Parallel implementation of a ray tracing algorithm for distributed memory parallel computers

Abstract

Ray tracing is a well known technique to generate life‐like images. Unfortunately, ray tracing complex scenes can require large amounts of CPU time and memory storage. Distributed memory parallel computers with large memory capacities and high processing speeds are ideal candidates to perform ray tracing. However, the computational cost of rendering pixels and patterns of data access cannot be predicted until runtime. To parallelize such an application efficiently on distributed memory parallel computers, the issues of database distribution, dynamic data management and dynamic load balancing must be addressed. In this paper, we present a parallel implementation of a ray tracing algorithm on the Intel Delta parallel computer. In our database distribution, a small fraction of database is duplicated on each processor, while the remaining part is evenly distributed among groups of processors. In the system, there are multiple copies of the entire database in the memory of groups of processors. Dynamic data management is acheived by an ALRU cache scheme which can exploit image coherence to reduce data movements in ray tracing consecutive pixels. We balance load among processors by distributing subimages to processors in a global fashion based on previous workload requests. The success of our implementation depends crucially on a number of parameters which are experimentally evaluated. © 1997 John Wiley & Sons, Ltd.

Gravitational microlensing: A parallel, large-data implementation

“…For the supercomputer implementation, the rays that are shot need to be distributed across the available processors. There are many ways of parallelizing ray-tracing (Amri, 2003;Lee et al, 1998;Verdu et al, 1996) but little experience with this in gravitational lensing simulations. It does not matter which ray is executed on which processor, or in what order, as they are completely independent.…”