Matchmaking: distributed resource management for high throughput computing

Raman, Rajesh; Livny, Miron; Solomon, Marvin

doi:10.1109/hpdc.1998.709966

Cited by 486 publications

(313 citation statements)

References 9 publications

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“…When the performance skeleton of an application is available, an estimate of the application execution time in a new environment is obtained by simply executing the performance skeleton and appropriately scaling the measured The state of the art in performance prediction and scheduling for distributed computing environments is based on modeling of application characteristics and execution environments, with some example systems discussed in [4,5,6]. The research presented in this paper is fundamentally different in being based on synthetically generated executable code as the primary vehicle for performance prediction.…”

Section: Introductionmentioning

confidence: 99%

Construction and Evaluation of Coordinated Performance Skeletons

Xu¹,

Subhlok²

2008

High Performance Computing - HiPC 2008

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Abstract. Performance prediction is particularly challenging for dynamic environments that cannot be modeled well due to reasons such as resource sharing and foreign system components. The approach to performance prediction taken in this work is based on the concept of a performance skeleton which is a short running program whose execution time in any scenario reflects the estimated execution time of the application it represents. The fundamental technical challenge addressed in this paper is the automatic construction of performance skeletons for parallel MPI programs. The steps in the skeleton construction procedure are 1) generation of process execution traces and conversion to a single coordinated logical program trace, 2) compression of the logical program trace, and 3) conversion to an executable parallel skeleton program. Results are presented to validate the construction methodology and prediction power of performance skeletons. The execution scenarios analyzed involve network sharing, different architectures and different MPI libraries. The emphasis is on identifying the strength and limitations of this approach to performance prediction.

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Section: Introductionmentioning

confidence: 99%

Construction and Evaluation of Coordinated Performance Skeletons

Xu¹,

Subhlok²

2008

High Performance Computing - HiPC 2008

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“…In Step (3), results from the query are returned to the User Interface; in Step (4), the response data is used to determine the set of all site ComputeElements that support the specified CDER; but at this point, it has not yet been determined which of these ComputeElements satisfies the specific CDER requirements. The data returned for each of these ComputeElements is then reconstituted in ClassAd [14] format as a Resource Offer. A ClassAd Resource Request is created from the job CDER requirements.…”

Section: Two-phase Job Submissionmentioning

confidence: 99%

Overview and Evaluation of Conceptual Strategies for Accessing Cpu-Dependent Execution Resources in Grid Infrastructures

Walsh

Dukes

Pierantoni

et al. 2015

csci

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The emergence of many-core and massively-parallel computational accelerators (e.g., GPGPUs) has led to user demand for such resources in grid infrastructures. A widely adopted approach for discovering and accessing such resources has, however, yet to emerge. GPGPUs are an example of a larger class of computational resources, characterized in part by dependence on an allocated CPU. This paper terms such resources "CPU-Dependent Execution Resources" (CDERs). Five conceptual strategies for discovering and accessing CDERs are described and evaluated against key criteria, and all five strategies are compliant with GLUE 1.3, GLUE 2.0, or both. From this evaluation, two of the presented strategies clearly emerge as providing the greatest flexibility for publishing both static and dynamic CDER information and identifying CDERs that satisfy specific job requirements. Furthermore, a two-phase approach to job-submission is proposed for those jobs requiring access to CDERs. The approach is compatible with existing grid services. Examples are provided to illustrate job submission under each strategy.

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“…This language, inspired by the Condor classads language [9], differentiates between the hard requirements, which should be met by a VMI to be considered a potential candidate, and the soft ones, which can be ranked by the client. Certain applications might be considered soft requirements since the client might rely on proper deployment software to delegate the installation of these software on the VM.…”

Section: The Virtual Machine Catalogue and Repositorymentioning

confidence: 99%

A Service-Oriented Architecture for Scientific Computing on Cloud Infrastructures

Moltó

Calatrava

Hernández

2013

Lecture Notes in Computer Science

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Calatrava Arroyo, A.; Hernández García, V. (2013)Abstract. This paper describes a service-oriented architecture that eases the process of scientific application deployment and execution in IaaS Clouds, with a focus on High Throughput Computing applications. The system integrates i) a catalogue and repository of Virtual Machine Images, ii) an application deployment and configuration tool, iii) a metascheduler for job execution management and monitoring. The developed system significantly reduces the time required to port a scientific application to these computational environments. This is exemplified by a case study with a computationally intensive protein design application on both a private Cloud and a hybrid three-level infrastructure (Grid, private and public Cloud).Topics Parallel and Distributed Computing.

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Matchmaking: distributed resource management for high throughput computing

Cited by 486 publications

References 9 publications

Construction and Evaluation of Coordinated Performance Skeletons

Construction and Evaluation of Coordinated Performance Skeletons

Overview and Evaluation of Conceptual Strategies for Accessing Cpu-Dependent Execution Resources in Grid Infrastructures

A Service-Oriented Architecture for Scientific Computing on Cloud Infrastructures

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