CAREER: Software Abstractions for Stochastic Embedding in Predictive Simulations on Extreme-Scale Cyberinfrastructure

职业：超大规模网络基础设施预测模拟中随机嵌入的软件抽象

• 批准号: 1350454
• 负责人: Onkar Sahni
• 金额: $ 49.94万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-02-01 至 2020-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1350454&HistoricalAwards=false
• 关键词: CAREER; Software; Abstractions; Stochastic; Embedding

Computer simulations for scientific problems are now considered as the third pillar of scientific inquiry, where simulation-based prediction for increasingly complex real-world problems has been matched with the growth in computing power. These physical problems can be described mathematically through models that encompass complex stochastic multiscale systems. In such models several terms and parameters are uncertain and not accounting for these uncertainties in the system-level prediction can lead to significant inaccuracies and futile predictions. For reliable predictions, the uncertainties must be statistically quantified to understand their effects on quantities being evaluated by the simulation. This need has given rise to several simulation tools that have been applied to tackle challenging problems. However, these simulation tools are often integrated in an ad-hoc fashion leading to under utilization of the hardware, or limited applicability in terms of the problem at hand, or both. Therefore, algorithmic and software elements and abstractions are needed that can re-use existing components, and support creation of new ones, such that they can be integrated with ease to construct effective tools for stochastic simulations.To achieve this goal, this project is investigating novel abstractions based on a rigorous and systematic approach to stochastic embedding techniques. Stochastic embedding implies insertion of uncertainty propagation loops/samples in the calculations within the physics engine. The idea of embedding is to increase the computational efficiency. With embedding, different software components become aware of the stochastic discretization and account for it not only in the underlying floating-point operations but also in parallelization and communications. The focus of this project is on generalizations that target different physics analysis codes and a broad range of stochastic discretization techniques including adaptive collocation, low-rank separated representation, and stochastic Galerkin. The ultimate goal is to achieve tremendously efficient and new levels of reliable predictive simulations on next-generation computing platforms and cyberinfrastructure, where the size of the overall stochastic problem is enormous (e.g., with many trillions of degrees-of-freedom in the joint spatiotemporal-stochastic space).The research goal is to provide remarkable improvements in our ability to reliably predict and control the performance of complex stochastic multiscale systems, which in-turn will have great scientific, economic and social impacts (e.g., in making energy generation and management systems highly efficient and reliable). The resulting techniques are expected to be applicable to other broad research areas such as large-scale parametric studies, optimization and inverse problems. This project builds on a comprehensive three-pronged education plan that includes K-12, undergraduate and graduate students as well as broader community (including industry). The idea is to educate and grow the next generation of researchers focused on advanced computing and computational science. This will be done through summer camps, courses and workshops. In order to have the maximum impact, results from this research will be disseminated via a variety of methods such as conference presentations, journal papers, software documents, and tutorials.

对科学问题的计算机模拟现在被认为是科学研究的第三支柱，其中对日益复杂的现实世界问题的基于模拟的预测已经与计算能力的增长相匹配。这些物理问题可以通过包含复杂随机多尺度系统的模型来数学描述。在这种模型中，一些项和参数是不确定的，如果不考虑系统级预测中的这些不确定性，可能会导致严重的不准确和徒劳的预测。为了进行可靠的预测，必须对不确定性进行统计量化，以了解它们对通过模拟评估的数量的影响。这种需求催生了几种模拟工具，它们已被应用于解决具有挑战性的问题。然而，这些模拟工具通常以特别的方式集成，导致硬件利用率不足，或者就手头的问题而言适用性有限，或者两者兼而有之。因此，需要能够重用现有组件并支持创建新组件的算法、软件元素和抽象，以便它们可以轻松地集成在一起来构建有效的随机模拟工具。为了实现这一目标，本项目基于严格和系统的随机嵌入技术研究新的抽象。随机嵌入意味着在物理引擎内的计算中插入不确定传播环路/样本。嵌入的思想是为了提高计算效率。通过嵌入，不同的软件组件意识到随机离散化，并不仅在底层浮点运算中考虑随机离散化，而且在并行化和通信中考虑随机离散化。本项目的重点是针对不同物理分析代码的泛化和广泛的随机离散化技术，包括自适应配置、低阶分离表示和随机Galerkin。最终目标是在下一代计算平台和网络基础设施上实现非常高效和可靠的预测模拟，其中总体随机问题的规模是巨大的(例如，在时空-随机联合空间中具有数万亿个自由度)。研究目标是显著提高我们可靠预测和控制复杂随机多尺度系统性能的能力，这反过来将产生巨大的科学、经济和社会影响(例如，使能源生产和管理系统高效和可靠)。由此产生的技术有望适用于其他广泛的研究领域，如大规模参数研究、优化和反问题。该项目建立在一个全面的三管齐下的教育计划之上，该计划包括K-12、本科生和研究生以及更广泛的社区(包括行业)。这个想法是为了教育和培养专注于先进计算和计算科学的下一代研究人员。这将通过夏令营、课程和讲习班来完成。为了产生最大的影响，这项研究的结果将通过各种方法传播，如会议报告、期刊论文、软件文档和教程。