Tackling Algorithmic Skeleton's Inversion of Control

Pabón, Gustavo; Leyton, Mario

doi:10.1109/pdp.2012.86

Cited by 2 publications

(2 citation statements)

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“…Second, the reactive nature of non-functional concerns is better reflected by events. For more details about this SoC approach, and Listing 1: Example: skeleton processing its implementation on Skandium, refer to our previous work [20].…”

Section: Separation Of Concerns Using Eventsmentioning

confidence: 99%

“…But inversion of control hides, at the same time, the actual execution flow to the programmer; which is essential to the implementation of nonfunctional concerns like autonomic computing. In our previous work [20], we proposed a solution to tackle skeleton's inversion of control by introducing a novel separation of concerns (SoC) based on event driven programming. This paper shows how to use such SoC for developing self-configuring and self-optimizing skeletons.…”

Section: Introductionmentioning

confidence: 99%

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Self-Configuration and Self-Optimization Autonomic Skeletons using Events

Pabón¹,

Henrio

2014

Proceedings of Programming Models and Applications on Multicores and Manycores

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This paper presents a novel way to introduce self-configuration and self-optimization autonomic characteristics to algorithmic skeletons using event driven programming techniques. Based on an algorithmic skeleton language, we show that the use of events greatly improves the estimation of the remaining computation time for skeleton execution. Events allow us to precisely monitor the status of the execution of algorithmic skeletons. Using such events, we provide a framework for the execution of skeletons with a very high level of adaptability. We focus mainly on guaranteeing a given execution time for a skeleton, by optimizing autonomically the number of threads allocated. The proposed solution is independent from the platform chosen for executing the skeleton for example we illustrate our approach in a multicore setting, but it could also be adapted to a distributed execution environment.

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Section: Separation Of Concerns Using Eventsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%