数字孪生万亿像素可视化的Petascale云超级计算
Petascale Cloud Supercomputing for Terapixel Visualization of a Digital Twin
摘要：
背景资料：照片般逼真的太像素可视化是计算密集型的，迄今为止还没有这样的城市数字孪生的可视化，存在的少数太像素可视化已经面向太空而不是地球。目的：我们的目标是：为可视化创建可扩展的云超级计算机软件架构;城市物联网数据的照片级逼真的万亿像素3D可视化，支持每日更新;严格评估云超级计算的应用。方法：我们将Blender Cycles路径跟踪器迁移到一个新的软件框架内的公共云，该框架旨在扩展到petaFLOP性能。结果如下：我们演示了我们可以在一小时内计算一个万亿像素的可视化，系统可以以98%的效率扩展到使用1024个公共云GPU节点，提供14 petaFLOPS。由此产生的terapixel图像支持交互式浏览的城市及其数据在广泛的传感尺度。结论：云计算中可用的GPU计算资源比我们国家超级计算机上可用的任何资源都要多，从而可以访问具有全球竞争力的资源。与采购和运行这些系统相比，使用这些系统的直接财务成本很低。在克服云软件开发的初期问题方面，间接成本应该会随着时间的推移而显著降低。
英文摘要：
Background: Photo-realistic terapixel visualization is computationally intensive and to date there have been no such visualizations of urban digital twins, the few terapixel visualizations that exist have looked towards space rather than earth. Objective: our aims are: creating a scalable cloud supercomputer software architecture for visualization; a photo-realistic terapixel 3D visualization of urban IoT data supporting daily updates; a rigorous evaluation of cloud supercomputing for our application. Method: we migrated the Blender Cycles path tracer to the public cloud within a new software framework designed to scale to petaFLOP performance. Results: we demonstrate we can compute a terapixel visualization in under one hour, the system scaling at 98% efficiency to use 1024 public cloud GPU nodes delivering 14 petaFLOPS. The resulting terapixel image supports interactive browsing of the city and its data at a wide range of sensing scales. Conclusion: The GPU compute resource available in the cloud is greater than anything available on our national supercomputers providing access to globally competitive resources. The direct financial cost of access, compared to procuring and running these systems, was low. The indirect cost, in overcoming teething issues with cloud software development, should reduce significantly over time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
