of active data structures and discuss the feasibility of implementing them on GPGPUs. We describe the GPU implementations of two such data structures, extensible sparse functional (ESF) arrays and index intervals. We measure their performance and discuss the potential of active data structures as an unconventional programming model that can exploit the capabilities of emerging fine grain architectures such as GPUs. © 2014 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
O’Donnell, J. T., Hall, C., & Monro, S. (2014). Active data structures on GPGPUs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8374 LNCS, pp. 875–884). Springer Verlag. https://doi.org/10.1007/978-3-642-54420-0_85
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