Efficient, scalable and productive parallel programming is a major challenge for exploiting the future multi-processor SoC platforms. This article presents the MultiFlex programming environment which has been developed to address this challenge. It is targeted for use on Platform 2012 , a scalable multi-processor fabric. The MultiFlex environment supports high-level simulation, iterative platform mapping, and includes tools for programming model aware debug, trace, visualization and analysis. This article focuses on the two classes of programming abstractions supported in MultiFlex. The first is a set of Parallel Programming Patterns (PPP) which offer a rich set of programming abstractions for implementing efficient data- and task-level parallel applications. The second is a Reactive Task Management (RTM) abstraction, which offers a lightweight C-based API to support dynamic dispatching of small grain tasks on tightly coupled parallel processing resources. The use of the MultiFlex native programming model is illustrated through the capture and mapping of two representative video applications. The first is a high-quality rescaling (HQR) application on a multi-processor platform. We present the details of the optimization process which was required for mapping the HQR application, for which the reference code requires 350 GIPS (giga instructions per second), onto a 16 processor cluster. Our results show that the parallel implementation using the PPP model offers almost linear acceleration with respect to the number of processing elements. The second application is a high-definition VC-1 decoder. For this application, we illustrate two different parallel programming model variants, one using PPPs, the other based on RTM. These two versions are mapped onto two variants of a homogeneous version of the Platform 2012 multi-core fabric.

SUMMARY Many‐core platforms, providing large numbers of parallel execution resources, emerge as a response to the increasing computation needs of embedded applications. A major challenge raised by this trend is the efficient mapping of applications on parallel resources. This is a nontrivial problem because of the number of parameters to be considered for characterizing both the applications and the underlying platform architectures. Recently, several authors have proposed to use multi‐objective evolutionary algorithm to solve this problem within the context of mapping applications on network‐on‐chips. However, these proposals have several limitations: (1) only few metaheuristics are explored (mainly Nondominated Sorting Genetic Algorithm II and Strength Pareto Evolutionary Algorithm 2), (2) only few objective functions are provided, and (3) they only deal with a small number of the application and architecture constraints. In this paper, we propose a new framework that avoids all of the problems cited previously. Our framework is implemented on top of the jMetal framework, which offers an extensible environment. Our framework allows designers to (1) explore several new metaheuristics, (2) easily add a new objective function (or to use an existing one), and (3) take into account any number of architecture and application constraints. The paper also presents experiments illustrating how our framework is applied to the problem of mapping streaming applications on an NoC‐based many‐core platform. Our results show that several new metaheuristics outperform the classical multi‐objective metaheuristics such as Nondominated Sorting Genetic Algorithm II and Strength Pareto Evolutionary Algorithm 2. Moreover, a parallel multi‐objective evolutionary algorithm is implemented in our framework in order to increase the explored space of solutions by simultaneously running several metaheuristics. Copyright © 2011 John Wiley & Sons, Ltd.

Many architecture description languages (ADLs) have been proposed to model, analyze, configure, and deploy complex software systems. To face this diversity, extensible ADLs (or ADL interchange formats) have been proposed. These ADLs provide linguistic support for integrating various architectural aspects within the same description. Nevertheless, they do not support extensibility at the tool level, i.e. they do not provide an extensible toolset for processing ADL descriptions. In this paper, we present an extensible toolset for easing the development of architecture-based software systems. This toolset is not bound to a specific ADL, but rather uses a grammar description mechanism to accept various input languages, e.g. ADLs, interface definition languages (IDLs), domain specific languages (DSLs). Moreover, it can easily be extended to implement many different features, such as behavioral analysis, code generation, deployment, etc. Its extensibility is obtained by designing its core functionalities using fine-grained components that implement flexible design patterns. Experiments are presented to illustrate both the functionalities implemented by the toolset and the way it can be extended.