How SPMD applications could be efficiently executed on multicore environments?

Muresano, Ronal; Rexachs, Dolores; Luque, Emilio

doi:10.1109/clustr.2009.5289135

Cited by 6 publications

(11 citation statements)

References 6 publications

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“…For example, mapping strategies are discussed with the aim of allocating the processes into each node and the scheduling which allows student to apply overlapping strategies. This strategies allows students to overlap the computations and the communications [7]. The instructor's strategies are applied by student and they obtained an significant improvement in their parallel execution.…”

Section: Parallel Application Improvement Evaluationmentioning

confidence: 94%

“…This improvement is achieved through the union of the considerations of the instructor and students, and also with the problems detected in the first phase of this methodology. Then, the proposed improvements are applied to the new versions of both applications which are tunned to the cluster architecture using mapping and scheduling strategies in order to improve the performance metrics [7]. The instructor follows a checking process in order to verify that students do not have troubles applying the proposed changes.…”

Section: Parallel Application Improvement Phasementioning

confidence: 99%

“…These evaluations are done through a characterization module which allow students to obtain a communication and computation parameters of the applications. These characterization module is explained to students using a methodology exposed in [7]. Once students finish this characterization, the performance metrics are calculated.…”

Section: Applications Performance Evaluations Phasementioning

confidence: 99%

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Learning parallel programming: a challenge for university students

Muresano

Rexachs

Luque

2010

Procedia Computer Science

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Currently, the need to learn parallel applications topics in students has become an important issue due to the rapid growth in the parallel computing field. In fact, this topic has been included in Computer Science curriculum, but students present difficulties to design MPI parallel applications efficiently. We present a novel methodology for teaching parallel programming centered on improving parallel applications written by students through their experiences obtained during classes. The methodology integrates theoretical and practical sections which are focused on teaching two parallel paradigms, master/Worker and SPMD. These paradigms were selected due to their different communication and computation behaviors, which generate challenges for students when they wish to improve performance application metrics. Our methodology allows students to discover their own errors and how to correct them. In addition, students analyze the issues and advantages in the application designed in order to enhance the performance metrics. Applying this methodology gave us a significant progress in parallel applications designed by students, where we have observed an improvement of around 47% in the students' skill about parallel programming when they design parallel applications.

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Section: Parallel Application Improvement Evaluationmentioning

confidence: 94%

Section: Parallel Application Improvement Phasementioning

confidence: 99%

Section: Applications Performance Evaluations Phasementioning

confidence: 99%

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Learning parallel programming: a challenge for university students

Muresano

Rexachs

Luque

2010

Procedia Computer Science

Self Cite

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“…The idle time generated decreases the performance, particularly efficiency and speedup. Our methodology, through its phases, can solve the inefficiencies generated in these multicore environments as is described in [5]. This methodology evaluates two possible distributions for SPMD applications: one (X) and two (X, Y) dimensional blocks [8], which have influences when a communication heterogeneous environments is used.…”

Section: Methodology Descriptionmentioning

confidence: 99%

“…From the problem defined above, we have developed a novel methodology which is composed of four phases: characterization, a tasks distribution model, mapping, and scheduling [5]. This methodology is centered on finding the minimum execution time while the efficiency is maintained over a threshold.…”

Section: Introductionmentioning

confidence: 99%

A tool for efficient execution of SPMD applications on multicore clusters

Muresano

Rexachs

Luque

2010

Procedia Computer Science

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A challenge for parallel programmers is to efficiently execute traditional MPI applications, designed to be run in a cluster of single core nodes, on a multicore cluster. Multicore clusters include communication heterogeneities which have to be handled carefully to improve efficiency and speedup. This research presents an execution tool developed for SPMD applications which is focused on managing communications heterogeneities, distributing the workload among cores and enhancing parallel performance on multicore clusters. Our tool has been designed through using an execution methodology which includes mapping and scheduling strategies. The tool integrates five modules which give programmers a method to execute their applications efficiently. This tool is centered on improving SPMD applications designed to use MPI for communications. These applications were selected because they are the most commonly used in parallel computing. Also, these applications are chosen due to their data synchronization and communications volumes which can generate communication imbalance issues. The novel contribution of this tool is to permit programmers to find a minimum execution time, while the efficiency level is maintained over a defined threshold. Our tool has been tested in different multicore clusters and with a set of scientific applications. The results obtained show a considerable improvement in the applications efficiency when the tool is applied.

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Case Study II: Resource Management of Network Virtualization

Marquezan

Granville

2012

Self-* and P2P for Network Management

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How SPMD applications could be efficiently executed on multicore environments?

Cited by 6 publications

References 6 publications

Learning parallel programming: a challenge for university students

Learning parallel programming: a challenge for university students

A tool for efficient execution of SPMD applications on multicore clusters

Case Study II: Resource Management of Network Virtualization

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