Fractal: Joint Multi-Level Sparse Pattern Tuning of Accuracy and Performance for DNN Pruning
Authors: 
Yue Guan, Changming Yu, Yangjie Zhou, Jingwen Leng, Chao Li, Minyi GuoAuthors Info & Claims
Pages 416 - 430
Abstract
Model pruning, which eliminates redundant parameters and reduces computational complexity, emerges as a viable strategy for efficient deep neural network (DNN) deployment. Owing to the irregular memory access and computation patterns in the sparse DNN models after pruning, existing arts have suggested various structured sparse patterns to enhance sparse DNN performance. In this work, we propose a unique perspective of understanding existing sparse pattern design as computation-skipping after tiling the tensor computation into multi-level hierarchies. This unified perspective opens up a new design space of multi-level sparse tiling to maximize the sparsity benefits of DNNs, as opposed to the single-level choice in current practices. We present Fractal, an auto-tuning system for sparse patterns that identifies the optimal multi-level sparse tiling pattern. We introduce PatternIR, a novel high-level intermediate representation (IR), to express a diverse range of multi-level sparse patterns. By leveraging insights from prior dense operator optimizations, we translate PatternIR into low-level compiler IRs, facilitating further operator optimization and code generation. Our evaluations demonstrate that Fractal yields substantial speedups of up to on average 3.16× on CUDA Core, 2.52× on TensorCore of GPUs compared to the state-of-art dense baseline under 75% sparsity while upholding minimal accuracy degradation compared to prior sparse operator libraries.
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Index Terms
- Fractal: Joint Multi-Level Sparse Pattern Tuning of Accuracy and Performance for DNN Pruning
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Published In
April 2024
1106 pages
ISBN:9798400703867
DOI:10.1145/3620666
- General Chairs:
- Nael Abu-Ghazaleh,
- Rajiv Gupta,
- Program Chairs:
- Madan Musuvathi,
- Dan Tsafrir
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs International 4.0 License.
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Published: 27 April 2024
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- National Natural Science Foundation of China (NSFC)
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ASPLOS '24: 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3
April 27 - May 1, 2024
CA, La Jolla, USA
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