Section: Scientific Foundations

Programming Abstractions

Another research direction deals with determining well suited programming abstractions to reconcile a priori contradictory goals: being “simple” to use and portable, while enabling high performance. Existing parallel and distributed programming models either expose infrastructure artifacts to programmers so that performance can be achieved —by experts!— but not portability or they propose very specialized models such as GridRPC and Google's MapReduce. In the latter case, an application is restricted to use one concept at a time. For example, it is very difficult for an application to simultaneously use two middleware systems providing respectively GridRPC and MapReduce.

The Avalon team addresses the challenge of designing a general composition based model supporting as many composition operators as possible while enabling efficient execution on parallel and distributed infrastructures. We mainly consider component based models as they offer the ability to manipulate the software architecture of an application. To achieve our goal, we consider a “compilation” approach that transforms a resource agnostic application description into a resource specific description. The challenge is thus to determine the best suited models.

Many works have already been done with respect to component models. However, existing model such as Fractal, CCA, BIP do not provide an adequate solution as they only support a limited set of interactions. We aims to extend the approach initiated with HLCM that aims at identifying core elements to let a programmer define any (spatial) compositions. We also target to provide mechanisms to support application and resource specialization algorithms.

A first challenge is to conduct an in depth validation of the ability of the proposed HLCM approach to deal with any kind of static compositions. In particular, it includes designing efficient transformation algorithms and understanding their generality.

A second challenge is to extend the proposed approach to support dynamic applications, either because of adaptation issues or because of temporal compositions such as workflows. Starting from motivating use cases, we will study whether just-in-time assembly transformation techniques provide an efficient and scalable solution.