Blue Gene was designed as a special purpose system, offering unprecedented computing performance coupled with very low power consumption and cost for a limited set of applications. Adoption has however become more widespread and Blue Gene is now being used in a relatively diverse range of scientific disciplines. Exploring the range and type of application that can make effective use of these systems, and the relevant architectural features influencing performance, is therefore highly topical.This tutorial will focus on the performance of a series of applications and consider the techniques required to achieve optimal performance and scaling on the Blue Gene architecture. We will discuss the factors and bottlenecks that influence performance and cover techniques for compiler, memory and communication optimization.Attendees will have the opportunity to gain hands on experience through a series of practical sessions on The University of Edinburgh's Blue Gene system.

Abstract We introduce HPCx—the U.K.'s new National HPC Service—which aims to deliver a world‐class service for capability computing to the U.K. scientific community. HPCx is targeting an environment that will both result in world‐leading science and address the challenges involved in scaling existing codes to the capability levels required. Close working relationships with scientific consortia and user groups throughout the research process will be a central feature of the service. A significant number of key user applications have already been ported to the system. We present initial benchmark results from this process and discuss the optimization of the codes and the performance levels achieved on HPCx in comparison with other systems. We find a range of performance with some algorithms scaling far better than others. Copyright © 2005 John Wiley & Sons, Ltd.

An OpenMP version of a Quantum Monte Carlo (QMC) code has been developed. The original parallel MPI version of the QMC code was developed by the Electronic Structure of Solids HPCI consortium in collaboration with EPCC. This code has been highly successful, and has resulted in numerous publications based on results generated on the National Cray MPP systems at EPCC. Recent interest has focused on also utilizing shared-memory parallelism in the code since future HPC systems are expected to comprise clusters of SMP nodes. The code has been rewritten to allow for an arbitrary mix of OpenMP and MPI parallelism. The various issues which arose during the parallelization are discussed. The performance of the mixed OpenMP/MPI code has been assessed on an SGI Origin 2000 system and the results compared and contrasted to the original MPI version. Copyright © 2000 John Wiley & Sons, Ltd.