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(1984) Andrew G. Knapp and James A. Anderson, "Theory of categorization based on distributed memory storage," Journal of Experimental Psychology: Learning, Memory, and Cognition 10 616-637.
The second implementation of the parallelized Lucas-Kanade algorithm is based on distributed memory with message-passing communications.
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Designing multiprocessors based on distributed shared memory (DSM) architecture considerably increases their scalability.
Parallelization has been carried out in a generic way in order to be sufficiently general to serve for different architectures, either based on distributed or on shared memory.
To show the advantages of an implementation of MPI on top of a distributed shared memory, this chapter describes MPISH, an implementation of MPI on top of DVSA, a package to emulate a shared memory on distributed memory architecture.
We demonstrate good scalability of the solver on distributed memory parallel processor with up to 2048 processors.
To handle the computational load, we present a parallel version of the model that runs on distributed memory computer clusters.
First, it can be executed efficiently both on shared memory architectures -for instance multicore processors- and on distributed memory architectures, such as clusters.
Its architecture is based on distributed caching layer, which enables the aggregation of spare memory from multiple nodes.
The parallelization is based on the distributed memory architecture and the message passing interface (MPI) protocol.
Recently, scalable machines based on logically shared physically distributed memory have been designed and implemented.
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