Performance of OpenMP Benchmarks on Multicore Processors

Marowka, Ami

doi:10.1007/978-3-540-69501-1_22

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…More performance details of EPCC micro-benchmarks on multicore machines can be found in Ref. [26]. Table 1 shows the running results of the IP program on Pentium D, Core 2 Duo and Core 2 Quad machines.…”

Section: Epcc Micro-benchmarksmentioning

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: Epcc Micro-benchmarksmentioning

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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“…Currently, there is a number of parallel programming models for Cell such as OpenMP, Sieve C++ and Offload. These models offer semi‐automatic parallelisation environments because the user is required to identify possible parallelism . Recent releases of the GNU tool chain and IBM XL offer compilers for C/C++ and FORTRAN on both architectures and support OpenMP for Linux platforms.…”

Section: Hardware Usedmentioning

confidence: 99%

Array languages and the N‐body problem

Cockshott

Gdura

Keir

2013

Concurrency and Computation

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SUMMARYThis paper is a description of the contributions to the SICSA multicore challenge on many body planetary simulation made by a compiler group at the University of Glasgow. Our group is part of the Computer Vision and Graphics research group and we have for some years been developing array compilers because we think these are a good tool both for expressing graphics algorithms and for exploiting the parallelism that computer vision applications require. We shall describe experiments using two languages on two dierent platforms and we shall compare the performance of these with reference C implementations running on the same platforms. Finally we shall draw conclusions both about the viability of the array language approach as compared to other approaches used in the challenge and also about the strengths and weaknesses of the two, very dierent, processor architectures we used. Copyright © 0000 John Wiley & Sons, Ltd.Received . . . ARRAY LANGUAGESBy the term array language, we mean a programming language that allows array values to be operated on as a whole: passed to functions as a whole, returned from functions as a whole, subjected to arithmetic operations as a whole, and, in the case of an imperative language, assigned as a whole.

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“…Again, it can be observed that the oldest architecture (Pentium D) obtained a relatively high overhead as compared to the newest architectures. We chose to compare TBB against the static schedule of OpenMP because it is usually the most efficient choice [13]. It can be observed from Fig.…”

Section: Scheduling Benchmarksmentioning

confidence: 99%

TBBench: A Micro-Benchmark Suite for Intel Threading Building Blocks

Marowka¹

2012

Journal of Information Processing Systems

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Abstract-Task-based programming is becoming the state-of-the-art method of choice for extracting the desired performance from multi-core chips. It expresses a program in terms of lightweight logical tasks rather than heavyweight threads. Intel Threading Building Blocks (TBB) is a task-based parallel programming paradigm for multi-core processors. The performance gain of this paradigm depends to a great extent on the efficiency of its parallel constructs. The parallel overheads incurred by parallel constructs determine the ability for creating large-scale parallel programs, especially in the case of fine-grain parallelism. This paper presents a study of TBB parallelization overheads. For this purpose, a TBB micro-benchmarks suite called TBBench has been developed. We use TBBench to evaluate the parallelization overheads of TBB on different multi-core machines and different compilers. We report in detail in this paper on the relative overheads and analyze the running results.

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Performance of OpenMP Benchmarks on Multicore Processors

Cited by 6 publications

References 6 publications

Bsp2omp: A Compiler For Translating Bsp Programs To Openmp

Bsp2omp: A Compiler For Translating Bsp Programs To Openmp

Array languages and the N‐body problem

TBBench: A Micro-Benchmark Suite for Intel Threading Building Blocks

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