TY - GEN
T1 - Reproducible large-scale social simulations on various computing environment
AU - Harada, Takuya
AU - Muarata, Tadahiko
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Number 26380277.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/30
Y1 - 2017/8/30
N2 - In this paper, we propose parallel computing techniques for reproducible large-scale social simulations on various computing environments including CPU (Central Processing Unit) or GPU (Graphic Processing Unit). When we use computing resources for large-scale social simulations, the reproducibility of a simulation should be considered. 'Reproducibility' means the same trial of a simulation can be repeated. If the same computing resources are available to repeat the trial, it is easy to reproduce the same simulation results. When not all the same computing resources are available, however, it becomes difficult to obtain the same trial since random number generators may become different from the original computation resources. In this study, we employ multi-thread computing on CPU or GPU. We propose two models to run reproducible social simulations on CPU or GPU. One is to parallelize trials (Trial Parallelization). The other is to parallelize agents of a single simulation (Agent Parallelization). These models can be ensured reproducibility even in different computing resources. Our experimental results show that the same computing processes are obtained on CPU or GPU. When we parallelize large-scale social simulation on CPU or GPU, we can accelerate the simulation as a secondary effect.
AB - In this paper, we propose parallel computing techniques for reproducible large-scale social simulations on various computing environments including CPU (Central Processing Unit) or GPU (Graphic Processing Unit). When we use computing resources for large-scale social simulations, the reproducibility of a simulation should be considered. 'Reproducibility' means the same trial of a simulation can be repeated. If the same computing resources are available to repeat the trial, it is easy to reproduce the same simulation results. When not all the same computing resources are available, however, it becomes difficult to obtain the same trial since random number generators may become different from the original computation resources. In this study, we employ multi-thread computing on CPU or GPU. We propose two models to run reproducible social simulations on CPU or GPU. One is to parallelize trials (Trial Parallelization). The other is to parallelize agents of a single simulation (Agent Parallelization). These models can be ensured reproducibility even in different computing resources. Our experimental results show that the same computing processes are obtained on CPU or GPU. When we parallelize large-scale social simulation on CPU or GPU, we can accelerate the simulation as a secondary effect.
KW - large-scale social simulation
KW - parallel computation
KW - randam number generator
UR - http://www.scopus.com/inward/record.url?scp=85030856034&partnerID=8YFLogxK
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U2 - 10.1109/IFSA-SCIS.2017.8023303
DO - 10.1109/IFSA-SCIS.2017.8023303
M3 - Conference contribution
AN - SCOPUS:85030856034
T3 - IFSA-SCIS 2017 - Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems
BT - IFSA-SCIS 2017 - Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th Joint World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems, IFSA-SCIS 2017
Y2 - 27 June 2017 through 30 June 2017
ER -