CCRI: ENS: Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI)

机器学习是一个迅速发展的领域。计算密集型工作流训练神经网络，然后将结果用于智能手机、机器人、无人机、自动驾驶车辆，以及运行物联网。对图形处理单元(GPU)的访问是通过Chase-CI的Nautilus提供的，这是一个高度分布式但集中管理的按需计算机集群，专为ML和计算媒体(CM)设计。Chase-CI为20多个园区提供了增加本地计算周期和快速数据处理的平台，并提供以研究人员为重点的支持和培训。使用Chase-CI的详细性能测量，研究人员学习成为优化他们的计算资源的专家。CHASE-CI是一个社区建设努力，使用专门建造的不断增强的全国分布式计算和数据存储基础设施来支持不断增长的ML/CM研究人员社区。研究人员探索在图形性能指标的帮助下优化的算法和体系结构的组合。研究人员受益于广泛的共享工作流程和开源软件。他们使用大通CI的在线社交媒体平台接受和给予支持，并分享技术。社区使用Chase-CI向计算机体系结构提供有关未来国家网络基础设施研究和教学实验室需求的讨论。Chase-CI形成了一个易于加入的全国性在线社区，旨在共享代码、数据和结果。Chase-CI开发的硬件、软件和社会技术方法为ML/CM技术的更广泛研究应用和学生培训提供了路线图。研究人员可以更广泛地接触数百个GPU，用于开发算法和软件，以培训传感设备并将结果可视化，从而吸引即将加入关键劳动力大军的学生，这些劳动力正在进行大规模的移动平台扩张，如机器人、无人机和自动驾驶汽车。Chase-CI影响了计算机科学的社会多样性，扩大了少数群体服务机构和服务不足的州之间的参与。Chase-CI全面测量和监控应用程序通过区域和国家R&P；E网络进行的数据访问。可在prp.ucsd.edu上找到该项目的储存库，该库至少要在整个项目期间进行维护。它是包含指向建立在太平洋研究平台上的所有研究努力的指针的锚定网站。它包含代码库、演示文稿、出版物、演示文稿和录音讲座等参考资料，并维护和归档活跃的社交媒体渠道。Chase-CI由加州大学圣地亚哥分校领导，与加州大学圣克鲁斯分校、内布拉斯加-林肯大学、佛罗里达农业与机械大学、纽约大学、伊利诺伊大学芝加哥分校和圣地亚哥州立大学的调查人员合作。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Machine learning (ML) is a rapidly expanding field. Computationally intensive workflows train neural nets and then use the results in smartphones, robots, drones, self-driving vehicles, and to run the Internet of Things. Access to graphics processing units (GPUs) is provided through CHASE-CI’s Nautilus, a highly distributed but centrally managed on-demand computer cluster designed for ML and Computational Media (CM). CHASE-CI provides over 20 campuses the scaffold for adding on-premises compute cycles and fast data handling and it offers researcher-focused support and training. Using CHASE-CI’s detailed measurements of performance, researchers learn to become experts in optimization of their computational resources.CHASE-CI is a community-building effort that sustains a growing community of ML/CM researchers using a purpose-built continuously enhanced nationally distributed computing and data storage infrastructure. Researchers explore combinations of algorithms and architectures optimized with the help of graphed performance metrics. Researchers benefit from extensive shared workflows and open-source software. They use CHASE-CI’s on-line social media platform to receive and give support and share techniques. Community use of CHASE-CI informs computer architecture discussions about future national cyberinfrastructure research and instructional lab needs. CHASE-CI forms a national on-line community that is easy to join, designed for sharing code, data, and results.The hardware, software, and socio-technical approaches developed by CHASE-CI have provided a roadmap for broader research uses and student training in ML/CM technologies. Researchers get expanded access to hundreds of GPUs for developing algorithms and software to train sensing devices and visualize results thus engaging the students who will soon join the essential workforce for the ongoing massive expansion of mobile platforms such as robots, drones, and self-driving cars. CHASE-CI impacts social diversity in computer science, broadening participation among Minority-Serving Institutions and underserved states. CHASE-CI thoroughly measures and monitors data access by applications over the regional and national R&E networks. The repository for the project may be found at prp.ucsd.edu, to be maintained for the length of the project at a minimum. It is the anchor website containing pointers to all the research efforts that build upon the Pacific Research Platform. It contains code repositories, presentations, references like publications, presentations, and recorded lectures, and it maintains and archives an active social media channel. CHASE-CI is led by UC San Diego, partnering with investigators at UC Santa Cruz, The University of Nebraska-Lincoln, Florida Agricultural and Mechanical University, New York University, The University of Illinois at Chicago, and San Diego State University.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.