When significant communication costs arise in the solution of multidimensional problems on parallel computers, optimal performance cannot always be achieved by perfectly balancing the computational load across cores. Modest sacrifices in the computational load balance may facilitate substantial overall performance improvements by achieving large savings in the costs associated with communications. This general approach is illustrated by application to GS2, an initial value gyrokinetic simulation code developed to study low-frequency turbulence in magnetized plasma. GS2 is parallelised using MPI with the simulation domain decomposed across tasks. The optimal domain decomposition is non-trivial, and is complicated by the fact that several domain decompositions are needed and that these do not all optimise at the chosen task count. Application to GS2, of the novel approach outlined in this paper, has improved performance by up to 17 percent for a representative simulation. Similar strategies may be beneficial in a broader class of problems.

We have previously documented the on-going work in the EUFORIA project to parallelise and optimise European fusion simulation codes, see. This involves working with a wide range of codes to try and address any performance and scaling issues that these codes have. However, as no two simulation codes are exactly the same, it is very hard to apply exactly the same approach to optimising a disparate range of codes. Indeed, it can be seen from that the codes investigated range in terms of performance and ability from well-optimised, highly parallelised codes, to serial or poorly performing codes. After analysing, optimising, and parallelising a range of codes it is, actually, possible to discern a number of distinct optimisation techniques or approaches/strategies that can be used to improve the performance or scaling of a parallel simulation code. This paper outlines the distinct approaches that we have identified, highlighting their benefits and drawbacks, giving an overview of the type of work that is often attempted for fusion simulation code optimisation.

Parallelisation, serial optimisation, compiler tuning, and many more techniques are used to optimise and improve the performance scaling of parallel programs. One area which is frequently not optimised is file I/O. This is because it is often not considered to be key to the performance of a program and also because it is traditionally difficult to optimise and very machine specific. However, in the current era of Peta- and Exascale computing it is no longer possible to ignore I/O performance as it can significantly limit the scaling of many codes when executing on very large numbers of processors or cores. Furthermore, as producing data is the main purpose of most simulation codes any work that can be undertaken to provide improved performance of I/0 can be applicable to a very large range of simulation codes, and provide them with improved functionality (i.e. the ability to produce more data).This paper describes some of the issues surrounding I/O, the technology that is commonly deployed to provide I/O on HPC machines and the software libraries available to programmers to undertake I/O. The performance of all these aspects of I/O on a range of HPC systems were investigated by the authors and a represented in this paper to motivate the discussions in the paper.

One of the central tasks of EUFORIA is to port, parallelise, and optimise fusion simulation codes, developed at individual research institutes in Europe. There are three supercomputer centres involved in the project located at Barcelona, Edinburgh, and Helsinki. For some of the fusion codes simply porting them to one of the supercomputers represents a major advancement in the use of the codes, as they until now have mainly been used by a small user community, or even exclusively by the author of the code. Also, where codes currently can only use one processor (i. e. are serial) providing any parallel functionality can be of major benefit to the code and the code owner(s). Many of the simulation codes for edge and core transport modelling of fusion plasma using high performance computing are estimated to currently require weeks or months of execution time to simulate science at a scale required to model the new fusion reactor ITER, and therefore these codes have to be optimised to run as fast as possible and parallelised in such a way that computer resources are used as effectively as possible. During the first fifteen month of the project, we have successfully ported eleven fusion codes to the supercomputers in Barcelona, Edinburgh and Helsinki. The installation procedure, library requirements and runtime scripts have been documented for each code, and deposited in the EUFORIA software repository and code revision system. Following this a number of these codes have been chosen for code optimisation and improvements in parallelisation and this paper outlines the experience that we have had with some of these codes, the performance improvements achieved, and the techniques used.

For many scientific applications, the fast Fourier transformation (FFT) of multi-dimensional data is the kernel that limits scalability on a large number of processors. This paper investigates the extent of performance improvements for a parallel three-dimensional FFT (3D-FFT) implementation when using customized MPI task mappings. The MPI tasks are mapped in a customized fashion from the two-dimensional virtual processor grid of the algorithm to the physical hardware of a system with a mesh interconnect. We compare and analyze the outcomes on Blue Gene/P with those from previous investigations on Blue Gene/L. The performance analysis is based on bandwidth considerations. The results demonstrate that on Blue Gene/P, a carefully chosen MPI task mapping with regards to the network characteristics is more important compared to Blue Gene/L and yields significant improvement.

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.