RI: Mastodon: A Large-Memory, High-Throughput Simulation Infrastructure
RI:Mastodon:大内存、高吞吐量的模拟基础设施
基本信息
- 批准号:0303609
- 负责人:
- 金额:$ 144.53万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2003
- 资助国家:美国
- 起止时间:2003-09-01 至 2008-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Digital simulation is becoming dominant in not only many fields of physical sciences, but computer science as well. Despite the great improvements in processing speed over the past decades, advanced research on large-scale systems requires simulation engines that are more powerful than the average desktop workstation.The Department of Computer Sciences at the University of Texas at Austin will assemble an infrastructure for performing large-scale, memory-intensive simulations. Called Mastodon, this infrastructure will consist of a large number of rack-mounted Linux/x86 servers, each with multiple processors and several gigabytes of physical memory. Leveraged by industrial and university matching funds, we anticipate scaling Mastodon to 418 processors and nearly a terabyte of DRAM. The cluster will utilize both distributed batch scheduling using the Condor job management software, as well as soft partitioning for timing and parallel experiments.This infrastructure will service the needs of a majority of our department's faculty. The projects utilizing Mastodon include the areas of systems (specifically, architecture, compilers, and run-time systems), network algorithms, computational biology, multi-agent robotics, and formal verification. In each of these areas, Mastodon makes multiple projects feasible, whereas with conventional computing infrastructure, most of these projects cannot even be attempted.Broader Impact: This unparalleled simulation resource will be made available to a broader community as well, including students attending summer camps to increase female enrollment in computer science, undergraduates for both educational class project and research use, and researchers in regional departments with insufficient resources to perform high-end simulations.
数字仿真不仅在物理科学的许多领域占据主导地位,而且在计算机科学领域也是如此。尽管在过去的几十年里,处理速度有了很大的提高,但对大规模系统的高级研究需要比普通桌面工作站更强大的仿真引擎。德克萨斯大学奥斯汀分校的计算机科学系将组装一个基础设施,用于执行大规模、内存密集型的模拟。这个基础架构被称为Mastodon,它将由大量安装在机架上的Linux/x86服务器组成,每个服务器都有多个处理器和几gb的物理内存。在工业和大学配套资金的推动下,我们预计将Mastodon扩展到418个处理器和近1tb的DRAM。集群将利用Condor作业管理软件的分布式批调度,以及用于定时和并行实验的软分区。这个基础设施将满足我们系大多数教员的需要。利用Mastodon的项目包括系统领域(特别是体系结构、编译器和运行时系统)、网络算法、计算生物学、多智能体机器人和形式化验证。在这些领域中,Mastodon使多个项目变得可行,而在传统的计算基础设施中,大多数项目甚至无法尝试。更广泛的影响:这种无与伦比的模拟资源也将提供给更广泛的社区,包括参加夏令营的学生,以增加计算机科学的女性入学率,用于教育课程项目和研究用途的本科生,以及资源不足的地区部门的研究人员进行高端模拟。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Risto Miikkulainen其他文献
Holdout Evaluation
坚持评估
- DOI:
10.1007/978-0-387-30164-8_369 - 发表时间:
2010 - 期刊:
- 影响因子:0
- 作者:
Antal van den Bosch;B. Hengst;J. Lloyd;Risto Miikkulainen;Hendrik Blockeel - 通讯作者:
Hendrik Blockeel
MARLEDA: Effective distribution estimation through Markov random fields
- DOI:
10.1016/j.tcs.2015.07.049 - 发表时间:
2016-06-20 - 期刊:
- 影响因子:
- 作者:
Matthew Alden;Risto Miikkulainen - 通讯作者:
Risto Miikkulainen
Evolutionary Supervised Machine Learning
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Risto Miikkulainen - 通讯作者:
Risto Miikkulainen
Extracting the dynamics of the Hodgkin-Huxley model using recurrent neural networks
- DOI:
10.1186/1471-2202-8-s2-p100 - 发表时间:
2007-07-06 - 期刊:
- 影响因子:2.300
- 作者:
Sari Andoni;Manish Saggar;Tekin Meriçli;Risto Miikkulainen - 通讯作者:
Risto Miikkulainen
Modeling self-organizing tri-chromatic color selective regions in primary visual cortex
- DOI:
10.1186/1471-2202-8-s2-s24 - 发表时间:
2007-07-06 - 期刊:
- 影响因子:2.300
- 作者:
Judah De Paula;Jim Bednar;Risto Miikkulainen - 通讯作者:
Risto Miikkulainen
Risto Miikkulainen的其他文献
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{{ truncateString('Risto Miikkulainen', 18)}}的其他基金
Collaborative Research: MOD and TLS: A Predictive Simulation Model of Competitive Dynamics in Innovation
合作研究:MOD 和 TLS:创新竞争动态的预测模拟模型
- 批准号:
0914796 - 财政年份:2009
- 资助金额:
$ 144.53万 - 项目类别:
Standard Grant
RI: Small: Learning Strategic Behavior in Sequential Decision Tasks
RI:小:学习顺序决策任务中的策略行为
- 批准号:
0915038 - 财政年份:2009
- 资助金额:
$ 144.53万 - 项目类别:
Standard Grant
Pilot: Leveraging Human Creativity with Machine Discovery
试点:通过机器发现利用人类创造力
- 批准号:
0757479 - 财政年份:2008
- 资助金额:
$ 144.53万 - 项目类别:
Standard Grant
Cooperative Coevolution of Neural Networks in Sequential Decision Tasks
顺序决策任务中神经网络的协同协同进化
- 批准号:
0083776 - 财政年份:2000
- 资助金额:
$ 144.53万 - 项目类别:
Continuing Grant
Modeling Development and Perceptual Phenomena in the Visual Cortex
视觉皮层的建模发展和感知现象
- 批准号:
9811478 - 财政年份:1998
- 资助金额:
$ 144.53万 - 项目类别:
Standard Grant
Symbiotic Evolution of Neural Networks in Sequential Decision Tasks
神经网络在顺序决策任务中的共生进化
- 批准号:
9504317 - 财政年份:1995
- 资助金额:
$ 144.53万 - 项目类别:
Continuing Grant
RIA: A Self-Organizing Neural Network Model of The PrimaryVisual cortex
RIA:初级视觉皮层的自组织神经网络模型
- 批准号:
9309273 - 财政年份:1993
- 资助金额:
$ 144.53万 - 项目类别:
Standard Grant
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