EAGER: Collaborative Research: Memristive Accelerator for Extreme Scale Linear Solvers

EAGER：协作研究：用于超大规模线性求解器的忆阻加速器

• 批准号: 1548093
• 负责人: Jack Dongarra
• 金额: $ 3.13万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1548093&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Memristive; Accelerator

Computer models of physical systems are a vital part of modern scientific and engineering research and development. Large scale computational models of the Earth?s weather, climate, and geological activity; models of biological systems; astronomical models of galaxies; and even macroeconomic models require immense computing resources. These simulations run on many thousands of processors for several months at a time, utilizing tens or hundreds of millions of CPU hours before completion. This project takes a radically new approach to the design and implementation of next generation, exascale supercomputing by leveraging recent developments at the intersection of conventional integrated circuit technology, and emerging resistive random access memory (RRAM) devices. The goal of this project is the acceleration of solvers for large linear systems, which form the backbone of modern scientific computing. In this project a novel set of digital and analog hardware primitives is co-designed with a new class of algorithms that exploit the proposed accelerator. A small-scale prototype is being designed, fabricated, and tested through the EAGER program to demonstrate the feasibility of the fundamental building blocks. RRAM is a non-volatile memory technology that avoids the scalability challenges of static and dynamic random access memories (SRAM and DRAM), and is a promising "universal memory" candidate, offering read speeds as fast as SRAM and DRAM, and densities comparable to FLASH memory. Beyond simply relying on RRAM for storage, the project integrates circuit, architecture, and algorithm level innovations in developing a qualitatively new hardware accelerator with orders of magnitude greater performance per watt than classical digital computers. Digital memristor-based circuits avoid data movement by performing bitwise matrix vector multiplication in parallel across an entire dataset. Analog hardware quickly provides an accurate, initial seed to an iterative solver, wherein error free digital circuits refine the initial estimate to solve a system of linear equations. A novel, iterative solver algorithm uniquely adapted to the proposed hardware compensates for the inaccuracies and random variations introduced by the analog circuits, systematically reducing the error through a small number of digital iterations. This combination of digital computation and analog memristor circuits, within high-density RRAM configurations, is expected to have a transformative effect on high performance computing. The system under investigation has the potential to reduce execution time from months to hours, enabling solutions to scientific problems heretofore beyond the reach of modern HPC systems. The project brings together researchers in computer architecture, high performance integrated circuit design, numerical algorithms, and scientific computing to accomplish this multi-disciplinary effort. Algorithm, architecture, and circuit level innovations are being disseminated to the broader research community through published papers, as well as tutorials on the simulation tools. The educational component of the project involves 1) training students in VLSI, architecture, and optimization; and 2) incorporating resistive memories into the architecture and circuits curricula. The PIs are also personally involved in local programs promoting the participation of women and underrepresented minorities in computer science and engineering, and will initiate an effort to increase the enrollment of local minorities in the University of Rochester CS and ECE programs.

物理系统的计算机模型是现代科学和工程研究与发展的重要组成部分。大规模的地球计算模型？S的天气、气候和地质活动模型；生物系统模型；星系的天文模型；甚至宏观经济模型，都需要巨大的计算资源。这些模拟一次在数千个处理器上运行几个月，在完成之前利用数千万个或数亿个CPU小时。该项目利用传统集成电路技术和新兴电阻随机存取存储器(RRAM)器件的最新发展，采用一种全新的方法来设计和实施下一代亿级超级计算。这个项目的目标是加速大型线性系统的求解器，这些系统构成了现代科学计算的支柱。在这个项目中，一组新颖的数字和模拟硬件原语与一类利用所提出的加速器的新算法共同设计。一个小规模的原型正在设计、制造和测试中，通过EARGER计划来演示基本构建块的可行性。RRAM是一种非易失性存储器技术，避免了静态和动态随机存取存储器(SRAM和DRAM)的可扩展性挑战，是一种很有前途的“通用存储器”候选者，提供与SRAM和DRAM一样快的读取速度和与闪存相当的密度。除了简单地依赖RRAM进行存储之外，该项目还集成了电路、架构和算法级别的创新，以开发一种性能比传统数字计算机高出数量级的全新硬件加速器。基于数字忆阻器的电路通过在整个数据集上并行执行按位矩阵向量乘法来避免数据移动。模拟硬件快速地向迭代求解器提供准确的初始种子，其中无误差的数字电路改进初始估计以解线性方程组。一种新颖的迭代求解器算法独特地适用于所提出的硬件，补偿由模拟电路引入的不准确和随机变化，通过少量的数字迭代来系统地减少误差。在高密度RRAM配置中，数字计算和模拟忆阻器电路的这种组合预计将对高性能计算产生变革性的影响。正在研究的系统有可能将执行时间从几个月减少到几个小时，使解决迄今超出现代高性能计算系统能力范围的科学问题成为可能。该项目汇集了计算机体系结构、高性能集成电路设计、数值算法和科学计算方面的研究人员，以完成这一多学科的努力。算法、架构和电路级的创新正通过已发表的论文和有关仿真工具的教程传播给更广泛的研究社区。该项目的教育部分包括1)培训学生在VLSI、建筑和优化方面的知识；2)将电阻记忆纳入建筑和电路课程。PIS还亲自参与促进妇女和代表不足的少数族裔参与计算机科学和工程的地方计划，并将发起一项努力，以增加当地少数族裔在罗切斯特大学CS和欧洲经委会计划中的入学人数。