A survey of algorithmic skeleton frameworks: high‐level structured parallel programming enablers

González–Vélez, Horacio; Leyton, Mario

doi:10.1002/spe.1026

Cited by 157 publications

(134 citation statements)

References 217 publications

(178 reference statements)

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“…Algorithmic skeletons are general models for parallel programming [1]. It provides a programming language which is not only simple and independent of machine architecture but also highly efficient.…”

Section: Algorithmic Skeletonsmentioning

confidence: 99%

“…A task is recursively sub-divided until a condition is met, then the sub-task is executed and results are merged while the recursion is unwound. The most important feature of a skeleton is its generality that is the ability to be used in different applications [1]. In most algorithmic skeletons, there are a set of functions that has to be defined by the user.…”

Section: Algorithmic Skeletonsmentioning

confidence: 99%

“…Machine vision involves methods to understand the content of images to be used in area such as robotics and axis images [17]. Parallel processing has accelerated processing such as computing speed of computer systems [1]. As image processing is capable of parallelism, this paper addresses image processing by using parallelism.…”

Section: Introductionmentioning

confidence: 99%

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Parallel Image Processing Using Algorithmic Skeletons

Khorami¹

2014

IJIIS

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Abstract:In the last few decades, image processing has achieved significant theoretical and practical progress. It has been so fast that image processing can be easily traced in several disciplines and industries. At present, various methods have been proposed to implement image processing. The present paper aims to present a technique for image processing which utilizes design and analysis of parallel algorithms. It employs a new approach called "algorithmic skeletons" which is composed of a set of programming templates; hence facilitating the programmers' work.

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Section: Algorithmic Skeletonsmentioning

confidence: 99%

Section: Algorithmic Skeletonsmentioning

confidence: 99%

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Parallel Image Processing Using Algorithmic Skeletons

Khorami¹

2014

IJIIS

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“…Algorithmic skeletons abstract commonly-used patterns of parallel computation, communication, and interaction into a set of language constructs [17,32]. Skeletons present a top-down structured approach where parallel programs are formed from the parametrisation of skeleton nest, also known as structured parallelism.…”

Section: Related Workmentioning

confidence: 99%

The ParaPhrase Project: Parallel Patterns for Adaptive Heterogeneous Multicore Systems

Hammond

Aldinucci²,

Brown

et al. 2013

Formal Methods for Components and Objects

Self Cite

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Abstract. This paper describes the ParaPhrase project, a new 3-year targeted research project funded under EU Framework 7 Objective 3.4 (Computer Systems), starting in October 2011. ParaPhrase aims to follow a new approach to introducing parallelism using advanced refactoring techniques coupled with high-level parallel design patterns. The refactoring approach will use these design patterns to restructure programs defined as networks of software components into other forms that are more suited to parallel execution. The programmer will be aided by high-level cost information that will be integrated into the refactoring tools. The implementation of these patterns will then use a wellunderstood algorithmic skeleton approach to achieve good parallelism.A key ParaPhrase design goal is that parallel components are intended to match heterogeneous architectures, defined in terms of CPU/GPU combinations, for example. In order to achieve this, the ParaPhrase approach will map components at link time to the available hardware, and will then re-map them during program execution, taking account of multiple applications, changes in hardware resource availability, the desire to reduce communication costs etc. In this way, we aim to develop a new approach to programming that will be able to produce software that can adapt to dynamic changes in the system environment. Moreover, by using a strong component basis for parallelism, we can achieve potentially significant gains in terms of reducing sharing at a high level of abstraction, and so in reducing or even eliminating the costs that are usually associated with cache management, locking, and synchronisation.

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“…Due to lack of space, we refer to [9] for an overview of skeletal programming and [8] for a recent survey of skeleton libraries for clusters and multi-core CPUs. Our contribution to skeletal programming is the introduction and efficient implementation of a new algorithmic skeleton for performing allpairs computations.…”

Section: Conclusion and Related Workmentioning

confidence: 99%

Introducing and Implementing the Allpairs Skeleton for Programming Multi-GPU Systems

Steuwer

Friese

Albers

et al. 2013

Int J Parallel Prog

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Algorithmic skeletons simplify software development: they abstract typical patterns of parallelism and provide their efficient implementations, allowing the application developer to focus on the structure of algorithms, rather than on implementation details. This becomes especially important for modern parallel systems with multiple Graphics Processing Units (GPUs) whose programming is complex and error-prone, because state-of-the-art programming approaches like CUDA and OpenCL lack high-level abstractions.We define a new algorithmic skeleton for allpairs computations which occur in real-world applications, ranging from bioinformatics to physics. We develop the skeleton's generic parallel implementation for multi-GPU Systems in OpenCL. To enable the automatic use of the fast GPU memory, we identify and implement an optimized version of the allpairs skeleton with a customizing function that follows a certain memory access pattern.We use matrix multiplication as an application study for the allpairs skeleton and its two implementations and demonstrate that the skeleton greatly simplifies programming, saving up to 90% of lines of code as compared to OpenCL. The performance of our optimized implementation is up to 6.8 times higher as compared with the generic implementation and is competitive to the performance of a manually written optimized OpenCL code.

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A survey of algorithmic skeleton frameworks: high‐level structured parallel programming enablers

Cited by 157 publications

References 217 publications

Parallel Image Processing Using Algorithmic Skeletons

Parallel Image Processing Using Algorithmic Skeletons

The ParaPhrase Project: Parallel Patterns for Adaptive Heterogeneous Multicore Systems

Introducing and Implementing the Allpairs Skeleton for Programming Multi-GPU Systems

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