Anne Benoit

Evaluating the Performance of Skeleton-Based High Level Parallel Programs

We show in this paper how to evaluate the performance of skeleton-based high level parallel programs. Since many applications fol- low some commonly used algorithmic skeletons, we identify such skele- tons and model them with process algebra in order to get relevant in- formation about the performance of the application, and be able to take some good scheduling decisions. This concept is illustrated through the case study of the Pipeline skeleton, and a tool which generates auto- matically a set of models and solves them is presented. Some numerical results are provided, proving the efficiency of this approach.

Flexible skeletal programming with eskel

We present an overview of eSkel, a library for skeletal parallel programming. eSkel aims to maximise the conceptual flexibility afforded by its component skeletons and to facilitate dynamic selection of skeleton compositions. We present simple examples which illustrate these properties, and discuss the implementation challenges which the model poses.

Two fundamental concepts in skeletal parallel programming

We define the concepts of nesting mode and interaction mode as they arise in the description of skeletal parallel programming systems. We suggest that these new concepts encapsulate fundamental design issues and may play a useful role in defining and distinguishing between the capabilities of competing systems. We present the decisions taken in our own Edinburgh Skeleton Library eSkel, and review the approaches chosen by a selection of other skeleton libraries.

Enhancing the effective utilisation of grid clusters by exploiting on-line performability analysis

In grid applications the heterogeneity and potential failures of the computing infrastructure poses significant challenges to efficient scheduling. Performance models have been shown to be useful in providing predictions on which schedules can be based (N. Furmento et al., 2002) and most such techniques can also take account of failures and degraded service. However, when several alternative schedules are to be compared it is vital that the analysis of the models does not become so costly as to outweigh the potential gain of choosing the best schedule. Moreover, it is vital that the modelling approach can scale to match the size and complexity of realistic applications. In this paper, we present a novel method of modelling job execution on grid compute clusters. As previously we use performance evaluation process algebra (PEPA) (J. Hillston, 1996) as the system description formalism, capturing both workload and computing fabric. The novel feature is that we make a continuous approximation of the state space underlying the PEPA model and represent it as a set of ordinary differential equations (ODEs) for solution, rather than a continuous time, but discrete state space, Markov chain.

Scheduling Skeleton-Based Grid Applications Using PEPA and NWS

Any scheduling scheme for grid applications must make implicit or explicit assumptions about both the future behaviour of the application and the future availability and performance of grid resources. This paper describes an approach in which the future application behaviour is constrained by the use of algorithmic skeletons, facilitating modelling with a performance oriented process algebra, and future grid resource performance is predicted by the Network Weather Service (NWS) tool. The concept is illustrated through a case study involving Pipeline and Deal skeletons. A tool is presented which automatically generates and solves a set of models which are parameterised with information obtained from NWS. Some numerical results and timing information on the use of the tool are provided, illustrating the efficacy of this approach.