Bilateral BBSRC-NSF/BIO: Collaborative Research: ABI Development: Seamless Integration of Neuroscience Models and Tools with HPC - Easy Path to Supercomputing for Neuroscience
双边 BBSRC-NSF/BIO:合作研究:ABI 开发:神经科学模型和工具与 HPC 的无缝集成 - 神经科学超级计算的简单途径
基本信息
- 批准号:1458840
- 负责人:
- 金额:$ 77.4万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2015
- 资助国家:美国
- 起止时间:2015-08-01 至 2020-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This project is a collaboration between the University of California San Diego and Yale University to develop a science gateway for the computational neuroscience community. A gateway such as this helps improve our understanding of how the brain works by making it easier for neuroscientists to use complex digital models of brain cells and circuits in their research. Powerful software has been developed for building and using models, and on-line resources such as Open Source Brain (OSB), ModelDB, Neuroscience Information Framework (NIF), and OpenWorm have been created to help neuroscientists find existing models, collaborate in developing new ones, and share the results of their work with others. However, models are becoming too complex for the computer hardware that is available to most neuroscientists, resulting in a critical need to use high performance computing resources (HPC). This work extends an existing Neuroscience Gateway (NSG), which was developed with support from NSF to eliminate or reduce many of the technical and administrative difficulties that previously limited neuroscientists' access to HPC (http://www.nsgportal.org/). That said, NSG users must still log in, upload models, launch simulations, and download results--a process that involves many time-consuming, error-prone steps. The expanded NSG-R will eliminate these steps by enabling on-demand, automated communication between itself and familiar working environments including resources like OSB and others mentioned above, and even with neural simulation software running on neuroscientists' own laptop and desktop computers. This seamless access to HPC is implemented in NSG-R by a software infrastructure that uses REpresentational State Transfer ("REST", the R in NSG-R). NSG-R utilizes set of web services which expose the capabilities of NSG for access via publicly available application programmer interfaces. This will allow users of neuroscience resources such as OSB, ModelDB, NIF and OpenWorm to readily access HPC from their respective websites via NSG-R. This enhances the usefulness of NSG-R, other neuroscience resources like OSB, and widely used neural simulators such as NEURON, GENESIS, PyNN, NEST, Brian and MOOSE. It also results in greater research productivity and enables wider use of large scale computational modeling by scientists and students. NSG-R will accelerate progress in brain science, and have far-reaching beneficial effects on related fields such as robotics and engineering of adaptive and learning systems. It will widen opportunities for educational and career advancement in neuroscience and engineering. Furthermore, by removing barriers that traditionally have limited access to HPC, NSG-R levels the playing field for all students and researchers regardless of their institutional affiliation. NSG-R, a free and open neuroscience gateway infrastructure, will naturally be a ready entry point for students and researchers from historically underrepresented schools and colleges. NSG-R workshops will be hosted at minority serving institutions (MSI) and opportunities for students to do internships with the NSG-R team at the University of California San Diego will be provided.
该项目是加州大学圣地亚哥分校和耶鲁大学之间的合作项目,旨在为计算神经科学界开发一个科学网关。这样的网关使神经科学家能够更轻松地在研究中使用脑细胞和电路的复杂数字模型,从而有助于提高我们对大脑工作原理的理解。我们开发了用于构建和使用模型的强大软件,并创建了开源大脑 (OSB)、ModelDB、神经科学信息框架 (NIF) 和 OpenWorm 等在线资源,以帮助神经科学家找到现有模型、合作开发新模型并与他人分享他们的工作成果。然而,对于大多数神经科学家可用的计算机硬件来说,模型变得过于复杂,导致迫切需要使用高性能计算资源 (HPC)。这项工作扩展了现有的神经科学网关 (NSG),该网关是在 NSF 的支持下开发的,旨在消除或减少以前限制神经科学家访问 HPC 的许多技术和管理困难 (http://www.nsgportal.org/)。也就是说,NSG 用户仍然必须登录、上传模型、启动模拟和下载结果,这个过程涉及许多耗时且容易出错的步骤。扩展后的 NSG-R 将通过实现自身与熟悉的工作环境之间的按需、自动通信来消除这些步骤,包括 OSB 等资源和上述其他资源,甚至可以使用在神经科学家自己的笔记本电脑和台式电脑上运行的神经模拟软件。这种对 HPC 的无缝访问是在 NSG-R 中通过使用表述性状态传输(“REST”,NSG-R 中的 R)的软件基础设施实现的。 NSG-R 利用一组 Web 服务,这些服务公开 NSG 的功能,以便通过公开可用的应用程序程序员接口进行访问。这将使 OSB、ModelDB、NIF 和 OpenWorm 等神经科学资源的用户能够通过 NSG-R 从各自的网站轻松访问 HPC。这增强了 NSG-R、OSB 等其他神经科学资源以及 NEURON、GENESIS、PyNN、NEST、Brian 和 MOOSE 等广泛使用的神经模拟器的实用性。它还可以提高研究效率,并使科学家和学生更广泛地使用大规模计算模型。 NSG-R将加速脑科学的进步,并对机器人、自适应和学习系统工程等相关领域产生深远的有益影响。它将扩大神经科学和工程学领域的教育和职业发展机会。此外,通过消除传统上限制 HPC 访问的障碍,NSG-R 为所有学生和研究人员提供了公平的竞争环境,无论其所属机构如何。 NSG-R 是一个免费开放的神经科学网关基础设施,自然会成为来自历史上代表性不足的学校和学院的学生和研究人员的现成切入点。 NSG-R 研讨会将在少数族裔服务机构 (MSI) 举办,并将为学生提供在加州大学圣地亚哥分校的 NSG-R 团队实习的机会。
