The National Center for Supercomputing Applications (NCSA) at the University of Illinois typifies current, high-performance computing centers that support a diverse user base of both academic and industrial accounts. The data requirements of this user community, coupled with resource constraints, mandate the use of an archival file system. This paper analyzes the spatial and temporal patterns of file archive transactions on the NCSA file archive. Analysis of archive transaction traces shows that the access patterns differ from those observed during studies of secondary storage systems: the files are larger, there are large temporal and spatial variations in transaction patterns, and a small number of research groups can easily account for twenty percent of the total traffic.

Abstract Among the many techniques in computer graphics, ray tracing is prized because it can render realistic images, albeit at great computational expense. Ray tracing's large computation requirements, coupled with its inherently parallel nature, make ray tracing algorithms attractive candidates for parallel implementation. In this paper we illustrate the utility and the importance of a suite of performance analysis tools when exploring the performance of several approaches to ray tracing on a distributed memory parallel system. These ray tracing algorithm variations introduce parallelism based on both ray and object partitions. Traditional timing analysis can quantify the performance effects of different algorithm choices (i.e. when an algorithm is best matched to a given problem), but it cannot identify the causes of these performance differences. We argue, by example, that a performance instrumentation system is needed that can trace the execution of distributed memory parallel programs by recording the occurrence of parallel program events. The resulting event traces can be used to compile summary stapistics that provide a global view of program performance. In addition, visualization tools permit the graphic display of event traces. Visual presentation of performance data is particularly useful, indeed, necessary for large‐scale, parallel computations; assimilating the enormous volume of performance data mandates visual display.

Among the many techniques in computer graphics, ray tracing is prized because it can render realistic images, albeit at great computational expense. In this note we explore the performance of several approaches to ray tracing on a distributed memory parallel system. A set of performance instrumentation tools and their associated visualization software are used to identify the underlying causes of performance differences.