Variability-Guided Performance Optimization

The past few decades have seen software and hardware growing more heterogeneous and layered in abstractions. This trend produced many benefits for hiding complexity and increasing efficiency and modularity. But it also makes reasoning about performance and identifying its underlying factors more challenging because of the presence of performance variability. Moreover, performance variability can prevent synchronous applications from scaling and server applications from meeting service-level agreements. In this paper, we present a variability-guided optimization (VGO) workflow that leverages information in performance distributions to optimize performance, and more importantly, to reduce performance variability. It works by uncovering software, hardware, and compiler factors associated with specific aspects of variability and then suggesting measures for reducing it. Our experimental evaluation on a set of CPU and GPU benchmarks and applications shows that our tool successfully reduces the standard deviation and coefficient of variation of application run times by 0.374× and 0.444×, while also reducing mean run time by 0.843×. This technique enables tuning applications and their environments, improving their performance and predictability.