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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Amitava Majumdar其他文献
Avoiding burnt probe tips: Practical solutions for testing internally regulated power supplies
避免探针尖端烧毁:测试内部稳压电源的实用解决方案
- DOI:
10.1109/ets.2014.6847810 - 发表时间:
2014-05-26 - 期刊:
- 影响因子:0
- 作者:
R. Swanson;Anna Wong;S. Ethirajan;Amitava Majumdar - 通讯作者:
Amitava Majumdar
Cyberinfrastructure Usage Modalities on the TeraGrid
TeraGrid 上的网络基础设施使用方式
- DOI:
10.1109/ipdps.2011.239 - 发表时间:
2011-05-16 - 期刊:
- 影响因子:0
- 作者:
Daniel S. Katz;David L. Hart;C. Jordan;Amitava Majumdar;J. Navarro;Warren Smith;John Towns;Von Welch;Nancy Wilkins - 通讯作者:
Nancy Wilkins
GPU-based ultra-fast dose calculation using a finite size pencil beam model
使用有限尺寸笔形射束模型进行基于 GPU 的超快速剂量计算
- DOI:
- 发表时间:
2009 - 期刊:
- 影响因子:3.5
- 作者:
Xuejun Gu;Dongju Choi;C. Men;Hubert Pan;Amitava Majumdar;Steve B Jiang - 通讯作者:
Steve B Jiang
A scalable, low cost design-for-test architecture for UltraSPARC/spl trade/ chip multi-processors
适用于 UltraSPARC/spl trade/chip 多处理器的可扩展、低成本测试设计架构
- DOI:
10.1109/test.2002.1041825 - 发表时间:
2002-10-07 - 期刊:
- 影响因子:0
- 作者:
I. Parulkar;T. Ziaja;R. Pendurkar;An;L. D'Souza;Amitava Majumdar - 通讯作者:
Amitava Majumdar
Ground bounce considerations in DC parametric test generation using boundary scan
使用边界扫描生成直流参数测试时的地弹注意事项
- DOI:
10.1109/vtest.1998.670853 - 发表时间:
1998-04-26 - 期刊:
- 影响因子:0
- 作者:
Amitava Majumdar;M. Komoda;Tim Ayres - 通讯作者:
Tim Ayres
Amitava Majumdar的其他文献
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{{ truncateString('Amitava Majumdar', 18)}}的其他基金
Collaborative Research: Frameworks: hpcGPT: Enhancing Computing Center User Support with HPC-enriched Generative AI
协作研究:框架:hpcGPT:通过 HPC 丰富的生成式 AI 增强计算中心用户支持
- 批准号:
2411297 - 财政年份:2024
- 资助金额:
$ 77.4万 - 项目类别:
Standard Grant
Collaborative Research: CIBR: Building Capacity for Data-driven Neuroscience Research
合作研究:CIBR:数据驱动神经科学研究能力建设
- 批准号:
1935749 - 财政年份:2020
- 资助金额:
$ 77.4万 - 项目类别:
Standard Grant
Category II: Exploring Neural Network Processors for AI in Science and Engineering
第二类:探索科学与工程中人工智能的神经网络处理器
- 批准号:
2005369 - 财政年份:2020
- 资助金额:
$ 77.4万 - 项目类别:
Cooperative Agreement
Collaborative Research: Frameworks: Designing Next-Generation MPI Libraries for Emerging Dense GPU Systems
协作研究:框架:为新兴密集 GPU 系统设计下一代 MPI 库
- 批准号:
1931450 - 财政年份:2019
- 资助金额:
$ 77.4万 - 项目类别:
Standard Grant
Promoting International Collaboration on Developing Scalable, Portable & Efficient HPC Software for Modern HPC Platforms
促进开发可扩展、便携的国际合作
- 批准号:
1849519 - 财政年份:2018
- 资助金额:
$ 77.4万 - 项目类别:
Standard Grant
SHF: Large: Collaborative Research: Next Generation Communication Mechanisms exploiting Heterogeneity, Hierarchy and Concurrency for Emerging HPC Systems
SHF:大型:协作研究:利用新兴 HPC 系统的异构性、层次结构和并发性的下一代通信机制
- 批准号:
1565336 - 财政年份:2016
- 资助金额:
$ 77.4万 - 项目类别:
Standard Grant
BIGDATA: F: DKM: Collaborative Research: Scalable Middleware for Managing and Processing Big Data on Next Generation HPC Systems
BIGDATA:F:DKM:协作研究:用于在下一代 HPC 系统上管理和处理大数据的可扩展中间件
- 批准号:
1447861 - 财政年份:2014
- 资助金额:
$ 77.4万 - 项目类别:
Standard Grant
Collaborative Research SI2-SSE:Sustained Innovation in Acceleration of Molecular Dynamics on Future Computational Environments: Power to the People in the Cloud and on Accelerators
合作研究 SI2-SSE:未来计算环境中分子动力学加速的持续创新:为云端和加速器中的人们提供力量
- 批准号:
1148276 - 财政年份:2012
- 资助金额:
$ 77.4万 - 项目类别:
Standard Grant
SHF: Large: Collaborative Research: Unified Runtime for Supporting Hybrid Programming Models on Heterogeneous Architecture.
SHF:大型:协作研究:支持异构架构上的混合编程模型的统一运行时。
- 批准号:
1213056 - 财政年份:2012
- 资助金额:
$ 77.4万 - 项目类别:
Standard Grant
Collaborative Research: SI2-SSI: A Comprehensive Performance Tuning Framework for the MPI Stack
合作研究:SI2-SSI:MPI 堆栈的综合性能调优框架
- 批准号:
1147926 - 财政年份:2012
- 资助金额:
$ 77.4万 - 项目类别:
Standard Grant
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