Collaborative Research: Advanced Parallel Computing Techniques with Applications to Computational Cosmology

合作研究：先进并行计算技术及其在计算宇宙学中的应用

• 批准号: 0205611
• 负责人: Laxmikant Kale
• 金额: $ 100万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0205611&HistoricalAwards=false
• 关键词: Collaborative; Research; Advanced; Parallel; Computing

This project is advancing the state of art in both computational cosmology and parallel computing simultaneously and synergetically. Breakthroughs in cosmology, which improve our understanding of the formation of galaxies and planets, are enabled by advances in parallel computing being made in this project. Parallel machines with over hundred thousand powerful processors are now being built. NSF's widely accessible TeraScale facilities have already deployed a 3,000 processor machine in 2001. At the same time algorithmic advances have made it possible to solve problems at a much faster rate. Yet the complexity of algorithms combined with the difficulty of parallelizing them on such large machines remains a hindrance to advances in Science and Engineering. This project explores an object based methodology that is simplifying the process of developing highly efficient parallel applications. This approach allows users to write applications at the level of natural entities in the application domain, without explicit regard to which processors will house such entities andcarry out associated computations. To make this possible, the "runtime system" must be able to make fine-grained resource allocation decisions automatically. Advances in parallel computing are being sought to that end. Specifically, application developers may specify a program in terms of a collection of millions of objects that communicate with each other in several stylized patterns. In addition, parallel components can be plugged in and out of running computations, and exchange data with each other in a exible manner. Based on this infrastructure, this project is building a framework that makes it easy to build "particle-oriented" parallel programs. In addition to computational cosmology, which predomi-nately involves simulations that represent galaxies, dark matter, stars, planetary bodies and gas as particles, such programs are used in other fields as well. The framework contains highly efficient parallel algorithms that operate on collections of billions of particles, spread across machines with tens of thousands of processors. These advances are being used to carry out scientific studies in cosmology. Large, detailed simulations of structure formation powered by parallel computers are necessary to make quantitative predictions from cosmological theories. By calculating the non-linear gravitational and gas dynamics of the formation of galaxies and clusters of galaxies, we are creating galaxy catalogues, X-ray maps, and other observables that can be compared directly with new satellite and ground-based data, and thereby constrain the parameters of cosmological theories. These parameters include the amount and nature of the dark matter, the existence and equation of state of any dark energy, the total amount of baryons, and the nature of the initial uctuations in the Universe. Similar simulation studies are being used to study how planets form from a proto-solar disk in order to create an ab initio theory of planet formation. The software developed via this project is being made available to a wide community of researchers. Also, the research results of simulations can be downloaded or visualized via the web.

这个项目同时和协同地推进了计算宇宙学和并行计算的艺术状态。宇宙学的突破，提高了我们对星系和行星形成的理解，是由并行计算的进步在这个项目中实现的。现在正在建造具有超过十万个强大处理器的并行机器。2001年，美国国家科学基金会广泛使用的TeraScale设备已经部署了一台3000处理器的机器。与此同时，算法的进步使得以更快的速度解决问题成为可能。然而，算法的复杂性，加上在如此大的机器上并行化它们的难度，仍然是科学和工程进步的障碍。这个项目探索了一种基于对象的方法，它简化了开发高效并行应用程序的过程。这种方法允许用户在应用程序域的自然实体级别编写应用程序，而无需明确考虑哪个处理器将容纳这些实体并执行相关的计算。为了实现这一点，“运行时系统”必须能够自动做出细粒度的资源分配决策。为此目的，人们正在寻求并行计算的进步。具体来说，应用程序开发人员可以根据数百万个对象的集合来指定程序，这些对象以几种程式化模式相互通信。此外，并行组件可以插入和退出正在运行的计算，并以灵活的方式相互交换数据。基于此基础设施，该项目正在构建一个框架，使构建“面向粒子”的并行程序变得容易。除了计算宇宙学（主要涉及将星系、暗物质、恒星、行星体和气体作为粒子进行模拟）之外，这些程序也用于其他领域。该框架包含了高效的并行算法，可以对数十亿粒子的集合进行操作，这些粒子分布在拥有数万个处理器的机器上。这些进步正被用于开展宇宙学的科学研究。由并行计算机驱动的结构形成的大规模、详细的模拟对于从宇宙学理论中进行定量预测是必要的。通过计算星系和星系团形成的非线性引力和气体动力学，我们正在创建星系目录、x射线图和其他可观测数据，这些数据可以直接与新的卫星和地面数据进行比较，从而约束宇宙学理论的参数。这些参数包括暗物质的数量和性质，任何暗能量的存在和状态方程，重子的总量，以及宇宙初始波动的性质。类似的模拟研究也被用于研究行星是如何从原始太阳盘形成的，以便创建行星形成的从头算理论。通过这个项目开发的软件正在向广泛的研究人员社区提供。同时，模拟的研究结果可以通过网络下载或可视化。