Abstract
We define a lexically scoped, asynchronous and distributed p-calculus, with local communication and process migration. This calculus adopts the network-awareness principle for distributed programming and follows a simple model of distribution for mobile calculi: a lexical scope discipline combines static scoping with dynamic linking, associating channels to a fixed site throughout computation. This discipline provides for both remote invocation and process migration. A simple type system is a straightforward extension of that of the p-calculus, adapted to take into account the lexical scope of channels. An equivalence law captures the essence of this model: a process behavior depends on the channels it uses, not on where it runs. This work was partially supported by the Portuguese Fundação para a Ciencia e a Tecnologia (via CLC, the project MIMO, POSI/CHS/39789/2001, and scholarships POCTI/SFRH/BPD/6782/2001 and POSI/SFRH/BD/7100/20- 01), by the EU FEDER (via CLC) and the EU IST proactive initiative FET- Global Computing (projects Mikado, IST-2001-32222, and Profundis, IST- 2001-33100). We thank Gérard Boudol, Ilaria Castellani, Matthew Hennessy and Francisco Martins, as well as the anonymous referees, for their comments. © 2003 Published by Elsevier Science B.V.

Abstract
We describe a linear channel inference system for the TyCO programming language, where channel usage is tracked through method invocations as well as definition instantiations. We then apply linear channel information to optimize code generation for a multithreaded runtime system. The impact in terms of speed and space is analyzed. © 2003 Published by Elsevier Science B.V.

Abstract
Progress in Prolog applications requires ever better performance and scalability from Prolog implementation technology. Most modern Prolog systems are emulator-based. Best performance thus requires both good emulator design and good memory performance. Indeed, Prolog applications can often spend hundreds of megabytes of data, but there is little work on understanding and quantifying the interactions between Prolog programs and the memory architecture of modern computers. In a previous study of Prolog systems we have shown through simulation that Prolog applications usually, but not always, have good locality, both for deterministic and non-deterministic applications. We also showed that performance may strongly depend on garbage collection and on database operations. Our analysis left two questions unanswered: how well do our simulated results holds on actual hardware, and how much did our results depend on a specific configuration? In this work we use several simulation parameters and profiling counters to improve understanding of Prolog applications. We believe that our analysis is of interest to any system implementor who wants to understand his or her own system's memory performance.

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Abstract

Abstract
Large-scale applications that require executing very large numbers of tasks are only feasible through parallelism. In this work we present a system that automatically handles large numbers of experiments and data in the context of machine learning. Our system controls all experiments, including re-submission of failed jobs and relies on available resource managers to spawn jobs through pools of machines. Our results show that we can manage a very large number of experiments, using a reasonable amount of idle CPU cycles, with very little user intervention.