Program diagramming and fundamental programming patterns for a polymorphic computing dataflow processor

JavaScript has become more and more popular in recent years because its wealthy features as being dynamic, interpreted and object-oriented with first-class functions. Furthermore, JavaScript is designed with event-driven and I/O non-blocking model that boosts the performance of overall application especially in the case of Node.js. To take advantage of these characteristics, many design patterns that implement asynchronous programming for JavaScript were proposed. However, choosing a right pattern and implementing a good asynchronous source code is a challenge and thus easily lead into less robust application and low quality source code. Extended from our previous works on exception handling code smells in JavaScript and exception handling code smells in JavaScript asynchronous programming with promise, this research aims at studying the impact of three JavaScript asynchronous programming patterns on quality of source code and application.

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Security/Trust as a Polymorphic Computing Constraint

10.21236/ada423822 ◽

2003 ◽

Author(s):

Clark Weissman ◽

Brant Hashii ◽

Jerry Cole

Keyword(s):

Polymorphic Computing

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High-Level Parallel Ant Colony Optimization with Algorithmic Skeletons

International Journal of Parallel Programming ◽

10.1007/s10766-021-00714-1 ◽

2021 ◽

Author(s):

Breno A. de Melo Menezes ◽

Nina Herrmann ◽

Herbert Kuchen ◽

Fernando Buarque de Lima Neto

Keyword(s):

Ant Colony Optimization ◽

High Performance ◽

Optimization Problems ◽

Programming Model ◽

Parallel Implementation ◽

Ant Colony ◽

Algorithmic Skeletons ◽

Low Level ◽

Programming Patterns ◽

High Level

AbstractParallel implementations of swarm intelligence algorithms such as the ant colony optimization (ACO) have been widely used to shorten the execution time when solving complex optimization problems. When aiming for a GPU environment, developing efficient parallel versions of such algorithms using CUDA can be a difficult and error-prone task even for experienced programmers. To overcome this issue, the parallel programming model of Algorithmic Skeletons simplifies parallel programs by abstracting from low-level features. This is realized by defining common programming patterns (e.g. map, fold and zip) that later on will be converted to efficient parallel code. In this paper, we show how algorithmic skeletons formulated in the domain specific language Musket can cope with the development of a parallel implementation of ACO and how that compares to a low-level implementation. Our experimental results show that Musket suits the development of ACO. Besides making it easier for the programmer to deal with the parallelization aspects, Musket generates high performance code with similar execution times when compared to low-level implementations.

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