417 research outputs found
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 sugegs
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
Toward a Formal Semantics for Autonomic Components
Autonomic management can improve the QoS provided by parallel/ distributed
applications. Within the CoreGRID Component Model, the autonomic management is
tailored to the automatic - monitoring-driven - alteration of the component
assembly and, therefore, is defined as the effect of (distributed) management
code. This work yields a semantics based on hypergraph rewriting suitable to
model the dynamic evolution and non-functional aspects of Service Oriented
Architectures and component-based autonomic applications. In this regard, our
main goal is to provide a formal description of adaptation operations that are
typically only informally specified. We contend that our approach makes easier
to raise the level of abstraction of management code in autonomic and adaptive
applications.Comment: 11 pages + cover pag
Analyzing FOSS license usage in publicly available software at scale via the SWH-analytics framework
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
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