Performance analysis of large-scale cosmology application on three cluster systems

A typical cosmological simulation requires a large amount of compute power, which is hard to satisfy with a single machine. Cluster systems provide the opportunity to execute such large-scale applications. In this paper, we investigate and analyze the performance of a large-scale production cosmology application, the ENZO code, on different cluster environments. Three cluster systems, each of them representing a widely-used cluster environment in the area of scientific computing, are used in this work: an IBM SP2 system at SDSC, an IA-64 Linux cluster at NCSA, and a SUN cluster at IIT. The performance is evaluated from three aspects: overall performance, communication characteristics, and load balancing characteristics. The experimental data shows that the cosmology performance on these clusters depends on the system performance and the application characteristics. The application performance on these clusters does not totally match the NPB measurement. Further, it seems that the IA-64 Linux cluster does not scale past 32 CPUs for this application.