Scalable Loop Self-Scheduling Schemes for Large-Scale Clusters and Cloud Systems

Han, Yiming; Chronopoulos, Anthony T.

doi:10.1007/s10766-016-0434-5

Cited by 8 publications

(2 citation statements)

References 29 publications

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“…So far, several loop scheduling strategies have been introduced to address Loop Scheduling Problem (LSP), [3][4][5][6][7][8][9] and they mainly rely on two techniques. In the first one, called on-demand scheduling, iterations are assigned on-the-fly at runtime, so that load imbalance and runtime variations may be dynamically handled.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive performance evaluation of the BinLPT workload‐aware loop scheduler

Penna

Gomes

Castro

et al. 2019

Concurrency and Computation

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Summary Workload‐aware loop schedulers were introduced to deliver better performance than classical loop scheduling strategies. However, they presented limitations such as inflexible built‐in workload estimators and suboptimal chunk scheduling. Targeting these challenges, we proposed previously a workload‐aware scheduling strategy called BinLPT, which relies on three features: (i) user‐supplied estimations of the workload of the loop; (ii) a greedy heuristic that adaptively partitions the iteration space in several chunks; and (iii) a scheduling scheme based on the Longest Processing Time (LPT) rule and on‐demand technique. In this paper, we present two new contributions to the state‐of‐the‐art. First, we introduce a multiloop support feature to BinLPT, which enables the reuse of estimations across loops. Based on this feature, we integrated BinLPT into a real‐world elastodynamics application, and we evaluated it running on a supercomputer. Second, we present an evaluation of BinLPT using simulations as well as synthetic and application kernels. We carried out this analysis on a large‐scale NUMA machine under a variety of workloads. Our results revealed that BinLPT is better at balancing the workloads of the loop iterations and this behavior improves as the algorithmic complexity of the loop increases. Overall, BinLPT delivers up to 37.15% and 9.11% better performance than well‐known loop scheduling strategies, for the application kernels and the elastodynamics simulation, respectively.

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Section: Introductionmentioning

confidence: 99%

A comprehensive performance evaluation of the BinLPT workload‐aware loop scheduler

Penna

Gomes

Castro

et al. 2019

Concurrency and Computation

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show abstract

“…Load balancing by considering the current status of all the available resources will solve the problem of inefficient utilization of resources. A scalable distributed loop self‐scheduling scheme is a load balancing method with reduced communication overhead. Although the system is scalable, it is only for the homogeneous clusters.…”

Section: Related Workmentioning

confidence: 99%

Service‐level agreement–aware scheduling and load balancing of tasks in cloud

Babu¹,

Samuel

2019

Softw Pract Exp

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Summary Cloud computing is an innovative computing paradigm designed to provide a flexible and low‐cost way to deliver information technology services on demand over the Internet. Proper scheduling and load balancing of the resources are required for the efficient operations in the distributed cloud environment. Since cloud computing is growing rapidly and customers are demanding better performance and more services, scheduling and load balancing of the cloud resources have become very interesting and important area of research. As more and more consumers assign their tasks to cloud, service‐level agreements (SLAs) between consumers and providers are emerging as an important aspect. The proposed prediction model is based on the past usage pattern and aims to provide optimal resource management without the violations of the agreed service‐level conditions in cloud data centers. It considers SLA in both the initial scheduling stage and in the load balancing stage, and it looks into different objectives to achieve the minimum makespan, the minimum degree of imbalance, and the minimum number of SLA violations. The experimental results show the effectiveness of the proposed system compared with other state‐of‐the‐art algorithms.

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An HPC hybrid parallel approach to the experimental analysis of Fermat’s theorem extension to arbitrary dimensions on heterogeneous computer systems

2021

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Scalable Loop Self-Scheduling Schemes for Large-Scale Clusters and Cloud Systems

Cited by 8 publications

References 29 publications

A comprehensive performance evaluation of the BinLPT workload‐aware loop scheduler

A comprehensive performance evaluation of the BinLPT workload‐aware loop scheduler

Service‐level agreement–aware scheduling and load balancing of tasks in cloud

An HPC hybrid parallel approach to the experimental analysis of Fermat’s theorem extension to arbitrary dimensions on heterogeneous computer systems

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