ACM Transactions on Modeling and Computer SimulationACM建模与计算机仿真汇刊
ACM Transactions on Modeling and Computer Simulation(英文缩写 ACM T MODEL COMPUT S),ISSN 1049-3301,eISSN 1558-1195,中文译名:ACM建模与计算机仿真汇刊 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-10-01 至 2026-09-30，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 0.851 | Q4 |
| 2022 | 0.900 | Q3 |
| 2023 | 0.700 | Q3 |
| 2024 | 1.900 | Q1 |
| 2025 | 1.400 | Q2 |
ACM Transactions on Modeling and Computer Simulation 最新收录文献
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1. Indemics: An Interactive High-Performance Computing Framework for Data Intensive Epidemic Modeling.
PMID:期刊:DOI:日期:2014-01We describe the design and prototype implementation of Indemics (Interactive EpidemicSimulation)-a modeling environment utilizing high-performance computing technologies for supporting complex epidemic simulations. Indemics can support policy analysts and epidemiologists interested in planning and control of pandemics. Indemics goes beyond traditional epidemic simulations by providing a simple and powerful way to represent and analyze policy-based as well as individual-based . Users can also stop the simulation at any point, assess the state of the simulated system, and add additional interventions. Indemics is available to end-users via a web-based interface. Detailed performance analysis shows that Indemics greatly enhances the capability and productivity of simulating complex intervention strategies with a marginal decrease in performance. We also demonstrate how Indemics was applied in some real case studies where complex interventions were implemented.
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2. Massive parallelization of serial inference algorithms for a complex generalized linear model.
PMID:期刊:日期:2013-01Following a series of high-profile drug safety disasters in recent years, many countries are redoubling their efforts to ensure the safety of licensed medical products. Large-scale observational databases such as claims databases or electronic health record systems are attracting particular attention in this regard, but present significant methodological and computational concerns. In this paper we show how high-performance statistical computation, including graphics processing units, relatively inexpensive highly parallel computing devices, can enable complex methods in large databases. We focus on optimization and massive parallelization of cyclic coordinate descent approaches to fit a conditioned generalized linear model involving tens of millions of observations and thousands of predictors in a Bayesian context. We find orders-of-magnitude improvement in overall run-time. Coordinate descent approaches are ubiquitous in high-dimensional statistics and the algorithms we propose open up exciting new methodological possibilities with the potential to significantly improve drug safety.
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3. A Distributed Platform for Global-Scale Agent-Based Models of Disease Transmission.
PMID:期刊:日期:2011-12The Global-Scale Agent Model (GSAM) is presented. The GSAM is a high-performance distributed platform for agent-based epidemic modeling capable of simulating a disease outbreak in a population of several billion agents. It is unprecedented in its scale, its speed, and its use of Java. Solutions to multiple challenges inherent in distributing massive agent-based models are presented. Communication, synchronization, and memory usage are among the topics covered in detail. The memory usage discussion is Java specific. However, the communication and synchronization discussions apply broadly. We provide benchmarks illustrating the GSAM's speed and scalability.