Suitability of the next generation of high-performance computing systems for petascale simulations will depend on a balance between factors such as processor performance, memory performance, local and global network performance, and Input/Output (I/O) performance. As the supercomputing industry develops new technologies for these subsystems, achieving system balance becomes challenging. In this paper, we evaluate the performance of a newly introduced dual-core-based SGI Altix 4700 system and we compare its performance with that of a single-core-based SGI Altix 3700 Bx2 system. We used the High-Performance Computing Challenge (HPCC) benchmarks and five real-world applications, three from computational fluid dynamics, one from climate modeling and one from nanotechnology. Our study shows that the SGI Altix 4700 performs slightly better than the SGI Altix 3700 Bx2 up to 128 processors, while the performance of the systems is almost the same beyond 128 processors, when the communication time dominates the compute time.

The suitability of next-generation high-performance computing systems for petascale simulations will depend on various performance factors attributable to processor, memory, local and global network, and input/output characteristics. In this paper, we evaluate performance of new dual-core SGI Altix 4700, quad-core SGI Altix ICE 8200, and dual-core IBM POWER5+ systems. To measure performance, we used micro-benchmarks from High Performance Computing Challenge (HPCC), NAS Parallel Benchmarks (NPB), and four real-world applications- three from computational fluid dynamics (CFD) and one from climate modeling. We used the micro-benchmarks to develop a controlled understanding of individual system components, then analyzed and interpreted performance of the NPBs and applications. We also explored the hybrid programming model (MPI+OpenMP) using multi-zone NPBs and the CFD application OVERFLOW-2. Achievable application performance is compared across the systems. For the ICE platform, we also investigated the effect of memory bandwidth on performance by testing 1, 2, 4, and 8 cores per node.

Many scientific applications running on today's supercomputers deal with increasingly large data sets and are correspondingly bottlenecked by the time it takes to read or write the data from/to the file system. We therefore undertook a study to characterize the parallel I/O performance of two of today's leading parallel supercomputers: the Columbia system at NASA Ames Research Center and the NEC SX-8 supercluster at the University of Stuttgart, Germany. On both systems, we ran a total of seven parallel I/O benchmarks, comprising five low-level benchmarks: (i) IO_Bench, (ii) MPI Tile IO, (iii) IOR (POSIX and MPI-IO), (iv) b_eff_io (five different patterns), and (v) SPIOBENCH, and two scalable synthetic compact application (SSCA) benchmarks: (a) HPCS (high productivity computing systems) SSCA #3 and (b) FLASH IO (parallel HDF5). We present the results of these experiments characterizing the parallel I/O performance of these two systems.