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• W2022931154 startingPage "2194" @default.
• W2022931154 abstract "By exploiting data-level parallelism, Graphics Processing Units (GPUs) have become a high-throughput, general purpose computing platform. Many real-world applications especially those following a stream processing pattern, however, feature interleaved task-pipelined and data parallelism. Current GPUs are ill equipped for such applications due to the insufficient usage of computing resources and/or the excessive off-chip memory traffic. In this paper, we focus on microarchitectural enhancements to enable task-pipelined execution of data-parallel kernels on GPUs. We propose an efficient adaptive dynamic scheduling mechanism and a moderately modified L2 design. With minor hardware overhead, our techniques orchestrate both task-pipeline and data parallelisms in a unified manner. Simulation results derived by a cycle-accurate simulator on real-world applications prove that the proposed GPU microarchitecture improves the computing throughput by 18% and reduces the overall accesses to off-chip GPU memory by 13%." @default.
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• W2022931154 date "2013-01-01" @default.
• W2022931154 modified "2023-10-03" @default.
• W2022931154 title "Exploiting the Task-Pipelined Parallelism of Stream Programs on Many-Core GPUs" @default.
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• W2022931154 doi "https://doi.org/10.1587/transinf.e96.d.2194" @default.
• W2022931154 hasPublicationYear "2013" @default.
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