This paper presents a parallel ray-tracing algorithm in order to compute very large models (more than 100 million triangles) with distributed computer architecture. On a single computer, the size of the used dataset generates an out of core computation. Cluster architectures designed with off-the-shelf components offer extended capacities which allow to keep the large dataset inside the aggregated main memories. Then, to achieve scalability of operational applications, the real challenge is to exploit efficiently the amount of available memory and computing power. Ray-tracing, for high quality image rendering, spawns non-coherent rays which generate irregular tasks difficult to distribute on such architectures. In this paper we present a cache mechanism for main memory management distributed on each parallel computer and we implement a load balancing solution based on an auto adaptive algorithm to distribute the computation efficiently.

This paper is based on a project sponsored by the French Ministry of Defense (STPA) The authors are researchers at the Centre d'Etudes et Recherches de Toulouse, a department of ONERA, Office National d'Etudes et Recherches A6ronautiques.Abstract: We describe the MaRS machine: a parallel, distributed control multiprocessor for graph reduction using a functional machine language. The object code language is based on an optimized set of combinators, and its functional character allows an automatic parallelisation of the execution. A programming language, "MARS LISP", has also been developed. A prototype of MaRS is currently being designed in VLSI 1. 5-micron CMOS technology with 2 levels of metal, by means of a CAD system. The machine uses three basic types of processors for Reduction, Memory and Communication, plus auxiliary 1/0 and Arithmetic Processors; communications do not constitute an operational bottleneck, as interprocessor messages are routed via an Omega switching network. Initially, a HostComputer will be used for startup, testing and direct memory access. The machine architecture and its functional organization are described, as well as the theoretical execution model. We conclude on a number of specialized hardware and software mechanism that differentiate MaRS machine from other similar projects currently going on.

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