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Data parallel programming is the most widely adopted paradigm for a large class of problems on traditional multicomputers (see SPMD programming model sidebar, p. 23). Nevertheless, it is a very hard task to preserve efficiency when this style is adopted on a cluster of heterogeneous nodes having nonuniform and time varying computational powers. Very popular(More)
Distributed systems have the potentiality of becoming an alternative platform for parallel computations. However, there are still many obstacles to overcome, one of the most serious is that distributed systems typically consist of shared heterogeneous components with highly variable computational power. In this paper we present a load balancing support that(More)
Implementing efficient parallel programs on a network-based computing platform (hypercomputing) is still a challenge. This paper proposes a new Adaptive Data Distribution (ADD) support that avoids to the programmer the complex task of managing irregular data distributions and adapting them to the nonuniform and variable conditions of a shared platform. In(More)
This paper aims at improving the performance of parallel applications running on nondedicated distributed platforms through a dynamic load balancer which is kept hidden to the programmer. The support periodically checks the status of the platform and, if necessary, redistributes portions of the data domain from overloaded to underloaded nodes. Various(More)
Real parallel applications nd little beneets from code porta-bility that does not guarantee acceptable eeciency. In this paper, we describe the new features of a framework that allows the development of Single Program Multiple Data (SPMD) applications adaptable to different distributed-memory machines, varying from traditional parallel computers to networks(More)
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