Investigating the applications and analysis of physics engine technologies

Research Article
Open access

Investigating the applications and analysis of physics engine technologies

Sheng Chen 1*
  • 1 Northeastern University    
  • *corresponding author lrice72961@student.napavalley.edu
Published on 21 February 2024 | https://doi.org/10.54254/2755-2721/40/20230654
ACE Vol.40
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-305-0
ISBN (Online): 978-1-83558-306-7

Abstract

This research project delves into the performance impact of implementing parallel programming techniques in physics engine applications. With the advent of multi-core processors in contemporary computing environments, optimizing physics simulations through parallel programming has become increasingly feasible. A conventional blob collision physics engine serves as the benchmark for evaluation, and its performance is juxtaposed against a parallel-programmed variant. Experimental findings indicate a significant reduction in computational time required for collision detection and response when parallel processing is employed. This efficiency gain is particularly pronounced in scenarios involving a large number of blobs, showcasing the scalability advantages of parallelization. Moreover, parallel programming facilitates optimal harnessing of multi-core processor capabilities, thereby enhancing the overall efficiency and performance of the physics engine in question. This study not only substantiates the technical merits of applying parallel programming but also illuminates the practical benefits, including resource-efficient operation and quicker simulation times. Consequently, the research provides valuable insights for developers and engineers aiming to fully exploit the capabilities of modern computing hardware in physics-based simulations and applications.

Keywords:

Physics Engine, Collision Detection, Parallel Programming, Parallel Optimization, OpenMP, Speedup

Chen,S. (2024). Investigating the applications and analysis of physics engine technologies. Applied and Computational Engineering,40,224-233.
Export citation

References

[1]. Vasheghani Farahani M, Foroughi S, Norouzi S, et al. Mechanistic study of fines migration in porous media using lattice Boltzmann method coupled with rigid body physics engine[J]. Journal of Energy Resources Technology, 2019, 141(12): 123001.

[2]. Freeman C D, Frey E, Raichuk A, et al. Brax--A Differentiable Physics Engine for Large Scale Rigid Body Simulation[J]. arXiv preprint arXiv:2106.13281, 2021.

[3]. Millington I. Game physics engine development[M]. CRC Press, 2007.

[4]. Vasheghani Farahani M, Foroughi S, Norouzi S, et al. Mechanistic study of fines migration in porous media using lattice Boltzmann method coupled with rigid body physics engine[J]. Journal of Energy Resources Technology, 2019, 141(12): 123001.

[5]. Quinn M J. Parallel programming[J]. TMH CSE, 2003, 526: 105.

[6]. Culler D E, Dusseau A, Goldstein S C, et al. Parallel programming in Split-C[C]//Supercomputing'93: Proceedings of the 1993 ACM/IEEE conference on Supercomputing. IEEE, 1993: 262-273.

[7]. Gilles K. The semantics of a simple language for parallel programming[J]. Information processing, 1974, 74(471-475): 15-28.

[8]. Chandra R. Parallel programming in OpenMP[M]. Morgan kaufmann, 2001.

[9]. Huber J, Cornelius M, Georgakoudis G, et al. Efficient execution of OpenMP on GPUs[C]//2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2022: 41-52.

[10]. Yviquel H, Pereira M, Francesquini E, et al. The OpenMP Cluster Programming Model[C]//Workshop Proceedings of the 51st International Conference on Parallel Processing. 2022: 1-11.

[11]. Doerfert J, Patel A, Huber J, et al. Co-Designing an OpenMP GPU runtime and optimizations for near-zero overhead execution[C]//2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2022: 504-514.

[12]. Pham B Q, Alkan M, Gordon M S. Porting fragmentation methods to graphical processing units using an OpenMP application programming interface: Offloading the Fock build for low angular momentum functions[J]. Journal of Chemical Theory and Computation, 2023, 19(8): 2213-2221.

[13]. Silva H U, Lucca N, Schepke C, et al. Parallel OpenMP and OpenACC porous media simulation[J]. The Journal of Supercomputing, 2023, 79(8): 8425-8446.

[14]. Marques S M V N, Serpa M S, Muñoz A N, et al. Optimizing the edp of openmp applications via concurrency throttling and frequency boosting[J]. Journal of Systems Architecture, 2022, 123: 102379.

[15]. Da Silva H U, Schepke C, Lucca N, et al. Parallel openmp and openacc mixing layer simulation[C]//2022 30th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP). IEEE, 2022: 181-188.

Cite this article

Chen,S. (2024). Investigating the applications and analysis of physics engine technologies. Applied and Computational Engineering,40,224-233.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-305-0(Print) / 978-1-83558-306-7(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.40
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Vasheghani Farahani M, Foroughi S, Norouzi S, et al. Mechanistic study of fines migration in porous media using lattice Boltzmann method coupled with rigid body physics engine[J]. Journal of Energy Resources Technology, 2019, 141(12): 123001.

[2]. Freeman C D, Frey E, Raichuk A, et al. Brax--A Differentiable Physics Engine for Large Scale Rigid Body Simulation[J]. arXiv preprint arXiv:2106.13281, 2021.

[3]. Millington I. Game physics engine development[M]. CRC Press, 2007.

[4]. Vasheghani Farahani M, Foroughi S, Norouzi S, et al. Mechanistic study of fines migration in porous media using lattice Boltzmann method coupled with rigid body physics engine[J]. Journal of Energy Resources Technology, 2019, 141(12): 123001.

[5]. Quinn M J. Parallel programming[J]. TMH CSE, 2003, 526: 105.

[6]. Culler D E, Dusseau A, Goldstein S C, et al. Parallel programming in Split-C[C]//Supercomputing'93: Proceedings of the 1993 ACM/IEEE conference on Supercomputing. IEEE, 1993: 262-273.

[7]. Gilles K. The semantics of a simple language for parallel programming[J]. Information processing, 1974, 74(471-475): 15-28.

[8]. Chandra R. Parallel programming in OpenMP[M]. Morgan kaufmann, 2001.

[9]. Huber J, Cornelius M, Georgakoudis G, et al. Efficient execution of OpenMP on GPUs[C]//2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2022: 41-52.

[10]. Yviquel H, Pereira M, Francesquini E, et al. The OpenMP Cluster Programming Model[C]//Workshop Proceedings of the 51st International Conference on Parallel Processing. 2022: 1-11.

[11]. Doerfert J, Patel A, Huber J, et al. Co-Designing an OpenMP GPU runtime and optimizations for near-zero overhead execution[C]//2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2022: 504-514.

[12]. Pham B Q, Alkan M, Gordon M S. Porting fragmentation methods to graphical processing units using an OpenMP application programming interface: Offloading the Fock build for low angular momentum functions[J]. Journal of Chemical Theory and Computation, 2023, 19(8): 2213-2221.

[13]. Silva H U, Lucca N, Schepke C, et al. Parallel OpenMP and OpenACC porous media simulation[J]. The Journal of Supercomputing, 2023, 79(8): 8425-8446.

[14]. Marques S M V N, Serpa M S, Muñoz A N, et al. Optimizing the edp of openmp applications via concurrency throttling and frequency boosting[J]. Journal of Systems Architecture, 2022, 123: 102379.

[15]. Da Silva H U, Schepke C, Lucca N, et al. Parallel openmp and openacc mixing layer simulation[C]//2022 30th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP). IEEE, 2022: 181-188.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.