TrivialSpy: Identifying Software Triviality via Fine-grained and Dataflow-based Value Profiling

Trivial operations cause software inefficiencies that waste functional units and memory bandwidth for executing useless instructions. Although previous works have identified a significant amount of trivial operations in widely used programs, the proposed solutions only provide useful observations, other than actionable guid-ance to eliminate trivial operations for better performance. In this paper, we propose TrivialSpy - a fine-grained and dataflow-based value profiler to effectively identify software triviality with optimization potential estimation. With the help of dataflow analysis, TrivialSpy can detect software trivialities of heavy operation, triv-ial chain, and redundant backward slice. In addition, TrivialSpy can identify trivial breakpoints that combine multiple trivial conditions for more optimization opportunities. The evaluation results demonstrate TrivialSpy is capable of identifying software triviality in highly optimized programs. Based on the optimization guid-ance provided by TrivialSpy, we can achieve 52.09% performance speedup at maximum after eliminating trivial operations.