StochKit-FF: Efficient Systems Biology on Multicore Architectures

The stochastic modelling of biological systems is an informative, and in some cases, very adequate technique, which may however result in being more expensive than other modelling approaches, such as differential equations. We present StochKit-FF, a parallel version of StochKit, a reference toolkit for stochastic simulations. StochKit-FF is based on the FastFlow programming toolkit for multicores and exploits the novel concept of selective memory. We experiment StochKit-FF on a model of HIV infection dynamics, with the aim of extracting information from efficiently run experiments, here in terms of average and variance and, on a longer term, of more structured data.Comment: 14 pages + cover pag

Gaspar data-centric framework

This paper presents the Gaspar data-centric framework to develop high performance parallel applications in Java. Our approach is based on data iterators and on the map pattern of computation. The framework provides an efficient data Application Programming Inter-face(API) that supports flexible data layout and data tiling. Data layout and tiling enable the improvement of data locality, which is essential to foster application scalability in modern multi-core systems. The paper presents the framework data-centric concepts and shows that the performance is comparable to pure Java code.(undefined)info:eu-repo/semantics/publishedVersio

Pushouts in software architecture design

A classical approach to program derivation is to progressively extend a simple specification and then incrementally refine it to an implementation. We claim this approach is hard or impractical when reverse engineering legacy software architectures. We present a case study that shows optimizations and pushouts--in addition to refinements and extensions--are essential for practical stepwise development of complex software architectures.NSF CCF 0724979NSF CNS 0509338NSF CCF 0917167NSF DGE-1110007FCT SFRH/BD/47800/2008FCT UTAustin/CA/0056/200

The Missing Link! A New Skeleton for Evolutionary Multi-agent Systems in Erlang

Evolutionary multi-agent systems (EMAS) play a critical role in many artificial intelligence applications that are in use today. In this paper, we present a new generic skeleton in Erlang for parallel EMAS computations. The skeleton enables us to capture a wide variety of concrete evolutionary computations that can exploit the same underlying parallel implementation. We demonstrate the use of our skeleton on two different evolutionary computing applications: (1) computing the minimum of the Rastrigin function; and (2) solving an urban traffic optimisation problem. We show that we can obtain very good speedups (up to 142.44 ×× the sequential performance) on a variety of different parallel hardware, while requiring very little parallelisation effort.Publisher PDFPeer reviewe