SC20 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Zerospy: Exploring Software Inefficiency with Redundant Zeros

Authors: Xin You, Hailong Yang, Zhongzhi Luan, and Depei Qian (Beihang University) and Xu Liu (North Carolina State University)

Abstract: Redundant zeros cause inefficiencies in which the zero values are loaded and computed repeatedly, resulting in unnecessary memory traffic and identity computation that waste memory bandwidth and CPU resources. Optimizing compilers is difficult in eliminating these zero-related inefficiencies due to limitations in static analysis. Hardware approaches, in contrast, optimize inefficiencies without code modification, but are not widely adopted in commodity processors. In this paper, we propose ZeroSpy: a fine-grained profiler to identify redundant zeros caused by both inappropriate use of data structures and useless computation. ZeroSpy also provides intuitive optimization guidance by revealing the locations where the redundant zeros happen in source lines and calling contexts. The experimental results demonstrate ZeroSpy is capable of identifying redundant zeros in programs that have been highly optimized for years. Based on the optimization guidance revealed by ZeroSpy, we can achieve significant speedups after eliminating redundant zeros.

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