Collective Mind: Towards Practical and Collaborative Auto-Tuning

Author:

Fursin Grigori1,Miceli Renato2,Lokhmotov Anton3,Gerndt Michael4,Baboulin Marc1,Malony Allen D.5,Chamski Zbigniew6,Novillo Diego7,Del Vento Davide8

Affiliation:

1. Inria and University of Paris-Sud, Orsay, France

2. University of Rennes 1, Rennes, France and ICHEC, Dublin, Ireland

3. ARM, Cambridge, UK

4. Technical University of Munich, Munich, Germany

5. University of Oregon, Eugene, OR, USA

6. Infrasoft IT Solutions, Plock, Poland

7. Google Inc., Toronto, Canada

8. National Center for Atmospheric Research, Boulder, CO, USA

Abstract

Empirical auto-tuning and machine learning techniques have been showing high potential to improve execution time, power consumption, code size, reliability and other important metrics of various applications for more than two decades. However, they are still far from widespread production use due to lack of native support for auto-tuning in an ever changing and complex software and hardware stack, large and multi-dimensional optimization spaces, excessively long exploration times, and lack of unified mechanisms for preserving and sharing of optimization knowledge and research material. We present a possible collaborative approach to solve above problems using Collective Mind knowledge management system. In contrast with previous cTuning framework, this modular infrastructure allows to preserve and share through the Internet the whole auto-tuning setups with all related artifacts and their software and hardware dependencies besides just performance data. It also allows to gradually structure, systematize and describe all available research material including tools, benchmarks, data sets, search strategies and machine learning models. Researchers can take advantage of shared components and data with extensible meta-description to quickly and collaboratively validate and improve existing auto-tuning and benchmarking techniques or prototype new ones. The community can now gradually learn and improve complex behavior of all existing computer systems while exposing behavior anomalies or model mispredictions to an interdisciplinary community in a reproducible way for further analysis. We present several practical, collaborative and model-driven auto-tuning scenarios. We also decided to release all material atc-mind.org/repoto set up an example for a collaborative and reproducible research as well as our new publication model in computer engineering where experimental results are continuously shared and validated by the community.

Publisher

Hindawi Limited

Subject

Computer Science Applications,Software

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Explicating the mapping between big data and knowledge management: a systematic literature review and future directions;Benchmarking: An International Journal;2024-03-29

2. Performance Tuning via Lean Measurements for Acceleration of Network Functions Virtualization;IEEE/ACM Transactions on Networking;2023-02

3. Object Intersection Captures on Interactive Apps to Drive a Crowd-sourced Replay-based Compiler Optimization;ACM Transactions on Architecture and Code Optimization;2022-05-04

4. SRTuner: Effective Compiler Optimization Customization by Exposing Synergistic Relations;2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO);2022-04-02

5. Developer and user-transparent compiler optimization for interactive applications;Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation;2021-06-18

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