使用抽象内核仿真的gpu性能建模

Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming Pub Date : 2018-02-10 DOI:10.1145/3178487.3178524

Changwan Hong, Aravind Sukumaran-Rajam, Jinsung Kim, P. Rawat, S. Krishnamoorthy, L. Pouchet, F. Rastello, P. Sadayappan

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引用次数: 1

摘要

GPU内核的性能建模是一个重大的挑战。在本文中，我们通过抽象内核仿真以及资源的延迟/间隙建模开发了一种新的gpu性能建模方法。在Rodinia套件的所有基准测试上的实验结果表明，在多个GPU平台上预测执行时间具有良好的准确性。

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Performance modeling for GPUs using abstract kernel emulation

Performance modeling of GPU kernels is a significant challenge. In this paper, we develop a novel approach to performance modeling for GPUs through abstract kernel emulation along with latency/gap modeling of resources. Experimental results on all benchmarks from the Rodinia suite demonstrate good accuracy in predicting execution time on multiple GPU platforms.

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Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

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