Scheduling Strategies for Cycle Scavenging in Multicluster Grid Systems

Sonmez, Ozan; Grundeken, Bart; Mohamed, Hashim; Iosup, Alexandru; Epema, Dick

doi:10.1109/ccgrid.2009.46

Cited by 14 publications

(15 citation statements)

References 21 publications

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“…Inspired by [21], we consider that this malleable applications is composed of an infinite number of singlenode tasks, each of duration d task . As described in Section 4, the PSA monitors its preemptive view.…”

Section: Parameter-sweep Application (Psa)mentioning

confidence: 99%

“…More general support for malleability can be found in KOALA [21]. Applications declare their minimum resource requirements, then the RMS divides the "extra" resources equally (equi-partitioning).…”

Section: Malleablementioning

confidence: 99%

“…In case the system is not congested (during a certain time interval), each application is assigned a view based on the number of resources that other applications leave unused (lines [19][20][21][22][23][24][25]. However, this should not be smaller that the equi-partition of the application.…”

Section: A43 Eqschedule()mentioning

confidence: 99%

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An RMS for Non-predictably Evolving Applications

Klein

Pérez

2011

2011 IEEE International Conference on Cluster Computing

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Non-predictably evolving applications are applications that change their resource requirements during execution. These applications exist, for example, as a result of using adaptive numeric methods, such as adaptive mesh refinement and adaptive particle methods. Increasing interest is being shown to have such applications acquire resources on the fly. However, current HPC Resource Management Systems (RMSs) only allow a static allocation of resources, which cannot be changed after it started. Therefore, non-predictably evolving applications cannot make efficient use of HPC resources, being forced to make an allocation based on their maximum expected requirements. This paper presents CooRMv2, an RMS which supports efficient scheduling of non-predictably evolving applications. An application can make "pre-allocations" to specify its peak resource usage. The application can then dynamically allocate resources as long as the pre-allocation is not outgrown. Resources which are pre-allocated but not used, can be filled by other applications. Results show that the approach is feasible and leads to a more efficient resource usage.Key-words: resource management; supercomputers; clusters; evolving applications; malleability. Un gestionnaire de ressources pour les applications évolutives Résumé :Les applications évolutives non-prédictibles sont des applications dont les besoins en ressources changent en cours d'exécution. Leurs comportements proviennent, par exemple, des méth-odes numériques adaptatives, comme le raffinement de maillage adaptatif et les méthodes de particules adaptatives. Il y a un intérêt croissant à ce que ce type d'application acquiert des ressources à la volée. Cependant, les gestionnaires de ressources haute performance (HPC) ne supportent qu'une allocation statique des ressources, qui ne peut donc pas changer après le démarrage de l'application. Par conséquence, les applications évolutives non-prédictibles ne peuvent pas utiliser les ressources HPC de manière efficace, car elles sont forcées de faire une demande de ressources en fonction de leurs pics de besoins.Cet article présente CooRMv2, un gestionnaire de ressources qui permet l'ordonnancement efficace d'applications évolutives non-prédictibles. Une application peut faire des « pré-allocations » pour exprimer ses pics de besoins en ressources. Ensuite, l'application peut dynamiquement allouer de manière sûre des ressources tant que la pré-allocation n'est pas dépassée. Les ressources pré-allouées, mais inutilisées, sont à la disposition des autres applications. Les résultats montrent que l'approche est réalisable et que l'utilisation des ressources est améliorée.

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Section: Parameter-sweep Application (Psa)mentioning

confidence: 99%

Section: Malleablementioning

confidence: 99%

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An RMS for Non-predictably Evolving Applications

Klein

Pérez

2011

2011 IEEE International Conference on Cluster Computing

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“…A tremendous amount of work has been dedicated to scheduling HPC applications without [4], [5], [6], [7], [8] or with preemption [9], [10], [11], [12], [13] (for brevity, the list is non-exhaustive), but all of the cited works consider the "amount of work" to be completed a fixed quantity, without considering the possibility to "drop" some work to improve system metrics. In other words, all computations are assumed mandatory.…”

Section: Introductionmentioning

confidence: 99%

Resource Management Architecture for Fair Scheduling of Optional Computations

Camillo

Caron

Guivarch

et al. 2013

2013 Eighth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing

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Abstract-Most High-Performance Computing platforms require users to submit a pre-determined number of computation requests (also called jobs). Unfortunately, this is cumbersome when some of the computations are optional, i.e., they are not critical, but their completion would improve results. For example, given a deadline, the number of requests to submit for a Monte Carlo experiment is difficult to choose. The more requests are completed, the better the results are, however, submitting too many might overload the platform. Conversely, submitting too few requests may leave resources unused and misses an opportunity to improve the results. This paper introduces and solves the problem of scheduling optional computations. It proposes a generic client-server architecture and an implementation in a production GridRPC middleware, which auto-tunes the number of requests. Real-life experiments show that several metrics are improved, such as user satisfaction, fairness and the number of completed requests. Moreover, the solution is shown to be scalable.

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“…Malleable applications allow implementing more flexible and efficient scheduling policies [2] that use idle processors to improve resource utilization [3,4]. While different RMSs support dynamic allocation of resources for malleable applications [5,6], MPI does not natively provide support for malleability.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the performance of malleable MPI applications by using performance-aware dynamic reconfiguration

et al. 2015

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The work in this paper focuses on providing malleability to MPI applications by using a novel performance-aware dynamic reconfiguration technique. This paper describes the design and implementation of Flex-MPI, an MPI library extension which can automatically monitor and predict the performance of applications, balance and redistribute the workload, and reconfigure the application at runtime by changing the number of processes. Unlike existent approaches, our reconfiguring policy is guided by user-defined performance criteria. We focus on iterative SPMD programs, a class of applications with critical mass within the scientific community. Extensive experiments show that Flex-MPI can improve the performance, parallel efficiency, and cost-efficiency of MPI programs with a minimal effort from the programmer.

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Scheduling Strategies for Cycle Scavenging in Multicluster Grid Systems

Abstract: Abstract

Cited by 14 publications

References 21 publications

An RMS for Non-predictably Evolving Applications

An RMS for Non-predictably Evolving Applications

Resource Management Architecture for Fair Scheduling of Optional Computations

Enhancing the performance of malleable MPI applications by using performance-aware dynamic reconfiguration

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