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• W2089672258 abstract "The pursuit of computational efficiency has led to the proliferation of throughput-oriented hardware, from GPUs to increasingly wide vector units on commodity processors and accelerators. This hardware is designed to efficiently execute data-parallel computations in a vectorized manner. However, many algorithms are more naturally expressed as divide-and-conquer, recursive, task-parallel computations. In the absence of data parallelism, it seems that such algorithms are not well suited to throughput-oriented architectures. This paper presents a set of novel code transformations that expose the data parallelism latent in recursive, task-parallel programs. These transformations facilitate straightforward vectorization of task-parallel programs on commodity hardware. We also present scheduling policies that maintain high utilization of vector resources while limiting space usage. Across several task-parallel benchmarks, we demonstrate both efficient vector resource utilization and substantial speedup on chips using Intel’s SSE4.2 vector units, as well as accelerators using Intel’s AVX512 units." @default.
• W2089672258 created "2016-06-24" @default.
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• W2089672258 date "2015-06-03" @default.
• W2089672258 modified "2023-10-17" @default.
• W2089672258 title "Efficient execution of recursive programs on commodity vector hardware" @default.
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• W2089672258 doi "https://doi.org/10.1145/2737924.2738004" @default.
• W2089672258 hasPublicationYear "2015" @default.
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