Collective Tuning Initiative

TL;DR

The paper introduces the Collective Tuning Infrastructure (CTI), an open-source, modular system that automates and distributes program optimization using collaborative, machine learning, and iterative compilation techniques to enhance performance across diverse computing architectures.

Contribution

It presents a novel, integrated, plugin-based system for collaborative optimization that leverages collective experience and machine learning to improve performance and reproducibility in system tuning.

Findings

Can reduce execution time of some programs by more than a factor of 2

Automates and simplifies optimization, saving development time

Enables research on self-tuning and adaptive systems

Abstract

Computing systems rarely deliver best possible performance due to ever increasing hardware and software complexity and limitations of the current optimization technology. Additional code and architecture optimizations are often required to improve execution time, size, power consumption, reliability and other important characteristics of computing systems. However, it is often a tedious, repetitive, isolated and time consuming process. In order to automate, simplify and systematize program optimization and architecture design, we are developing open-source modular plugin-based Collective Tuning Infrastructure (CTI, http://cTuning.org) that can distribute optimization process and leverage optimization experience of multiple users. CTI provides a novel fully integrated, collaborative, "one button" approach to improve existing underperfoming computing systems ranging from embedded architectures to high-performance servers based on systematic iterative compilation, statistical collective optimization and machine learning. Our experimental results show that it is possible to reduce execution time (and code size) of some programs from SPEC2006 and EEMBC among others by more than a factor of 2 automatically. It can also reduce development and testing time considerably. Together with the first production quality machine learning enabled interactive research compiler (MILEPOST GCC) this infrastructure opens up many research opportunities to study and develop future realistic self-tuning and self-organizing adaptive intelligent computing systems based on systematic statistical performance evaluation and benchmarking. Finally, using common optimization repository is intended to improve the quality and reproducibility of the research on architecture and code optimization.