OpenMP is Not as Easy as It Appears

Goncalves, Rogerio Aparecido; Amarís, Marcos; Okada, Thiago Kenji; Bruel, Pedro; Goldman, Alfredo

doi:10.1109/hicss.2016.710

Cited by 13 publications

(4 citation statements)

References 19 publications

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“…More recently, the performance of different runtimes for OpenMP dependent tasks and multithreaded libraries was compared in [36]. As for the programmability issues, the general ease of use of OpenMP has been discussed in [23], [15], and lists of typical mistakes and good practices have been compiled in [32]. A motivation, design description and performance evaluation of OpenMP 3.0 tasks is found in [2].…”

Section: Related Workmentioning

confidence: 99%

A Comparison of Task Parallel Frameworks based on Implicit Dependencies in Multi-core Environments

Fraguela

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Abstract-The larger flexibility that task parallelism offers with respect to data parallelism comes at the cost of a higher complexity due to the variety of tasks and the arbitrary patterns of dependences that they can exhibit. These dependencies should be expressed not only correctly, but optimally, i.e. avoiding over-constraints, in order to obtain the maximum performance from the underlying hardware. There have been many proposals to facilitate this non-trivial task, particularly within the scope of nowadays ubiquitous multi-core architectures. A very interesting family of solutions because of their large scope of application, ease of use and potential performance are those in which the user declares the dependences of each task, and lets the parallel programming framework figure out which are the concrete dependences that appear at runtime and schedule accordingly the parallel tasks. Nevertheless, as far as we know, there are no comparative studies of them that help users identify their relative advantages. In this paper we describe and evaluate four tools of this class discussing the strengths and weaknesses we have found in their use.

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Section: Related Workmentioning

confidence: 99%

A Comparison of Task Parallel Frameworks based on Implicit Dependencies in Multi-core Environments

Fraguela

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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“…Some parallelizations are carried out also using the semiautomatic platforms and the annotations/directives oriented frameworks like CUDA and OpenMP [4][5][6][7]. Although semiautomatic tools are often used for parallel implementation, they are not as easily applicable as it appears [8].…”

Section: Introductionmentioning

confidence: 99%

Incremental Banerjee test conditions committing for robust parallelization framework

Debbi¹,

Bakhti²

2018

Turk J Elec Eng & Comp Sci

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This paper describes the design of an automatic parallelization framework. The kernel supplied at its front end was suggested as an instrument for parallel potential assessment. It was used to measure the maximum achievable speedups in the major set of the CHStone benchmark suite programs. In such framework, we suggested the liberation of parallelism incrementally. We proposed a data dependency heuristic-based transformation method to make true dependences dissociation. We generated an internal representation (IR 2), where the Banerjee test conditions are met. Two among three of Banerjee test conditions came to be committed. In shared memory many/multicore platforms, the third condition could be satisfied by privatization. We would be able to choose the safe and the opportune pairwise (mapping-privatization) scheme among a number of threads mapping scenarios that become available in the IR 2 structure. Instrumentation on a subset of CHStone benchmark was carried out as a validity proof of our proposal, and the results confirmed that our framework kernel is robust.

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“…To alleviate the programmability challenges of parallel computing systems [12,2,25], various parallel programming models and languages [5] are proposed, including OpenMP, OpenACC, MPI, Pthreads, OpenCL, Cuda, and Intel TBB. While these approaches have helped to significantly reduce the programming effort, writing more complex code requires more effort, advanced knowledge of parallel algorithms and underlying architecture and is more prone to mistakes that may lead to incorrect program behavior or performance degradation [8]. To efficiently utilize the available resources on the modern parallel computing systems, programmers require adequate education [3].…”

Section: Introductionmentioning

confidence: 99%

“…However, it is up to the programmer to decide how and when to use these directives. Even though, the available documentation suggests that using OpenMP is simple and easy, Gonçalves et al [8] show that using OpenMP is not as easy as it appears, and such suggestions may lead to code that provides incorrect results or poor performance [17,18]. Furthermore, Kolosov et al [13] and Süß et al [24] has identified a number of commonly made OpenMP mistakes, and they have categorized them in logical and performance related errors.…”

Section: Introductionmentioning

confidence: 99%

Using Cognitive Computing for Learning Parallel Programming: An IBM Watson Solution

Chozas

Memeti

Pllana

2017

Procedia Computer Science

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While modern parallel computing systems provide high performance resources, utilizing them to the highest extent requires advanced programming expertise. Programming for parallel computing systems is much more difficult than programming for sequential systems. OpenMP is an extension of C++ programming language that enables to express parallelism using compiler directives. While OpenMP alleviates parallel programming by reducing the lines of code that the programmer needs to write, deciding how and when to use these compiler directives is up to the programmer. Novice programmers may make mistakes that may lead to performance degradation or unexpected program behavior. Cognitive computing has shown impressive results in various domains, such as health or marketing. In this paper, we describe the use of IBM Watson cognitive system for education of novice parallel programmers. Using the dialogue service of the IBM Watson we have developed a solution that assists the programmer in avoiding common OpenMP mistakes. To evaluate our approach we have conducted a survey with a number of novice parallel programmers at the Linnaeus University, and obtained encouraging results with respect to usefulness of our approach.

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OpenMP is Not as Easy as It Appears

Cited by 13 publications

References 19 publications

A Comparison of Task Parallel Frameworks based on Implicit Dependencies in Multi-core Environments

A Comparison of Task Parallel Frameworks based on Implicit Dependencies in Multi-core Environments

Incremental Banerjee test conditions committing for robust parallelization framework

Using Cognitive Computing for Learning Parallel Programming: An IBM Watson Solution

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