SHF: EAGER: Developing General Techniques for Tightening Bounds of the Data-Movement Complexity of Large Scale Parallel Applications

实现更快、更节能的数值模拟至关重要，例如，在基础科学中实现新的科学发现，或在工程中开发革命性的新产品。 在过去的几十年中，计算机系统中的技术趋势已经导致与数据移动性能相比，计算吞吐量的改进速率大不相同。 在未来的系统中，相对于执行算术运算的成本，在时间和能量方面，通过存储器层次结构的数据移动的成本预计将变得更加主导。因此，数值模拟的数据移动和通信成本成为求解时间和能量消耗的决定性因素。 这项研究正在开发新的通用技术，用于收紧数值模拟数据移动复杂性的界限。本研究的成果为数值模拟通信需求的推导方法。评估算法的最优性的能力使得能够理解各种计算平台的参数对数值模拟性能的影响。PI正在开发新的通用技术，用于收紧给定架构上给定算法的数据移动复杂性的界限。 这项工作涉及新颖的跨学科视角，并汇集了应用数学，理论计算机科学，数据分析和高性能计算。

Enabling faster and more energy-efficient numerical simulations is critical, for example, in basic science for enabling novel scientific discoveries, or in engineering for developing revolutionary new products. In the last decades, technology trends in computer systems have resulted in widely differing rates of improvement in computational throughput as compared to data movement performance. With future systems, the cost of data movement through the memory hierarchy is expected to become even more dominant relative to the cost of performing arithmetic operations, both in terms of time and energy. Consequently, the data movement and communication costs of a numerical simulation become determinant factors for the time to solution and the energy consumption. This research is developing novel generic techniques for tightening bounds on the data movement complexity of numerical simulations. Theoutcomes of this research are methods to derive the communication needs of numerical simulations. The ability to assess the optimality of an algorithm enables understanding of the implications of various computing platform's parameters on the performance of a numerical simulation. The PIs are developing novel generic techniques for tightening bounds on the data movement complexity of a given algorithm on a given architecture. This work engages in novel interdisciplinary perspectives and brings together applied mathematics, theoretical computer science, data analytics and high performance computing.