In this paper we describe an implementation of some kinds of parallel genetic algorithms on the PVM, Parallel Virtual Machine, a portable parallel environment. We give details of a genetic algorithm running on many small subpopulations with an occasional identification and exchange of their useful

. We present an implementation of the functional language FASAN for automatic coarse-grain program parallelization on workstation clusters. It is designed primarily for recursive numerical algorithms with distributed tree-like data structures and it exploits the maximal inherent parallelism of a

This report describes a 1994 demonstration implementation of PVM that uses IP multicast. PVM's one-to-many unicast implementation of its pvm mcast() function is replaced with reliable IP multicast. Performance of PVM using IP multicast over local and wide-area networks is measured

for an interesting spectrum of reward functions that may not be so easily accessible by using only a single theory. For example, it seems that uncertainty plays a larger role in parts of microeconomic theory than in learning theory, and the investigation of neural mechanisms of uncertainty in outcomes of behavior

as space requirements. Figuring out the kind of computation is another way to view structure, e.g., partially separable or composite functions whose structure can be exploited to get performance gains. In this thesis we develop a general structure framework which can be viewed hierarchically and allows

We describe the parallelisation of the GMRES(c) method and its implementation on distributed-memory architectures, using both networks of transputers and networks of workstations under the PVM message-passing system. The test systems of linear equations considered are those derived from five

The computation of the distance function is a crucial and limiting element in many applications of image processing. This is particularly true for the PDE-based methods, where the distance is used to compute various geometric properties of the travelling curve. Massive Marching is a parallel

to formulate and programming. Therefore, there is a method of computation to pursue the variation of functional, Automatic Differentiation (AD). The AD is implemented with C++ programs using operator overloading technique. The partial derivatives are computed according to the differentiation rule of a

differentiation (AD), by which partial derivatives of a function can be computed automatically. Second order variation can be obtained by advancing AD. Here, this AD will be referred to as Second Order Automatic Differentiation (SOAD) for convenience, and ordinary AD is First Order Automatic Differentiation (FOAD

Abstract. A spectral method for solving linear partial differential equations (PDEs) with vari-able coefficients and general boundary conditions defined on rectangular domains is described, based on separable representations of partial differential operators and the one-dimensional ultraspherical