Parallel, distributed and GPU computing technologies in single-particle electron microscopy
- Max Planck Institute for Biophysical Chemistry (Germany)
An introduction to the current paradigm shift towards concurrency in software. Most known methods for the determination of the structure of macromolecular complexes are limited or at least restricted at some point by their computational demands. Recent developments in information technology such as multicore, parallel and GPU processing can be used to overcome these limitations. In particular, graphics processing units (GPUs), which were originally developed for rendering real-time effects in computer games, are now ubiquitous and provide unprecedented computational power for scientific applications. Each parallel-processing paradigm alone can improve overall performance; the increased computational performance obtained by combining all paradigms, unleashing the full power of today’s technology, makes certain applications feasible that were previously virtually impossible. In this article, state-of-the-art paradigms are introduced, the tools and infrastructure needed to apply these paradigms are presented and a state-of-the-art infrastructure and solution strategy for moving scientific applications to the next generation of computer hardware is outlined.
- OSTI ID:
- 22347943
- Journal Information:
- Acta Crystallographica. Section D: Biological Crystallography, Vol. 65, Issue Pt 7; Other Information: PMCID: PMC2703572; PMID: 19564686; PUBLISHER-ID: ic5056; OAI: oai:pubmedcentral.nih.gov:2703572; Copyright (c) International Union of Crystallography 2009; This is an open-access article distributed under the terms described at http://journals.iucr.org/services/termsofuse.html.; Country of input: International Atomic Energy Agency (IAEA); ISSN 0907-4449
- Country of Publication:
- Denmark
- Language:
- English
Similar Records
Computational Materials Science and Chemistry: Accelerating Discovery and Innovation through Simulation-Based Engineering and Science
Optimization of sparse matrix-vector multiplication on emerging multicore platforms