SIGSIM Principles of Advanced Discrete Simulation最新文献

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Accelerating parallel agent-based epidemiological simulations 加速基于智能体的并行流行病学模拟
SIGSIM Principles of Advanced Discrete Simulation Pub Date : 2014-05-18 DOI: 10.1145/2601381.2601387
D. Rao
{"title":"Accelerating parallel agent-based epidemiological simulations","authors":"D. Rao","doi":"10.1145/2601381.2601387","DOIUrl":"https://doi.org/10.1145/2601381.2601387","url":null,"abstract":"Background: Simulations play a central role in epidemiological analysis and design of prophylactic measures. Spatially explicit, agent-based models provide temporo-geospatial information that cannot be obtained from traditional equation-based and individual-based epidemic models. Since, simulation of large agent-based models is time consuming, optimistically synchronized parallel simulation holds considerable promise to significantly decrease simulation execution times.\u0000 Problem: Realizing efficient and scalable optimistic parallel simulations on modern distributed memory supercomputers is a challenge due to the spatially-explicit nature of agent-based models. Specifically, conceptual movement of agents results in large number of inter-process messages which significantly increase synchronization overheads and degrades overall performance.\u0000 Proposed solution: To reduce inter-process messages, this paper proposes and experimentally evaluates two approaches involving single and multiple active-proxy agents. The Single Active Proxy (SAP) approach essentially accomplishes logical process migration (without any support from underlying simulation kernel) reflecting conceptual movement of the agents. The Multiple Active Proxy (MAP) approach improves upon SAP by utilizing multiple agents at boundaries between processes to further reduce inter-process messages thereby improving scalability and performance. The experiments conducted using a range of models indicate that SAP provides 200% improvement over the base case and MAP provides 15% to 25% improvement over SAP depending on the model.","PeriodicalId":255272,"journal":{"name":"SIGSIM Principles of Advanced Discrete Simulation","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124183278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 15
Mesoscopic traffic simulation on CPU/GPU 基于CPU/GPU的介观交通模拟
SIGSIM Principles of Advanced Discrete Simulation Pub Date : 2014-05-18 DOI: 10.1145/2601381.2601396
Yan Xu, Gary S. H. Tan, Xiaosong Li, Xiao Song
{"title":"Mesoscopic traffic simulation on CPU/GPU","authors":"Yan Xu, Gary S. H. Tan, Xiaosong Li, Xiao Song","doi":"10.1145/2601381.2601396","DOIUrl":"https://doi.org/10.1145/2601381.2601396","url":null,"abstract":"Mesoscopic traffic simulation is an important branch of technology to support offline large-scale simulation-based traffic planning and online simulation-based traffic management. One of the major concerns using mesoscopic traffic simulations is the performance, which means the required time to simulate a traffic scenario. At the same time, the GPU has recently been a success, because of its massive performance compared to the CPU. Thus, a critical question is \"whether the GPU can be a potential high-performance platform for mesoscopic traffic simulations\"? To the best of our knowledge, there is no clear answer in the research area. In this paper, we firstly propose a comprehensive framework to run a traditional time-stepped mesoscopic traffic simulation on CPU/GPU. Then, we design a boundary processing method to guarantee the correctness of running mesoscopic supply traffic simulations on the GPU. Thirdly, the proposed mesoscopic traffic simulation framework is demonstrated to simulate 100,000 vehicles moving on a large-scale grid road network. In this case study, running a mesoscopic supply traffic simulation on a GPU (GeForce GT 650M) gives 11.2 times speedup, compared with running the same supply simulation on a CPU core (Intel E5-2620). In the end, this paper explains the theoretical limitation of running mesoscopic supply traffic simulations on the GPU. In conclusion, regardless of high system complexity, the proposed mesoscopic traffic simulation framework on CPU/GPU provides an innovative and promising solution for high-performance mesoscopic traffic simulations.","PeriodicalId":255272,"journal":{"name":"SIGSIM Principles of Advanced Discrete Simulation","volume":"57 Pt 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126231839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 21
An IoT-oriented Multiple Data Replicas Placement Strategy in Hybrid Fog-Cloud Environment 雾云混合环境下面向物联网的多数据副本放置策略
SIGSIM Principles of Advanced Discrete Simulation Pub Date : 1900-01-01 DOI: 10.1145/3437959.3459251
N. Salah, Narjès Bellamine Ben Saoud
{"title":"An IoT-oriented Multiple Data Replicas Placement Strategy in Hybrid Fog-Cloud Environment","authors":"N. Salah, Narjès Bellamine Ben Saoud","doi":"10.1145/3437959.3459251","DOIUrl":"https://doi.org/10.1145/3437959.3459251","url":null,"abstract":"The growing adoption of Fog computing for the sensitive-time IoT applications allows to facilitate the real-time actions and to enhance their efficiency and performance. In fact, keeping the data in the distributed Fog network brings the advantages and power of the Cloud closer to where data are generated while saving network bandwidth and reducing latency and operational costs. However, due to the diversity of the Fog nodes, IoT system distribution and data sharing, how and where to place the produced data with low latency is a main challenge. Moreover, a data placement based on a single replica cannot meet the data access requirements of all data consumers that have different topology positions. Thus, in this paper, we propose a multi-objective optimization data placement model in a hybrid Fog-Cloud environment based on multiple data replicas. It aims to find better distributed data storage while optimizing the overall system latency and the used storage space by minimizing the data replicas and following full and partial data replication methods. Further, we propose a greedy algorithm $iFogDP_h$ which uses a refined method to find a solution for assigning the IoT data to the appropriate data hosts in polynomial time by reducing the time required to transfer data for storage, access and replication. We conducted the experiments on iFogSim, a toolkit for modeling and simulation of Fog environments. The experimental results show the effectiveness of our proposed solution in terms of latency, storage overhead and the number of data replicas compared to the existing strategies.","PeriodicalId":255272,"journal":{"name":"SIGSIM Principles of Advanced Discrete Simulation","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125456318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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