PERI auto-tuning

Bailey, David H.; Chame, Jacqueline; Chen, Chun; Dongarra, Jack; Hall, Mary; Hollingsworth, Jeffrey K.; Hovland, Paul; Moore, Shirley; Seymour, Keith; Shin, Jaewook; Tiwari, Ananta; Williams, Samuel; You, Haihang

doi:10.1088/1742-6596/125/1/012089

Cited by 12 publications

(9 citation statements)

References 1 publication

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“…Recently, compiler-based techniques have even been extended to dynamic code generation [11], [33]. Also, projects like PERI [34] establish interfaces for the various tools involved in the auto-tuning process to enable future interoperability. The main difference between our work and all the compilerbased solutions mentioned above is our focus on multiobjective optimization.…”

Section: Related Workmentioning

confidence: 99%

A multi-objective auto-tuning framework for parallel codes

Jordan

Thoman

Durillo

et al. 2012

2012 International Conference for High Performance Computing, Networking, Storage and Analysis

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Auto-tuning has become increasingly popular for optimizing non-functional parameters of parallel programs. The typically large search space requires sophisticated techniques to find well performing parameter values in a reasonable amount of time. Different parts of a program often perform best with different parameter values. We therefore subdivide programs into several regions, and try to optimize the parameter values for each of those regions separately as opposed to setting the parameter values globally for the entire program. As this enlarges the search space even further, we have to extend existing auto-tuning techniques in order to obtain good results. In this paper we introduce a novel enhancement to the RS-GDE3 algorithm which is used to explore the search space for auto-tuning programs with multiple regions regarding several objectives. We have implemented our auto-tuner using the Insieme compiler and runtime system. In comparison to a non-optimized parallel version of the tested programs, our novel approach achieves up to 7.6, 10.5, and 61.6 fold improvements for three tuned objectives wall time, energy consumption, and resource usage, respectively.

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Section: Related Workmentioning

confidence: 99%

A multi-objective auto-tuning framework for parallel codes

Jordan

Thoman

Durillo

et al. 2012

2012 International Conference for High Performance Computing, Networking, Storage and Analysis

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“…Whaley and Dongarra [1998], Cooper et al [1999], Bodin et al [1998], Matteo and Johnson [1998], Cooper et al [2002], Fursin et al [2002], Kulkarni et al [2003], Triantafyllis et al [2003], Singer and Veloso [2000], Eigenmann [2004, 2006], Qasem et al [2006], Bailey et al [2008], and Hoste and Eeckhout [2008] demonstrated that optimizations search techniques can effectively improve performance of statically compiled programs on rapidly evolving architectures, thereby outperforming state-of-the-art compilers, albeit at the cost of a large number of exploration runs.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Over the years, the approach has been perfected with fast optimization space search techniques, sophisticated machinelearning algorithms, and continuous optimization [Voss and Eigenmann 2000;Monsifrot et al 2002;Stephenson et al 2003;Lu et al 2004;Lattner and Adve 2004;Franke et al 2005;Stephenson and Amarasinghe 2005;Zhao et al 2005;Agakov et al 2006;Qasem et al 2006;Cavazos et al 2007;Bailey et al 2008;Fursin et al 2008;Dubach et al 2009]. Nevertheless, empirical iterative optimization is far from mainstream in production environments.…”

Section: Introductionmentioning

confidence: 99%

Collective optimization

Fursin

Temam

2010

ACM Trans. Archit. Code Optim.

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Iterative optimization is a popular and efficient research approach to optimize programs using feedback-directed compilation. However, one of the key limitations that prevented widespread use in production compilers and day-to-day practice is the necessity to perform a large number of program runs with the same dataset and environment (architecture, OS, compiler) to test many different combinations of optimizations. In this article, we propose to overcome such a practical obstacle using collective optimization, where the task of optimizing a program or tuning default compiler optimization heuristic leverages the experience of many other users continuously, rather than being performed in isolation, and often redundantly, by each user. During this unobtrusive approach, performance information is sent to a central database after each run and statistically combined with the data from all users to suggest most profitable optimizations for a given program and an architecture, or to gradually improve default optimization level of a compiler for a given architecture.In this article, we address two key challenges of collective optimization. We show that it is possible to simultaneously learn and improve performance while avoiding long training phases. We also demonstrate how to use our approach with static compilers to learn optimizations across multiple datasets and architectures without even a reference run normally needed to compute speedups over the baseline optimization by using static function cloning and dynamic adaptation. We present a novel probabilistic approach based on competition among pairs of optimizations (program reaction to optimizations) to enable optimization knowledge reuse and achieve nearly the best possible iterative optimization performance. We implemented our technique in GCC (widespread production open-source compiler that supports multiple architectures) and connected it to a public collective optimization database at cTuning.org to gather profile and optimization data continuously and transparently in realistic environments ranging from desktop PCs and mobile systems to supercomputers and data centers.

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“…These approaches treat rapidly evolving computer system as a black box and explore program and architecture design and optimization spaces empirically [1,8,21,27,38,44,48,49,51,53,59,64,70,73,77,78].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Collective Mind: Towards Practical and Collaborative Auto-Tuning

Fursin

Miceli

Lokhmotov

et al. 2014

Scientific Programming

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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 at c-mind.org/repo to 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.

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PERI auto-tuning

Cited by 12 publications

References 1 publication

A multi-objective auto-tuning framework for parallel codes

A multi-objective auto-tuning framework for parallel codes

Collective optimization

Collective Mind: Towards Practical and Collaborative Auto-Tuning

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