ZeroSpy: Exploring Software Inefficiency with Redundant Zeros Xin You∗, Hailong Yang∗y, Zhongzhi Luan∗, Depei Qian∗ and Xu Liuz Beihang University, China∗, North Carolina State University, USAz State Key Laboratory of Mathematical Engineering and Advanced Computing, Wuxi, Chinay fyouxin2015,hailong.yang,07680,
[email protected]∗,
[email protected] Abstract—Redundant zeros cause inefficiencies in which the 1 for(int i=0;i<1000;++i) { 2 A[i] = 0; B[i] = i; zero values are loaded and computed repeatedly, resulting in 3 } 4 ... unnecessary memory traffic and identity computation that waste 5 for(int i=0;i<1000;++i) memory bandwidth and CPU resources. Optimizing compilers 6 I C[i] = A[i]-B[i]; is difficult in eliminating these zero-related inefficiencies due to Listing 1. An example code working on redundant zeros, where all variables are 32-bit integers. limitations in static analysis. Hardware approaches, in contrast, optimize inefficiencies without code modification, but are not enough to fulfill the computation, which yields better cache widely adopted in commodity processors. In this paper, we 1 propose ZeroSpy - a fine-grained profiler to identify redundant usage and vectorization potentials . zeros caused by both inappropriate use of data structures and Actually, a larger number of real-world applications (e.g., useless computation. ZeroSpy also provides intuitive optimization deep neural network and high-efficiency video coding) have guidance by revealing the locations where the redundant zeros already been reported to contain a significant amount of happen in source lines and calling contexts. The experimental redundant zeros and achieved significant speedup from the results demonstrate ZeroSpy is capable of identifying redundant zeros in programs that have been highly optimized for years.