GPU-Accelerated Simulation Ensembles of Stochastic Reaction Networks

Köster, Till; Herrmann, Leon; Andelfinger, Philipp; Uhrmacher, Adelinde M.

doi:10.1109/wsc57314.2022.10015448

2022 Winter Simulation Conference (WSC) 2022

DOI: 10.1109/wsc57314.2022.10015448

|View full text |Cite

GPU-Accelerated Simulation Ensembles of Stochastic Reaction Networks

Till Köster

Leon Herrmann

Philipp Andelfinger

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

FPGA Implementation of Reaction Systems

Shang,

Verlan,

et al. 2024

Electronics

View full text Add to dashboard Cite

Reaction system (RS) belongs to a type of qualitative computing model inspired by biochemical reactions taking place inside biological cells. It concerns more the interactions and causality among reactions rather than concrete concentrations of chemical entities. Many biochemical processes and models can be represented in the form of reaction systems so that complex relations and ultimate products of a variety of reactions can be revealed qualitatively. The reaction system works in parallel mode. Software simulation of this kind of model may suffer from the penalty of inefficient parallelism for the limited performance of CPU/GPU, especially for the simulation of large-scale models. Considering potential applications of reaction systems in disease diagnoses and in drug developments, hardware implementation of reaction systems provides a better way to accelerate computations involved. In this paper, an FPGA implementation method of a reaction system called RSFIM is proposed. Two small-scale models, i.e., the reaction system of intermediate filaments self-assembly and heat shock response, are implemented on FPGA, achieving a computing speed of 2×108 steps per second. For large-scale models, the ErbB reaction system is implemented, obtaining a speedup of 7.649×104 compared with its highest performance GPU simulation so far. The reaction system binary counter, which is a quantitative model, is also implemented by the Boolean explanation of the qualitative character of the reaction system. FPGA implementation of reaction systems opens a novel research line to speed up the simulations of reaction systems and other biological models in the perspective of parallel digital circuits.

show abstract

FPGA Implementation of Reaction Systems

Shang,

Verlan,

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

GPU-Accelerated Simulation Ensembles of Stochastic Reaction Networks

Cited by 1 publication

References 26 publications

FPGA Implementation of Reaction Systems

FPGA Implementation of Reaction Systems

Contact Info

Product

Resources

About