Computational and data Grids in large-scale science and engineering
Future Generation Computer Systems - Grid computing: Towards a new computing infrastructure
The Telescience Portal for advanced tomography applications
Journal of Parallel and Distributed Computing - Special issue on computational grids
Global telescience featuring IPv6 at iGrid2002
Future Generation Computer Systems - iGrid 2002
Journal of Parallel and Distributed Computing
Performance Evaluation in Computational Grid Environments
HPCASIA '04 Proceedings of the High Performance Computing and Grid in Asia Pacific Region, Seventh International Conference
Communications of the ACM - Bioinformatics
Concurrency and Computation: Practice & Experience
Benchmarking of high throughput computing applications on Grids
Parallel Computing
Future trends in distributed applications and problem-solving environments
Future Generation Computer Systems - Special section: Complex problem-solving environments for grid computing
Introducing grid speedup Γ: a scalability metric for parallel applications on the grid
EGC'05 Proceedings of the 2005 European conference on Advances in Grid Computing
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Electron tomography allows elucidation of the three-dimensional structure of large complex biological specimens at molecular resolution. To achieve such resolution levels, sophisticated algorithms for tomographic reconstruction are needed. Iterative algebraic algorithms yield high quality reconstructions, but they are computationally expensive and high performance techniques are needed to exploit them in practice. We present here a grid computing approach for tomographic reconstruction of large biological specimens. The approach is based on the computational Single-Program-Multiple-Data model, which basically decomposes the global problem into a number of independent 3D reconstruction subproblems. New performance metrics and job submission policies are proposed here that could be of general interest in the field of Grid Computing. We have evaluated this approach on the grid hosted by the European EGEE (Enabling Grids for E-sciencE) project. The influence of the problem size and the parallelism grain has been thoroughly analyzed. Our results demonstrate that the grid is better suited for large reconstructions, as currently needed in electron tomography. To fully exploit the potential of computational grids, the global problem should be decomposed into an adequate number of subdomains.