Computer Science > Distributed, Parallel, and Cluster Computing
[Submitted on 9 Sep 2020 (v1), last revised 22 Sep 2020 (this version, v3)]
Title:Time-Based Roofline for Deep Learning Performance Analysis
View PDFAbstract:Deep learning applications are usually very compute-intensive and require a long run time for training and inference. This has been tackled by researchers from both hardware and software sides, and in this paper, we propose a Roofline-based approach to performance analysis to facilitate the optimization of these applications. This approach is an extension of the Roofline model widely used in traditional high-performance computing applications, and it incorporates both compute/bandwidth complexity and run time in its formulae to provide insights into deep learning-specific characteristics. We take two sets of representative kernels, 2D convolution and long short-term memory, to validate and demonstrate the use of this new approach, and investigate how arithmetic intensity, cache locality, auto-tuning, kernel launch overhead, and Tensor Core usage can affect performance. Compared to the common ad-hoc approach, this study helps form a more systematic way to analyze code performance and identify optimization opportunities for deep learning applications.
Submission history
From: Charlene Yang [view email][v1] Wed, 9 Sep 2020 23:29:04 UTC (644 KB)
[v2] Wed, 16 Sep 2020 07:11:36 UTC (646 KB)
[v3] Tue, 22 Sep 2020 21:51:45 UTC (646 KB)
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