Porting Decision Tree Algorithms to Multicore using FastFlow

The whole computer hardware industry embraced multicores. For these machines, the extreme optimisation of sequential algorithms is no longer sufficient to squeeze the real machine power, which can be only exploited via thread-level parallelism. Decision tree algorithms exhibit natural concurrency that makes them suitable to be parallelised. This paper presents an approach for easy-yet-efficient porting of an implementation of the C4.5 algorithm on multicores. The parallel porting requires minimal changes to the original sequential code, and it is able to exploit up to 7X speedup on an Intel dual-quad core machine.Comment: 18 pages + cove

Integration of DFDs into a UML - based model-driven engineering approach

The main aim of this article is to discuss how the functional and the object-oriented views can be inter-played to represent the various modeling perspectives of embedded systems.We discuss whether the object-oriented modeling paradigm, the predominant one to develop software at the present time, is also adequate for modeling embedded software and how it can be used with the functional paradigm.More specifically, we present how the main modeling tool of the traditional structured methods, data flow diagrams, can be integrated in an object-oriented development strategy based on the unified modeling language. The rationale behind the approach is that both views are important for modeling purposes in embedded systems environments, and thus a combined and integrated model is not only useful, but also fundamental for developing complex systems. The approach was integrated in amodel-driven engineering process, where tool support for the models used was provided. In addition, model transformations have been specified and implemented to automate the process.We exemplify the approach with an IPv6 router case study.FEDER -Fundação para a Ciência e a Tecnologia(HH-02-383

Examples of Fuzziness in Compilers and Runtime Systems

In this paper, I review some techniques used in compilers and runtime systems for parallel and distributed computation, involving aspects of fuzziness. Fuzziness is in a general sense considered as being present if the techniques applied classify, rate or otherwise handle information with some range of tolerance, if they operate with similarities, uncertainties, or do not seek for perfect/optimal solutions (but apply heuristics or approximations). In many cases, fuzziness, in this sense, is already used but may need a better theoretical basis; and in others, its integration may improve approaches, opens up new perspectives or become relevant for future applications, as in distributed computing. In this paper, I focus on the potential application of fuzzy theory, providing a simplified -- but in many cases sufficiently accurate -- approach on sound theoretical principles. Other important theoretical approaches -- as far as they are applied in compilers for dealing with approximateness..

Extracting Characteristics from Functional Programs for Mapping to Massively Parallel Machines

For problems with highly dynamic behavior, our experiments showed that there are specific characteristics for different applications. We therefore propose a mapping environment providing several strategies for both granularity control and dynamic load balancing. For appropriate selection and parameterization of strategies, we extract the application characteristics using trace-based profiling and appropriate evaluations. Our profiling approach differs from others in that more information (like argument sizes or the branching factor) is collected and more advanced evaluations are performed. Large real-life applications have been successfully measured, and examples demonstrate differences in characteristics and which the corresponding strategies being appropriate are. 1 Introduction Although functional programs, by definition, provide a rich source of parallelism because of the absence of side-effects, they nevertheless pose similar problems as encountered with procedural programs for c..

On-Line Task Granularity Adaptation for Dynamic Grid Applications

Deploying lightweight tasks on grid resources would let the communication overhead dominate the overall application processing time. Our aim is to increase the resulting computation-communication ratio by adjusting the task granularity at the grid scheduler. We propose an on-line scheduling algorithm which performs task grouping to support an unlimited number of user tasks, arriving at the scheduler at runtime. The algorithm decides the task granularity based on the dynamic nature of a grid environment: task processing requirements; resource-network utilisation constraints; and users QoS requirements. Simulation results reveal that our algorithm reduces the overall application processing time and communication overhead significantly while satisfying the runtime constraints set by the users and the resources