A Deep Learning Mapper (DLM) for Scheduling on Heterogeneous Systems

Nemirovsky, Daniel; Arkose, Tugberk; Marković, Nikola; Nemirovsky, Mario; Ünsal, Osman; Cristal, Adrián; Valero, Mateo

doi:10.1007/978-3-319-73353-1_1

Cited by 4 publications

(4 citation statements)

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“…An implementation approach is to modify the scheduler code within the OS to collect the attributes and also run the predictions using the trained machine learning algorithms. This is the approach taken in [18,19] and has been shown to produce around 30% performance improvements over state-of-the-art schedulers. Other examples of machine learning predictors being exploited by mechanisms to improve system performance are in the area of branch prediction [9] and cache line reusability [10,25].…”

Section: Exploiting the Predictorsmentioning

confidence: 99%

“…Recently, there has been pioneering studies conducted on applying machine/deep learning to CPU scheduling. In the works [18,19] artificial neural network performance predictors are used by the scheduler to improve the system throughput over a Linux based scheduler by over 30%. Other approaches to using machine/deep learning for scheduling has been to classify applications, as well as to identify process attributes and a program's execution history.…”

Section: Related Workmentioning

confidence: 99%

“…However, the few works that have done so in the areas of CPU scheduling [18,19], cache replacement [10,25], and branch prediction [9] have shown tremendous promise. These are but a few of the opportunities we foresee where machine learning could provide a significant advantage towards improving the efficiency of computer systems.…”

Section: Introductionmentioning

confidence: 99%

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General Guide to Applying Machine Learning to Computer Architecture

Nemirovsky

Arkose²,

Marković

et al. 2018

JSFI

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The resurgence of machine learning since the late 1990s has been enabled by significant advances in computing performance and the growth of big data. The ability of these algorithms to detect complex patterns in data which are extremely difficult to achieve manually, helps to produce effective predictive models. Whilst computer architects have been accelerating the performance of machine learning algorithms with GPUs and custom hardware, there have been few implementations leveraging these algorithms to improve the computer system performance. The work that has been conducted, however, has produced considerably promising results. The purpose of this paper is to serve as a foundational base and guide to future computer architecture research seeking to make use of machine learning models for improving system efficiency. We describe a method that highlights when, why, and how to utilize machine learning models for improving system performance and provide a relevant example showcasing the effectiveness of applying machine learning in computer architecture. We describe a process of data generation every execution quantum and parameter engineering. This is followed by a survey of a set of popular machine learning models. We discuss their strengths and weaknesses and provide an evaluation of implementations for the purpose of creating a workload performance predictor for different core types in an x86 processor. The predictions can then be exploited by a scheduler for heterogeneous processors to improve the system throughput. The algorithms of focus are stochastic gradient descent based linear regression, decision trees, random forests, artificial neural networks, and k-nearest neighbors.

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Section: Exploiting the Predictorsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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General Guide to Applying Machine Learning to Computer Architecture

Nemirovsky

Arkose²,

Marković

et al. 2018

JSFI

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“…Such predictive models may even comprise direct minimization of the estimated environmental impact of a calculation as the target quantity in the model. 16 ML has already successfully been applied, however, towards improving scheduling itself, 17 or entire compute work flows. 18,19 Furthermore, a potentially valuable application in the context of quantum chemistry may be the run time optimization of a given tensor contraction scheme on a specific hardware by predictive modelling techniques.…”

Section: Introductionmentioning

confidence: 99%

Machine learning the computational cost of quantum chemistry

Heinen

Schwilk

Rudorff

et al. 2020

Mach. Learn.: Sci. Technol.

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Computational quantum mechanics based molecular and materials design campaigns consume increasingly more high-performance compute resources, making improved job scheduling efficiency desirable in order to reduce carbon footprint or wasteful spending. We introduce quantum machine learning (QML) models of the computational cost of common quantum chemistry tasks. For single point, geometry optimization, and transition state calculations the out of sample prediction error of QML models of wall times decays systematically with training set size. We present numerical evidence for thousands of organic molecular systems including closed and open shell equilibrium structures, as well as transition states. Levels of electronic structure theory considered include B3LYP/def2-TZVP, MP2/6-311G(d), local CCSD(T)/VTZ-F12, CASSCF/VDZ-F12, and MRCISD+Q-F12/VDZ-F12. In comparison to conventional indiscriminate job treatment, QML based wall time predictions significantly improve job scheduling efficiency for all tasks after training on just thousands of molecules. Resulting reductions in CPU time overhead range from 10% to 90%.

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