加速：学术、科学和工业研究 DDN 存储助力 Pawsey 超级计算中心加速揭示宇宙秘密的科学发现
公司规模
1,000+
地区
- Pacific
国家
- Australia
产品
- DDN’s GRIDScaler®
- IBM Spectrum Scale
- DDN GS12K storage appliances
技术栈
- IBM Spectrum Scale
- DDN GS12K
实施规模
- Enterprise-wide Deployment
影响指标
- Innovation Output
- Productivity Improvements
技术
- 应用基础设施与中间件 - 数据交换与集成
- 基础设施即服务 (IaaS) - 云存储服务
适用行业
- 航天
- 教育
适用功能
- 商业运营
- 产品研发
用例
- 边缘计算与边缘智能
- 预测性维护
- 实时定位系统 (RTLS)
服务
- 云规划/设计/实施服务
- 数据科学服务
关于客户
位于西澳大利亚珀斯的 Pawsey 超级计算中心是澳大利亚两个一级高性能计算 (HPC) 中心之一，也是南半球最强大的设施之一。Pawsey 支持射电天文学、能源资源和工程领域的科学突破。在任何特定时刻，都有超过一千名科学家依靠 Pawsey 最先进的设施进行数据密集型研究、复杂模拟和高级可视化。该中心还在开创性的平方公里阵列 (SKA) 项目中发挥着关键作用，该项目专注于建造下一代射电望远镜，它将比当今最先进的望远镜更灵敏、更强大，以令人难以置信的深度和速度勘测宇宙。据 Pawsey 超级计算中心执行主任 Neil Stringfellow 介绍，SKA 项目将从数千个连接在一起的天线生成大量数据，让天文学家对宇宙的形成有前所未有的洞察力。
挑战
位于西澳大利亚珀斯的 Pawsey 超级计算中心是南半球最强大的设施之一。它支持射电天文学、能源资源和工程学领域的科学突破。在任何给定时刻，都有超过一千名科学家依靠 Pawsey 最先进的设施进行数据密集型研究、复杂模拟和高级可视化。该中心还在开创性的平方公里阵列 (SKA) 项目中发挥着关键作用，该项目专注于建造下一代射电望远镜，它将比当今最先进的望远镜更灵敏、更强大，以令人难以置信的深度和速度勘测宇宙。SKA 项目将从数千个连接的天线生成大量数据，为天文学家提供前所未有的宇宙形成见解。为了支持这项开创性的研究，Pawsey 必须为世界各地的科学家提供轻松访问高端计算平台和弹性、可扩展存储的权限。
解决方案
Pawsey 超级计算中心寻求可靠的高性能、高密度磁盘基础设施，以便能够根据需要灵活地扩展和扩展存储。能够无缝插入其他现有磁带和云存储以及后端系统非常重要，能够满足中心的各种研究需求也很重要。在权衡了所有选择标准后，Pawsey 部署了两台 DDN GS12K 存储设备，存储容量为 5 PB，支持一系列改变游戏规则的科学发现，包括沙漠火球网络 (DFN) 项目。作为首批利用新 DDN 存储的项目之一，DFN 使用摄像机跟踪火球在澳大利亚沙漠夜空中的飞行，帮助发现和回收新坠落的陨石。摄取后，该图像网络将驻留在 DDN GS12K 上。研究人员使用元数据对事件进行三角测量，以促进快速回收陨石。
运营影响
数量效益
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
相关案例.
Case Study
Airbus Soars with Wearable Technology
Building an Airbus aircraft involves complex manufacturing processes consisting of thousands of moving parts. Speed and accuracy are critical to business and competitive advantage. Improvements in both would have high impact on Airbus’ bottom line. Airbus wanted to help operators reduce the complexity of assembling cabin seats and decrease the time required to complete this task.
Case Study
Aircraft Predictive Maintenance and Workflow Optimization
First, aircraft manufacturer have trouble monitoring the health of aircraft systems with health prognostics and deliver predictive maintenance insights. Second, aircraft manufacturer wants a solution that can provide an in-context advisory and align job assignments to match technician experience and expertise.
Case Study
Aerospace & Defense Case Study Airbus
For the development of its new wide-body aircraft, Airbus needed to ensure quality and consistency across all internal and external stakeholders. Airbus had many challenges including a very aggressive development schedule and the need to ramp up production quickly to satisfy their delivery commitments. The lack of communication extended design time and introduced errors that drove up costs.
Case Study
Developing Smart Tools for the Airbus Factory
Manufacturing and assembly of aircraft, which involves tens of thousands of steps that must be followed by the operators, and a single mistake in the process could cost hundreds of thousands of dollars to fix, makes the room for error very small.
Case Study
Accelerate Production for Spirit AeroSystems
The manufacture and assembly of massive fuselage assemblies and other large structures generates a river of data. In fact, the bill of materials for a single fuselage alone can be millions of rows of data. In-house production processes and testing, as well as other manufacturers and customers created data flows that overwhelmed previous processes and information systems. Spirit’s customer base had grown substantially since their 2005 divestiture from Boeing, resulting in a $41 billion backlog of orders to fill. To address this backlog, meet increased customer demands and minimize additional capital investment, the company needed a way to improve throughput in the existing operational footprint. Spirit had a requirement from customers to increase fuselage production by 30%. To accomplish this goal, Spirit needed real-time information on its value chain and workflow. However, the two terabytes of data being pulled from their SAP ECC was unmanageable and overloaded their business warehouse. It had become time-consuming and difficult to pull aggregate data, disaggregate it for the needed information and then reassemble to create a report. During the 6-8 hours it took to build a report, another work shift (they run three per day) would have already taken place, thus the report content was out-of-date before it was ever delivered. As a result, supervisors often had to rely on manual efforts to provide charts, reports and analysis.
