We present the initial results from the FHPCA Supercomputer project at the University of Edinburgh. The project has successfully built a general-purpose 64 FPGA computer and ported to it three demonstration applications from the oil, medical and finance sectors. This paper describes in brief the machine itselfMaxwell -its hardware and software environment and presents very early benchmark results from runs of the demonstrators.

BackgroundMicroarray analysis allows the simultaneous measurement of thousands to millions of genes or sequences across tens to thousands of different samples. The analysis of the resulting data tests the limits of existing bioinformatics computing infrastructure. A solution to this issue is to use High Performance Computing (HPC) systems, which contain many processors and more memory than desktop computer systems. Many biostatisticians use R to process the data gleaned from microarray analysis and there is even a dedicated group of packages, Bioconductor, for this purpose. However, to exploit HPC systems, R must be able to utilise the multiple processors available on these systems. There are existing modules that enable R to use multiple processors, but these are either difficult to use for the HPC novice or cannot be used to solve certain classes of problems. A method of exploiting HPC systems, using R, but without recourse to mastering parallel programming paradigms is therefore necessary to analyse genomic data to its fullest.ResultsWe have designed and built a prototype framework that allows the addition of parallelised functions to R to enable the easy exploitation of HPC systems. The Simple Parallel R INTerface (SPRINT) is a wrapper around such parallelised functions. Their use requires very little modification to existing sequential R scripts and no expertise in parallel computing. As an example we created a function that carries out the computation of a pairwise calculated correlation matrix. This performs well with SPRINT. When executed using SPRINT on an HPC resource of eight processors this computation reduces by more than three times the time R takes to complete it on one processor.ConclusionSPRINT allows the biostatistician to concentrate on the research problems rather than the computation, while still allowing exploitation of HPC systems. It is easy to use and with further development will become more useful as more functions are added to the framework.

The GridPP Collaboration is building a UK computing Grid for particle physics, as part of the international effort towards computing for the Large Hadron Collider. The project, funded by the UK Particle Physics and Astronomy Research Council (PPARC), began in September 2001 and completed its first phase 3 years later. GridPP is a collaboration of approximately 100 researchers in 19 UK university particle physics groups, the Council for the Central Laboratory of the Research Councils and CERN, reflecting the strategic importance of the project. In collaboration with other European and US efforts, the first phase of the project demonstrated the feasibility of developing, deploying and operating a Grid-based computing system to meet the UK needs of the Large Hadron Collider experiments. This note describes the work undertaken to achieve this goal. S Supplementary documentation is available from stacks.iop.org/JPhysG/32/N1. References to sections S1, S2.1, etc are to sections within this online supplement.

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