OpenMP‐oriented applications for distributed shared memory architectures

Marowka, Ami; Liu, Zhenying; Chapman, Barbara

doi:10.1002/cpe.752

Cited by 10 publications

(7 citation statements)

References 7 publications

(19 reference statements)

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“…Moreover, there are many classes of problems that demand other types of programming models that are currently not supported by the OpenMP standard [27]. For example, OpenMP is a shared-data programming model, while many highperformance applications need to be run on distributed-shared memory machines and Beowulf clusters.…”

Section: A Marowka 296mentioning

confidence: 99%

Bsp2omp: A Compiler For Translating Bsp Programs To Openmp

Marowka

2009

International Journal of Parallel, Emergent and Distributed Sys

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The convergence of the two widely used parallel programming paradigms, sharedmemory and distributed-shared-memory parallel programming models, into a unified parallel programming model is crucial for parallel computing to become the next mainstream programming paradigm. We study the design differences and the performance issues of two parallel programming models: a shared-memory programming model (OpenMP) and a distributed-shared programming model (BSP).The study was carried out by designing a compiler for translating BSP parallel programs to an OpenMP programming model called BSP2OMP. Analysis of the compiler outcome, and of the performance of the compiled programs, show that the two models are based on very similar underlying principles and mechanisms.

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Section: A Marowka 296mentioning

confidence: 99%

Bsp2omp: A Compiler For Translating Bsp Programs To Openmp

Marowka

2009

International Journal of Parallel, Emergent and Distributed Sys

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“…The HPCTools group at the University of Houston has been working in this area for a long time, proposing compile-time techniques that can help improving memory affinity on hierarchical architectures like distributed shared memory platforms [10]. Huang et al [6] proposed OpenMP extensions to deal with memory affinity on NUMA machines, like ways of explicitly aligning tasks and data inside logical partitions of the architecture called locations.…”

Section: Related Workmentioning

confidence: 99%

Description, Implementation and Evaluation of an Affinity Clause for Task Directives

Virouleau

Roussel

Broquedis

et al. 2016

OpenMP: Memory, Devices, and Tasks

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OpenMP 4.0 introduced dependent tasks, which give the programmer a way to express fine grain parallelism. Using appropriate OS support (such as NUMA libraries), the runtime can rely on the information in the depend clause to dynamically map the tasks to the architecture topology. Controlling data locality is one of the key factors to reach a high level of performance when targeting NUMA architectures. On this topic, OpenMP does not provide a lot of flexibility to the programmer yet, which lets the runtime decide where a task should be executed. In this paper, we present a class of applications which would benefit from having such a control and flexibility over tasks and data placement. We also propose our own interpretation of the new affinity clause for the task directive, which is being discussed by the OpenMP Architecture Review Board. This clause enables the programmer to give hints to the runtime about tasks placement during the program execution, which can be used to control the data mapping on the architecture. In our proposal, the programmer can express affinity between a task and the following resources: a thread, a NUMA node, and a data. We then present an implementation of this proposal in the Clang-3.8 compiler, and an implementation of the corresponding extensions in our OpenMP runtime LIBKOMP. Finally, we present a preliminary evaluation of this work running two task-based OpenMP kernels on a 192-core NUMA architecture, that shows noticeable improvements both in terms of performance and scalability.

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“…The main obstacle to the adaptation of OpenMP on NUMA architecture stems from the absence of facilities for data placement among processors and threads to achieve data locality. The absence of such a mechanism causes remote memory accesses and inefficient cache memory use, both of which lead to poor performance [5].…”

Section: Int C[100][t Hreads]mentioning

confidence: 99%

Execution Model of Three Parallel Languages: OpenMP, UPC and CAF

Marowka

2005

Scientific Programming

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Abstract. The aim of this paper is to present a qualitative evaluation of three state-of-the-art parallel languages: OpenMP, Unified Parallel C (UPC) and Co-Array Fortran (CAF). OpenMP and UPC are explicit parallel programming languages based on the ANSI standard. CAF is an implicit programming language. On the one hand, OpenMP designs for shared-memory architectures and extends the base-language by using compiler directives that annotate the original source-code. On the other hand, UPC and CAF designs for distribute-shared memory architectures and extends the base-language by new parallel constructs. We deconstruct each language into its basic components, show examples, make a detailed analysis, compare them, and finally draw some conclusions.

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OpenMP‐oriented applications for distributed shared memory architectures

Cited by 10 publications

References 7 publications

Bsp2omp: A Compiler For Translating Bsp Programs To Openmp

Bsp2omp: A Compiler For Translating Bsp Programs To Openmp

Description, Implementation and Evaluation of an Affinity Clause for Task Directives

Execution Model of Three Parallel Languages: OpenMP, UPC and CAF

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