Predicting Unroll Factors Using Supervised Classification

Stephenson, Mark W.; Amarasinghe, Saman

doi:10.1109/cgo.2005.29

Cited by 136 publications

(137 citation statements)

References 15 publications

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“…Most of these works also require a large number of training runs. Stephenson et al [28] show more complementarity with collective optimization as program matching is solely based on static features.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Several research works have shown how machine-learning and statistical techniques [25,29,28,34] can be used to select or tune program transformations based on program features. Agakov et al [3] and Cavazos et al [8] use machine-learning to focus iterative search using either syntactic program features or dynamic hardware counters and multiple program transformations.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Over the years, the approach has been perfected with fast optimization space search techniques, sophisticated machine-learning algorithms and continuous optimization [25,29,28,34,3,8,32,23,21]. And, even though these different research works have demonstrated significant performance improvements, the technique is far from mainstream in production environments.…”

Section: Introductionmentioning

confidence: 99%

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Collective Optimization

Fursin

Temam

2009

High Performance Embedded Architectures and Compilers

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Abstract. Iterative compilation is an efficient approach to optimize programs on rapidly evolving hardware, but it is still only scarcely used in practice due to a necessity to gather a large number of runs often with the same data set and on the same environment in order to test many different optimizations and to select the most appropriate ones. Naturally, in many cases, users cannot afford a training phase, will run each data set once, develop new programs which are not yet known, and may regularly change the environment the programs are run on. In this article, we propose to overcome that practical obstacle using Collective Optimization, where the task of optimizing a program leverages the experience of many other users, rather than being performed in isolation, and often redundantly, by each user. Collective optimization is an unobtrusive approach, where performance information obtained after each run is sent back to a central database, which is then queried for optimizations suggestions, and the program is then recompiled accordingly. We show that it is possible to learn across data sets, programs and architectures in non-dynamic environments using static function cloning and run-time adaptation without even a reference run to compute speedups over the baseline optimization. We also show that it is possible to simultaneously learn and improve performance, since there are no longer two separate training and test phases, as in most studies. We demonstrate that extensively relying on competition among pairs of optimizations (program reaction to optimizations) provides a robust and efficient method for capturing the impact of optimizations, and for reusing this knowledge across data sets, programs and environments. We implemented our approach in GCC and will publicly disseminate it in the near future.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Collective Optimization

Fursin

Temam

2009

High Performance Embedded Architectures and Compilers

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“…Within the scope of this survey, scientific publications that use machine learning for code optimization at compile time include [1,17,34,35,57,69,87,95,99], whereas scientific publications that use meta-heuristics for code optimization include [21,88,92,93]. Table 4 lists the characteristics of the selected primary studies that address code optimization at compile time.…”

Section: Code Optimizationmentioning

confidence: 99%

“…It is critical to choose features that have significant impact on the prediction model. There are different feature selection techniques that can find features that contain the most useful information to distinguish between classes, for example mutual information score (MIS) [27], greedy feature selection [87], or information gain ratio [45].…”

Section: Machine Learningmentioning

confidence: 99%

Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review

et al. 2018

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While modern parallel computing systems offer high performance, utilizing these powerful computing resources to the highest possible extent demands advanced knowledge of various hardware architectures and parallel programming models. Furthermore, optimized software execution on parallel computing systems demands consideration of many parameters at compile-time and run-time. Determining the optimal set of parameters in a given execution context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning. In this paper, we undertake a systematic literature review to aggregate, analyze and classify the existing software optimization methods for parallel computing systems. We review approaches that use machine learning or meta-heuristics for software optimization at compile-time and run-time. we discuss challenges and future research directions. The results of this study may help to better understand the state-of-the-art techniques that use machine learning and meta-heuristics to deal with the complexity of software optimization for parallel computing systems. Furthermore, it may aid in understanding the limitations of existing approaches and identification of areas for improvement.

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Machine learning‐based auto‐tuning for enhanced performance portability of OpenCL applications

Falch

Elster

2016

Concurrency and Computation

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Summary Heterogeneous computing, combining devices with different architectures such as CPUs and GPUs, is rising in popularity and promises increased performance combined with reduced energy consumption. OpenCL has been proposed as a standard for programming such systems and offers functional portability. However, it suffers from poor performance portability, because applications must be retuned for every new device. In this paper, we use machine learning‐based auto‐tuning to address this problem. Benchmarks are run on a random subset of the tuning parameter spaces, and the results are used to build a machine learning‐based performance model. The model can then be used to find interesting subspaces for further search. We evaluate our method using five image processing benchmarks, with tuning parameter space sizes up to 2.3 M, using different input sizes, on several devices, including an Intel i7 4771 (Haswell) CPU, an Nvidia Tesla K40 GPU, and an AMD Radeon HD 7970 GPU. We compare different machine learning algorithms for the performance model. Our model achieves a mean relative error as low as 3.8% and is able to find solutions on average only 0.29% slower than the best configuration in some cases, evaluating less than 1.1% of the search space. The source code of our framework is available at https://github.com/acelster/ML‐autotuning.

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Predicting Unroll Factors Using Supervised Classification

Cited by 136 publications

References 15 publications

Collective Optimization

Collective Optimization

Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review

Machine learning‐based auto‐tuning for enhanced performance portability of OpenCL applications

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