🤖 AI Summary
This work addresses the challenge that state-dependent behaviors in modern computing environments—such as those introduced by adaptive system mechanisms—induce time-dependent biases in traditional software benchmarking, undermining reliable performance comparisons. The paper reframes benchmarking as a decision problem aimed at identifying the fastest program and introduces an experimental paradigm centered on pairwise performance comparisons, thereby avoiding strong assumptions about modeling system dynamics. By leveraging contrastive estimators, consistent statistical inference, and test strategies under finite evaluation budgets, the approach eliminates program-specific biases without relying on absolute performance metrics. The method provides asymptotic guarantees for correct decisions in stateful environments, offering a robust and reliable benchmarking framework for performance-sensitive software development.
📝 Abstract
In the perpetual pursuit of performance, modern computing systems rely ever more on stateful mechanisms to accommodate the dynamics of workloads and physical environments, bolstering efficiency but confounding benchmarking and thereby the optimization of software. Indeed, by their nature, adaptive mechanisms introduce temporal dependencies between measurements and render naive estimators of individual program performance biased. Observing that rectifying such biases necessitates speculative assumptions about system dynamics, we call for prioritizing performance differentials over absolute measures and formalize software benchmarking as the decision problem of identifying the fastest program, for which relative knowledge suffices. To this end, we propose simple experiment designs admitting consistent estimators of contrasts, whereby program-specific biases cancel under tenable assumptions. These designs asymptotically yield the correct decision and afford a robust methodology for finite-budget benchmarking in stateful environments, bearing broad implications for the development of performance-sensitive software.