Eunice E. Santos

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A vast body of theoretical research has focused either on overly simplistic models of parallel computation, notably the PRAM, or overly specific models that have few representatives in the real world. Both kinds of models encourage exploitation of formal loopholes, rather than rewarding development of techniques that yield performance across a range of(More)
enough to be generally useful and to keep the algorithm analysis tractable. Ideally, producing a better algorithm under the model should yield a better program in practice. The Parallel Random Access Machine (PRAM) [8] is the most popular model for representing and analyzing the complexity of parallel algorithms. A LogP A Practic Parallel
In many distributed-memory parallel computers the only built-in communication primitive is point-to-point message transmission, and more powerful operations such as broadcast and synchronization must be realized using this primitive. Within the LogP model of parallel computation we present algorithms that yield optimal communication schedules for several(More)
Summary form only given. We design parallel Monte Carlo algorithms for the Ising spin model on a hierarchical cluster. A hierarchical cluster can be considered as a cluster of homogeneous nodes which are partitioned into multiple supernodes such that communication across homogenous clusters are represented by a supernode topological network. We consider(More)
Since many distributed-memory machines rely only on point-to-point communication between processors, various broadcast operations must be created using this type of primitive. In this paper we consider the fundamental problem of broadcasting k-items from one processor to all the remaining processors on a parallel machine. Using point-to-point communication(More)
With the proliferation of the Internet and rapid development of information and communication infrastructure, E-governance has become a viable option for effective deployment of government services and programs. Areas of E-governance such as Homeland security and disaster relief have to deal with vast amounts of dynamic heterogeneous data. Providing rapid(More)
We address the problem of information fusion in uncertain environments. Imagine there are multiple experts building probabilistic models of the same situation and we wish to aggregate the information they provide. There are several problems we may run into by naively merging the information from each. For example, the experts may disagree on the probability(More)