CRII: CI: Scalable Multigrid Algorithms for Solving Elliptic PDEs on Power-Efficient Clusters

点击翻译按钮获取中文摘要

While emerging extreme-scale computing systems could provide unprecedented resources for scientific discovery, two major challenges are the cost and the energy required to run and cool these systems. The system-on-a-chip (SoC) components widely used in the mobile device market are substantially cheaper and more energy efficient compared to desktop or server processors, and represent a promising option for future systems. This project addresses three challenges for emerging extreme-scale computing systems: the potential move to mobile processors, the increasing levels of concurrency, and the need for energy efficiency. Shared infrastructure is developed to accelerate interdisciplinary and collaborative research. This is a first step in the development of mathematical and computational methods for solving scientific computing problems on low-energy systems that can reduce the overall cost of scientific discoveries and promote the progress of science. The project develops a scalable and power efficient parallel multigrid solver for elliptic partial differential equations (PDEs) that targets emerging extreme scale computing systems. Elliptic PDEs are ubiquitous in natural, engineered and societal systems, and the efficient multigrid solvers being developed as part of this project are beneficial to research across several disciplines. The project also develops a power-performance model to aid in application-controlled power-performance management, at the per-node level. Motivated by the slower interconnections common on low-power clusters, the project develops a new class of parallel algorithms that lower the power utilization of computations to overlap with the communication. This is the reverse of what has conventionally been done, where communication costs are hidden by overlapping with computation. Additionally, the algorithms utilize compute nodes that are not computationally active at all times -- a radically different approach that creates a new class of energy-efficient scalable parallel algorithms. The research will be evaluated initially using a 16 node Tegra/ARM-based cluster and ultimately, the CloudLab cluster at the University of Utah. The developed software will be disseminated using an open source license. The scalability experiments will be run on the NSF-supported CloudLab cluster, hosted at the University of Utah, allowing other users to re-create both the hardware and software stack used for the experiments. The resulting system will be among the first large scale low-energy clusters available anywhere.