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https://dx.doi.org/10.48550/ar...
Article . 2013
License: arXiv Non-Exclusive Distribution
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GPU-powered Simulation Methodologies for Biological Systems

Authors: BESOZZI, DANIELA; CARAVAGNA, GIULIO; CAZZANIGA, PAOLO; NOBILE, MARCO SALVATORE; PESCINI, DARIO; Re, A.;

GPU-powered Simulation Methodologies for Biological Systems

Abstract

The study of biological systems witnessed a pervasive cross-fertilization between experimental investigation and computational methods. This gave rise to the development of new methodologies, able to tackle the complexity of biological systems in a quantitative manner. Computer algorithms allow to faithfully reproduce the dynamics of the corresponding biological system, and, at the price of a large number of simulations, it is possible to extensively investigate the system functioning across a wide spectrum of natural conditions. To enable multiple analysis in parallel, using cheap, diffused and highly efficient multi-core devices we developed GPU-powered simulation algorithms for stochastic, deterministic and hybrid modeling approaches, so that also users with no knowledge of GPUs hardware and programming can easily access the computing power of graphics engines.

In Proceedings Wivace 2013, arXiv:1309.7122

Country
Italy
Keywords

FOS: Computer and information sciences, QA75.5-76.95, GPU computing, Computational Engineering, Finance, and Science (cs.CE), Computer Science - Distributed, Parallel, and Cluster Computing, Electronic computers. Computer science, QA1-939, Distributed, Parallel, and Cluster Computing (cs.DC), Computer Science - Computational Engineering, Finance, and Science, Mathematics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
Green
gold