Book Chapter10.1007/978-3-030-22744-9_35
Improving ODE Integration on Graphics Processing Units by Reducing Thread Divergence
Thomas Kovac,Tom Haber,Frank Van Reeth,Niel Hens +3 more
- 12 Jun 2019
- pp 450-456
1
TL;DR: A general-purpose integrator that runs massively parallel on graphics processing units by minimizing thread divergence and bundling similar tasks using linear regression, execution time can be reduced by 40–80% when compared to a naive GPU implementation.
read more
Abstract: Ordinary differential equations are widely used for the mathematical modeling of complex systems in biology and statistics. Since the analysis of such models needs to be performed using numerical integration, many applications can be gravely limited by the computational cost. This paper present a general-purpose integrator that runs massively parallel on graphics processing units. By minimizing thread divergence and bundling similar tasks using linear regression, execution time can be reduced by 40–80% when compared to a naive GPU implementation. Compared to a 36-core CPU implementation, a 150 fold runtime improvement is measured.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Optimization Techniques for GPU Programming
TL;DR: In this article , a survey discusses various optimization techniques found in 450 articles published in the last 14 years and analyzes the optimizations from different perspectives which shows that the various optimizations are highly interrelated, explaining the need for techniques such as auto-tuning.
54
References
Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Sequential Monte Carlo methods in practice
Arnaud Doucet,Nando de Freitas,Neil Gordon,Adrian F. M. Smith +3 more
- 01 Jan 2001
TL;DR: This book presents the first comprehensive treatment of Monte Carlo techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection.
Introduction to Evolutionary Computing
Agoston E. Eiben,James C. Smith +1 more
- 01 Jan 2015
TL;DR: In the second edition, the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations as discussed by the authors.
5.2K
•Book
Introduction to evolutionary computing
Agoston E. Eiben,James C. Smith +1 more
- 01 Jan 2003
TL;DR: The authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations, and added a chapter on evolutionary robotics with an outlook on possible exciting developments in this field.
A family of embedded Runge-Kutta formulae
J. R. Dormand,P.J. Prince +1 more
TL;DR: In this article, a family of embedded Runge-Kutta formulae RK5 (4) are derived from these and a small principal truncation term in the fifth order and extended regions of absolute stability.
3.7K