: For technological reasons, in a realistic parallel computer the processors have to communicate via a communication network with bounded degree. Thus the question for a "good" communication network comes up. In this paper we present such a network, a universal parallel computer (UPC) with the following properties: (i) It has optimal time-loss, namely O(log c) for simulating networks of degree c. (We also prove the lower bound \Omega\Gammand/ c) for the time-loss.) (ii) We introduce the broadcast-capability (how many processors can be reached by one processor in i steps?) and demonstrate its influence on the number of processors needed for a simulation of a network with n processors. E.g. for broadcast-capability O(c i ) (e.g. networks with degree c), O(n 1+" log n) processors are needed (" ? 0 arbitrary) whereas O(n \Delta polylog(n)) processors suffice for networks with polynomial broadcast-capability (e.g. k-dimensional grids). (iii) The UPC is potentially infinite and has mu...

We give a brief description of what we consider to be data parallel programming and processing, trying to pinpoint the typical problems and pitfalls that occur. We then proceed with a short annotated history of data parallel programming, and sketch a taxonomy in which data parallel languages can be classified. Finally we present our own model of data parallel programming, which is based on the view of parallel data collections as functions. We believe that this model has a number of distinct advantages, such as being abstract, independent of implicitly assumed machine models, and general.

In this paper, we address the question how efficiently a single constant-degree processor network can simulate the computation of any constant-degree processor network. We show the following lower bound trade-off: If M is an arbitrary constant-degree processor network of size m that can simulate all constantdegree processor networks of size n with slowdown s, then m \Delta s = \Omega\Gamma n log m). Our trade-off holds for a very general model of simulations. It covers all previously considered models and all known techniques for simulations among networks. For m n, this improves a previous lower bound by a factor of log log n, proved for a weaker simulation model. For m ! n, this is the first non-trivial lower bound for this problem. In this case, this lower bound is asymptotically tight. 1 Introduction The Problem. In the past few years, the model of parallel processor networks has received much attention for solving time-critical tasks [8]. In this model of computation, the proce...