CSR: Medium: A Computing Cloud for Graphical Simulation

今天，许多图形模拟运行在单个功能强大的服务器或高性能、高成本节点的小型集群上。这项研究旨在回答这个问题——在计算云中运行图形模拟是否可能？——通过设计和实现Nimbus，一个在计算云中的图形模拟软件。目标是能够使用按需云计算系统运行大型、复杂的模拟。Nimbus支持PhysBAM，这是一个由首席研究员Fedkiw开发和维护的开源图形模拟包。该项目将与现有的PhysBAM用户合作，以支持Nimbus软件的广泛使用和采用。Nimbus专注于三个重要原则，以支持在数百到数千个云服务器上运行的图形模拟。首先是解耦数据访问和布局。Nimbus用三层表示数据：程序层、逻辑层和物理层。这些层将程序操作的单元（程序）与Nimbus软件管理和传输的单元（逻辑）以及它们在实际计算机内存（物理）中的布局分开。其次是非均匀的、几何感知的数据放置。Nimbus利用模拟具有基本底层几何的事实来智能地放置数据和计算。这个几何图形在Nimbus软件中是明确的，它知道附近的模拟区域应该放在附近的计算机上。第三是动态分配和负载平衡：今天的图形模拟将模拟量平均分配给不同的计算机，尽管有些区域比其他区域需要更多的计算。Nimbus将模拟划分为许多较小的分区，并根据负载变化动态分配和移动，以减少运行时间，同时考虑分区间通信。这三个原则使Nimbus提供了极大的灵活性。该系统将模拟分解成小块，由控制计算机发送给工作计算机进行计算。这些工作计算机决定何时安排这些模拟部件以及如何将处理器分配给不同的部件。运行时根据需要自动以最有效的方式移动数据，压缩数据并在为不同部分拥有多个副本时进行复制，从而提高性能。发现如何在现代数据中心计算系统上运行这些应用程序将有助于将算术密集型科学计算带入云。随着百亿亿级和其他超级计算的发展势头日益强劲，它们的规模将需要处理云系统在过去十年中一直在解决的问题，即掉队、故障和异构。通过专注于一个特定的引人注目的应用，这项工作将为未来更广泛的努力建立一个知识框架。

Today, many graphical simulations run on a single powerful server or a small cluster of high-performance, high-cost nodes. This research aims to answer the question -- is it possible to run graphical simulations in the computational cloud? -- by designing and implementing Nimbus, a software for graphical simulation in the computing cloud. The goal is to be able to run large, complex simulations using on-demand cloud computing systems. Nimbus supports PhysBAM, an open-source graphical simulation package developed and maintained by Principal Investigator Fedkiw. The project will collaborate with existing PhysBAM users to support the Nimbus software for broader use and adoption.Nimbus focuses on three important principles to support graphical simulations running on hundreds to thousands of cloud servers. First is decoupling data access and layout. Nimbus represents data in three layers: program, logical, and physical. These layers separate the units which a program operates on (program) from the units which the Nimbus software manages and transfers (logical) from how they are laid out in actual computer memory (physical). Second is non-uniform, geometry-aware data placement. Nimbus uses the fact that simulations have a basic underlying geometry to intelligently place data and computation. This geometry is explicit in the Nimbus software, which knows that nearby regions of the simulation should be placed on nearby computers. Third is dynamic assignment and load balancing: Graphical simulations today divide the simulation volume equally across computers, despite the fact that some regions require much more computation than others. Nimbus divides a simulation into a larger number of smaller partitions, which it dynamically assigns and moves as load changes to reduce running time while considering inter-partition communication. These three principles allow Nimbus to provide tremendous flexibility. The system breaks a simulation into small pieces that a controller computer sends to worker computers to compute. These worker computers decide when to schedule these simulation pieces and how to assign processors to different pieces. The runtime automatically moves data in the most efficient manner possible as needed, compressing data and replicating it when having multiple copies for different pieces increases performance. Discovering how these applications can be run on modern data center computing systems will help bring arithmetically intensive scientific computing to the cloud. As Exascale and other supercomputing efforts gain momentum, their scale will need to deal with the same issues cloud systems have been tackling for the past decade, stragglers, failures, and heterogeneity. By focusing on one particular compelling application, this work will establish an intellectual framework for future, broader efforts.