Scheduling Algorithms

Brucker, Peter

doi:10.1007/978-3-662-03088-2

Cited by 358 publications

(372 citation statements)

References 39 publications

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“…Therefore, the parallel performance of the ex- plicit scheme is expected to be dominated by the load balance. Another key issue of communication is scheduling [2]. In Alya, a relatively simple but efficient scheme is used, based on coloring and build upon the adjacency graph of each subdomain.…”

Section: Communication Types and Schedulingmentioning

confidence: 99%

Alya: Multiphysics engineering simulation toward exascale

Vázquez

Houzeaux

Korić

et al. 2016

Journal of Computational Science

180

111

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Abstract. Alya is a multi-physics simulation code developed at Barcelona Supercomputing Center (BSC). From its inception Alya code is designed using advanced High Performance Computing programming techniques to solve coupled problems on supercomputers efficiently. The target domain is engineering, with all its particular features: complex geometries and unstructured meshes, coupled multi-physics with exotic coupling schemes and physical models, ill-posed problems, flexibility needs for rapidly including new models, etc. Since its beginnings in 2004, Alya has scaled well in an increasing number of processors when solving single-physics problems such as fluid mechanics, solid mechanics, acoustics, etc. Over time, we have made a concerted effort to maintain and even improve scalability for multi-physics problems. This poses challenges on multiple fronts, including: numerical models, parallel implementation, physical coupling models, algorithms and solution schemes, meshing process, etc. In this paper, we introduce Alya's main features and focus particularly on its solvers. We present Alya's performance up to 100.000 processors in Blue Waters, the NCSA supercomputer with selected multi-physics tests that are representative of the engineering world. The tests are incompressible flow in a human respiratory system, low Mach combustion problem in a kiln furnace, and coupled electro-mechanical contraction of the heart. We show scalability plots for all cases and discuss all aspects of such simulations, including solver convergence.Key words. Multi-physics coupling, Parallelisation, Computational Mechanics 1. Introduction. Across a range of engineering fields, the use of computational models is pervasive in the whole design and manufacturing process. In complex systems, High Performance Computing (HPC) plays an essential role in simulation and modelling. Researchers and manufacturing teams depend on HPC to create safe cars and energy-efficient aircraft as well as effective communication systems and efficient supply chain models. Availability of advanced HPC technologies has also fundamentally altered the investigative paradigm in the field of biomechanics. But paradoxically, for many engineers and researchers, the existing hardware and software cannot be used to solve their problems. There are many reasons why this happens, but we focus here in only two. On one hand, current HPC systems lack the computational power, network bandwidth and data storage needed for solving tomorrow's real-world engineering challenges. On the other hand, while emerging peta-scale computing is already a strategic enabler of large-scale simulations in many scientific areas (such as astronomy, biology and chemistry), even the most powerful hardware will fail to deliver on its full potential unless matched with simulation software designed specifically for such environments.Several papers describe the effort of performing large-scale simulations on supercomputers, covering key areas: molecular dynamics [26], mantle convection in solid earth dynami...

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Section: Communication Types and Schedulingmentioning

confidence: 99%

Alya: Multiphysics engineering simulation toward exascale

Vázquez

Houzeaux

Korić

et al. 2016

Journal of Computational Science

180

111

View full text Add to dashboard Cite

show abstract

“…Problem Statement Job scheduling on parallel machines has been widely studied (see, e.g., the surveys in [4], [28]). In particular, much attention was given to interval scheduling [19], where jobs are given as intervals on the real line, each representing the time interval during which a job should be processed; each job has to be processed on some machine, and it is commonly assumed that a machine can process a single job at any given time.…”

Section: Introductionmentioning

confidence: 99%

“…All the jobs in the batch start simultaneously, and the completion time of a batch is the last completion time of any job in the batch. (For known results on batch scheduling, see, e.g., [4].) Our scheduling problem differs from batch scheduling in several aspects.…”

Section: Introductionmentioning

confidence: 99%

Optimizing busy time on parallel machines

Mertzios

Shalom

Voloshin

et al. 2015

Theoretical Computer Science

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Abstract-We consider the following fundamental scheduling problem in which the input consists of n jobs to be scheduled on a set of identical machines of bounded capacity g (which is the maximal number of jobs that can be processed simultaneously by a single machine). Each job is associated with a start time and a completion time; it is supposed to be processed from the start time to the completion time (and in one of our extensions it has to be scheduled also in a continuous number of days; this corresponds to a two-dimensional version of the problem). We consider two versions of the problem. In the scheduling minimization version the goal is to minimize the total busy time of machines used to schedule all jobs. In the resource allocation maximization version the goal is to maximize the number of jobs that are scheduled for processing under a budget constraint given in terms of busy time. This is the first study of the maximization version of the problem. The minimization problem is known to be NP-Hard, thus the maximization problem is also NP-Hard. We consider various special cases, identify cases where an optimal solution can be computed in polynomial time, and mainly provide constant factor approximation algorithms for both minimization and maximization problems. Some of our results improve upon the best known results for this job scheduling problem. Our study has applications in power consumption, cloud computing and optimizing switching cost of optical networks.

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“…• shown that the problem of scheduling wrapped core tests on the TAM is equivalent to independent job scheduling on identical machines, • made use of a preemptive approach to produce a solution with optimal test time in linear time [2] using reconfigurable wrappers [15], and • extended the scheduling algorithm to handle wrapped core tests and cross-core tests while preserving the production of an optimal solution in linear time.…”

Section: Introductionmentioning

confidence: 99%

“…4 we present our optimal preemptive test scheduling technique, based on a known preemptive scheduling algorithm [2]. In the experiments we have made a comparison with previous approaches and we illustrate the advantages with our wrapper and its use in the proposed scheduling approach in Section 5.…”

Section: Introductionmentioning

confidence: 99%