BD Spokes: SPOKE: MIDWEST: Collaborative:Advanced Computational Neuroscience Network (ACNN)

BD 发言：发言：中西部：协作：高级计算神经科学网络 (ACNN)

• 批准号: 1636846
• 负责人: Dhabaleswar Panda
• 金额: $ 16.65万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1636846&HistoricalAwards=false
• 关键词: BD; Spokes; SPOKE; MIDWEST; Collaborative

Novel neuroscience tools and techniques are necessary to enable insight into the building blocks of neural circuits, the interactions between these circuits that underpin the functions of the human brain, and modulation of these circuits that affect our behavior. To leverage rapid technological development in sensing, imaging, and data analysis new ground breaking advances in neuroscience are necessary to facilitate knowledge discovery using data science methods. To address this societal grand challenge, the project will foster new interdisciplinary collaborations across computing, biological, mathematical, and behavioral science disciplines together with partnerships in academia, industry, and government at multiple levels. The Big Data Neuroscience Spoke titled Midwest: Advanced Computational Neuroscience Network (ACNN) is strongly aligned with the national priority area of neuroscience and brings together a diverse set of committed regional partners to enable the Midwest region to realize the promise of Big Data for neuroscience. The ACNN Spoke will build broad consensus on the core requirements, infrastructure, and components needed to develop a new generation of sustainable interdisciplinary Neuroscience Big Data research. ACNN will leverage the strengths and resources in the Midwest region to increase innovation and collaboration for the understanding of the structure, physiology, and function of the human brain through partnerships and services in education, tools, and best practices. The ACNN will design, pilot and support powerful neuroscientific computational resources for high-throughput, collaborative, and service-oriented data aggregation, processing and open-reproducible science. The ACNN Spoke framework will address three specific problems related to neuroscience Big Data: (1) data capture, organization, and management involving multiple centers and research groups, (2) quality assurance, preprocessing and analysis that incorporates contextual metadata, and (3) data communication to software and hardware computational resources that can scale with the volume, velocity, and variety of neuroscience datasets. The ACNN will build a sustainable ecosystem of neuroscience community partners in both academia and industry using existing technologies for collaboration and virtual meeting together with face-to-face group meetings. The planned activities of the ACNN Spoke will also allow the Midwest Big Data Hub to disseminate additional Big Data technologies resources to the neuroscience community, including access to supercomputing facilities, best practices, and platforms.This award received co-funding from CISE Divisions of Advanced Cyberinfrastructure (ACI) and Information and Intelligent Systems (IIS).

新的神经科学工具和技术是必要的，以使人们能够深入了解神经电路的组成部分，这些电路之间支撑人类大脑功能的相互作用，以及影响我们行为的这些电路的调制。为了利用传感、成像和数据分析方面的快速技术发展，神经科学方面的新的突破性进展是必要的，以促进使用数据科学方法的知识发现。为了应对这一社会重大挑战，该项目将促进跨计算、生物、数学和行为科学学科的新的跨学科合作，以及学术界、行业和政府在多个层面的合作伙伴关系。题为中西部：高级计算神经科学网络(ACNN)的大数据神经科学演讲与神经科学的国家优先领域紧密结合，并聚集了一系列不同的坚定的地区合作伙伴，使中西部地区能够实现大数据对神经科学的承诺。ACNN的讲话将就发展新一代可持续跨学科神经科学大数据研究所需的核心要求、基础设施和组件建立广泛的共识。美国有线电视新闻网将利用中西部地区的优势和资源，通过在教育、工具和最佳实践方面的伙伴关系和服务，加强创新和合作，以了解人脑的结构、生理和功能。ACNN将设计、试点和支持强大的神经科学计算资源，以实现高通量、协作性和面向服务的数据聚合、处理和可开放复制的科学。ACNN SPEED框架将解决与神经科学大数据相关的三个具体问题：(1)涉及多个中心和研究小组的数据捕获、组织和管理；(2)结合上下文元数据的质量保证、预处理和分析；以及(3)与软件和硬件计算资源的数据通信，这些资源可以随着神经科学数据集的数量、速度和种类而扩展。ACNN将利用现有的协作和虚拟会议技术以及面对面的小组会议，建立一个可持续的生态系统，由学术界和工业界的神经科学社区合作伙伴组成。ACNN发言计划的活动还将允许中西部大数据中心向神经科学界传播更多的大数据技术资源，包括访问超级计算设施、最佳实践和平台。该奖项由高级网络基础设施(ACI)和信息与智能系统(IIS)的CEISE部门联合资助。

项目成果

Analyzing, Modeling, and Provisioning QoS for NVMe SSDs

• DOI: 10.1109/ucc.2018.00033
• 发表时间: 2018-12
• 期刊: 2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC)
• 作者: Shashank Gugnani;Xiaoyi Lu;D. Panda

Accelerating TensorFlow with Adaptive RDMA-Based gRPC

• DOI: 10.1109/hipc.2018.00010
• 发表时间: 2018-12
• 期刊: 2018 IEEE 25th International Conference on High Performance Computing (HiPC)
• 作者: Rajarshi Biswas;Xiaoyi Lu;D. Panda

Spark-uDAPL: Cost-Saving Big Data Analytics on Microsoft Azure Cloud with RDMA Networks

Spark-uDAPL：使用 RDMA 网络在 Microsoft Azure 云上进行节省成本的大数据分析

• DOI: 10.1109/bigdata.2018.8622615
• 发表时间: 2018
• 期刊: Proceedings of 2018 IEEE International Conference on Big Data (Big Data
• 作者: Lu, Xiaoyi;Shankar, Dipti;Shi, Haiyang;Panda, Dhabaleswar K.
• 通讯作者: Panda, Dhabaleswar K.

C-GDR: High-Performance Container-Aware GPUDirect MPI Communication Schemes on RDMA Networks

• DOI: 10.1109/ipdps.2019.00034
• 发表时间: 2019-05
• 期刊: 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
• 作者: Jie Zhang;Xiaoyi Lu;Ching-Hsiang Chu;D. Panda

EC-Bench: Benchmarking Onload and Offload Erasure Coders on Modern Hardware Architectures

• DOI: 10.1007/978-3-030-32813-9_18
• 发表时间: 2018-12
• 作者: Haiyang Shi;Xiaoyi Lu;D. Panda