Search

Abstract: This paper evaluates the performance of bioinformatics applications on the Cell Broadband Engine (Cell/B.E.) recently developed at IBM. In particular we focus on three highly popular bioinformatics applications – FASTA, ClustalW, and HMMER. The characteristics of these bioinformatics applications, such as small critical time-consuming code size, regular memory accesses, existing vectorized code and embarrassingly parallel computation, make them uniquely suitable for the Cell/B.E. processing platform. The price and power advantages afforded by the Cell/B.E. processor also make it an attractive alternative to general purpose processors. We report preliminary performance results for these applications, and contrast these results with the state-of-the-art hardware. [Copyright &y& Elsevier]

Background: De novo transcriptome assembly is an important technique for understanding gene expression in non-model organisms. Many de novo assemblers using the de Bruijn graph of a set of the RNA sequences rely on in-memory representation of this graph. However, current methods analyse the complete set of read-derived k-mer sequence at once, resulting in the need for computer hardware with large shared memory. Results: We introduce a novel approach that clusters k-mers as the first step. The clusters correspond to small sets of gene products, which can be processed quickly to give candidate transcripts. We implement the clustering step using the MapReduce approach for parallelising the analysis of large datasets, which enables the use of compute clusters. The computational task is distributed across the compute system using the industry-standard MPI protocol, and no specialised hardware is required. Using this approach, we have re-implemented the Inchworm module from the widely used Trinity pipeline, and tested the method in the context of the full Trinity pipeline. Validation tests on a range of real datasets show large reductions in the runtime and per-node memory requirements, when making use of a compute cluster. Conclusions: Our study shows that MapReduce-based clustering has great potential for distributing challenging sequencing problems, without loss of accuracy. Although we have focussed on the Trinity package, we propose that such clustering is a useful initial step for other assembly pipelines. [ABSTRACT FROM AUTHOR]

With the right software infrastructure, clouds can provide scientists with "as a service" access to high-performance computing resources. An award-winning prototype framework transforms the Blue Gene/P system into an elastic cloud to run a representative HPC application. The Web extra at http://youtu.be/TidQezFUK0E is a video demonstrating an award-winning prototype framework that transforms the Blue Gene/P system into an elastic cloud to run a representative high-performance computing application. [ABSTRACT FROM PUBLISHER]