Improving backfilling by using machine learning to predict running times

Gaussier, Éric; Glesser, David; Reis, Valentin; Trystram, Denis

doi:10.1145/2807591.2807646

Cited by 80 publications

(51 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several works use this method in the context of HPC, in particular [29,16], hoping that better job runtime estimations should improve the scheduling [9]. Some algorithms estimate runtime distributions model and choose jobs using probabilistic integration procedures [22].…”

Section: Data-aware Resource Managementmentioning

confidence: 99%

See 1 more Smart Citation

Tuning EASY-Backfilling Queues

Lelong

Reis

Trystram

2018

Job Scheduling Strategies for Parallel Processing

Self Cite

View full text Add to dashboard Cite

Abstract. EASY-Backfilling is a popular scheduling heuristic for allocating jobs in large scale High Performance Computing platforms. While its aggressive reservation mechanism is fast and prevents job starvation, it does not try to optimize any scheduling objective per se. We consider in this work the problem of tuning EASY using queue reordering policies. More precisely, we propose to tune the reordering using a simulationbased methodology. For a given system, we choose the policy in order to minimize the average waiting time. This methodology departs from the First-Come, First-Serve rule and introduces a risk on the maximum values of the waiting time, which we control using a queue thresholding mechanism. This new approach is evaluated through a comprehensive experimental campaign on five production logs. In particular, we show that the behavior of the systems under study is stable enough to learn a heuristic that generalizes in a train/test fashion. Indeed, the average waiting time can be reduced consistently (between 11% to 42% for the logs used) compared to EASY, with almost no increase in maximum waiting times. This work departs from previous learning-based approaches and shows that scheduling heuristics for HPC can be learned directly in a policy space.

show abstract

Section: Data-aware Resource Managementmentioning

confidence: 99%

“…However, these works do not address the duality between the cumulative and maximal scheduling costs, as mentionned in [16].…”

Section: Data-aware Resource Managementmentioning

confidence: 99%

Tuning EASY-Backfilling Queues

Lelong

Reis

Trystram

2018

Job Scheduling Strategies for Parallel Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…3.1). Please note that a single paper may target more than one architecture (for instance, [36,60]) during the considered periods of time. The optimization of grid computing systems using machine learning and meta-heuristic approaches has received less attention, whereas optimization of cloud computing systems has received attention during the period 2012-2017.…”

Section: Classification Based On Architecture Software Optimization mentioning

confidence: 99%

“…Artificial neural network (ANN) [30,41,43,44], regression (LR, QR, PR) [9,24,36,52,54], support vector machines (SVM) [23,51,76], and decisiontrees (DT) [16, …”

Section: Rq2: Software Optimization Algorithms Used For Run-time Dynamentioning

confidence: 99%

“…Memeti and Pllana [64] can determine a near-optimal workload distribution on heterogeneous system, which results in performance improvement of up to 35.6× compared to sequential version. Thomas et al [91] show that a performance accuracy between 86 and 100% is capable to dynamically optimize the execution time by choosing the most suitable algorithm in a given context Regression Gaussier et al [36] can predict the execution time, which help to achieve up to 28% makespan reduction. Zhang et al [103] show performance improvement up to 27% when using regression techniques to predict the optimal number of threads and scheduling policy.…”

Section: Support Vector Machinesmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

A data‐driven approach to modeling power consumption for a hybrid supercomputer

Sîrbu

Babaoğlu

2018

Concurrency and Computation

View full text Add to dashboard Cite

Summary Power consumption of current High Performance Computing systems has to be reduced by at least one order of magnitude before they can be scaled up towards ExaFLOP performance. While we can expect novel hardware technologies and architectures to contribute towards this goal, significant advances have to come also from software technologies such as proactive and power‐aware scheduling, resource allocation, and fault‐tolerant computing. Development of these software technologies in turn relies heavily on our ability to model and accurately predict power consumption in large computing systems. In this paper, we present a data‐driven model of power consumption for a hybrid supercomputer (which held the top spot in the Green500 ranking in June 2013) that combines CPU, GPU, and MIC technologies to achieve high levels of energy efficiency. Our model takes as input workload characteristics—the number and location of resources that are used by each job at a certain time—and calculates a predicted power consumption at the system level. The model is application‐code‐agnostic and is based solely on a data‐driven predictive approach, where log data describing the past jobs in the system are employed to estimate future power consumption. For this, three different model components are developed and integrated. The first employs support vector regression to predict power usage for jobs before these are started. The second uses a simple heuristic to predict the length of jobs, again before they start. The two predictions are then combined to estimate power consumption due to the job at all computational elements in the system. The third component is a linear model that takes as input the power consumption at the computing units and predicts system‐wide power consumption. Our method achieves highly‐accurate predictions starting solely from workload information and user histories. The model can be applied to power‐aware scheduling and power capping: alternative workload dispatching configurations can be evaluated from a power perspective and more efficient ones can be selected. The methodology outlined here can be easily adapted to other HPC systems where the same types of log data are available.

show abstract

Improving backfilling by using machine learning to predict running times

Cited by 80 publications

References 28 publications

Tuning EASY-Backfilling Queues

Tuning EASY-Backfilling Queues

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

A data‐driven approach to modeling power consumption for a hybrid supercomputer

Contact Info

Product

Resources

About