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

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Abstract. Large-scale parallel computations are more common than ever, due to the increasing availability of multi-processor systems. However, writing parallel software is often a complicated and error-prone task. To relieve Diffpack users of the tedious and low-level technical details of parallel

We survey parallel programming models and languages using 6 criteria [:] should be easy to program, have a software development methodology, be architecture-independent, be easy to understand, guranatee performance, and provide info about the cost of programs. ... We consider programming models

Parallel discrete event simulation (PDES), sometimes I called distributed simulation, refers to the execution of a single discrete event simulation program on a parallel computer. PDES has attracted a considerable amount of interest in recent years. From a pragmatic standpoint, this interest arises

The PVM system is a programming environment for the development and execution of large concurrent or parallel applications that consist of many interacting, but relatively independent, components. It is intended to operate on a collection of heterogeneous computing elements interconnected by one

. This paper reviews the fundamentals of parallel computing with a focus on implicit, or automatic, parallelization. We discuss different software techniques as well as restrictions that arise when creating parallel code. 1.

Parallel computation, cluster, evolutionary algorithm,

It is difficult to achieve elegance, efficiency and parallelism simultaneously in functional programs that manipulate large data structures. We demonstrate this through careful analysis of program examples using three common functional data-structuring approaches--- lists using Cons and arrays

We present methods for the computation of multivariate normal probabilities on parallel/ distributed systems. After a transformation of the initial integral, an approximation can be obtained using Monte-Carlo or quasirandom methods. We propose a meta-algorithm for asynchronous sampling methods