Program Autotuning as a Service: Opportunities and Challenges

Abstract

Program autotuning is becoming an increasingly valuable tool for improving performance portability across diverse target architectures, exploring trade-offs between several criteria, or meeting quality of service requirements. Recent work on general autotuning frameworks enabled rapid development of domain-specific autotuners reusing common libraries of parameter types and search techniques. In this work we explore the use of such frameworks to develop general-purpose online services for program autotuning using the Software as a Service model. Beyond the common benefits of this model, the proposed approach opens up a number of unique opportunities, such as collecting performance data and utilizing it to improve further runs, or enabling remote online autotuning. However, the proposed autotuning as a service approach also brings in several challenges, such as accessing target systems, dealing with measurement latency, and supporting execution of user-provided code. This paper presents the first step towards implementing the proposed approach and addressing these challenges. We describe an implementation of generic autotuning service that can be used for tuning arbitrary programs on user-provided computing systems. The service is based on OpenTuner autotuning framework and runs on Everest platform that enables rapid development of computational web services. In contrast to OpenTuner, the service doesn't require installation of the framework, allows users to avoid writing code and supports efficient parallel execution of measurement tasks across multiple machines. The performance of the service is evaluated by using it for tuning synthetic and real programs.