XPS: EXPL: DSD: A Memristive Hardware Platform for Large Scale Combinatorial Optimization

数学优化在几乎每一门科学学科中都扮演着至关重要的角色。美国国家科学院(NAS)2013年的一份报告将优化确定为对海量数据进行统计数据分析的七大巨头之一，而美国能源部(DOE)科学办公室报告称，数学优化将在艾级时代越来越多地被使用。在现代超级计算机上解决海量数据集上的大规模优化问题可能需要数周或数月的计算时间，遗憾的是，由于大量的数据移动开销，这些超级计算机只提供了峰值性能的一小部分。在数量级数学优化平台成为现实之前，需要能够将能效提高数量级的硬件和软件创新。这个项目体现了一个截然不同的未来愿景，大规模的组合优化问题被映射到以内存为中心的非冯·诺伊曼计算基板上，并在存储单元内就地求解，性能和能效比当代超级计算机高出数量级。利用电阻随机存取存储器(RRAM)技术的最新发展，构建了一种极低功耗、快速的存储器基板，用于加速组合优化算法。RRAM是一种记忆性、非易失性存储器技术，可提供类似闪存的密度和类似DRAM的读取速度。该项目利用RRAM的电学特性与CMOS晶体管相结合，在存储单元内实现就地优化，从而消除了存储阵列和计算单元之间的数据移动，降低了能量，并显著提高了性能。将开发新的算法，将不同科学和工程领域的问题映射到建议的记忆硬件基板上。将开发用于内存分配和分区、动态资源管理以及软硬件协同设计的软件模块和库，使用户能够在运行时控制优化过程。在硬件层面，加速器采用由新型RRAM单元构建的数据阵列的通用组织，不仅能够存储数据，而且能够对该数据执行现场计算。拟议的研究有望在艾级优化框架的性能和能效方面带来变革性的变化，对科学、技术和整个社会产生巨大的积极影响。架构和软件创新将通过发表的论文以及关于拟议框架和测绘算法的教程向更广泛的研究界传播。该项目的教育部分包括对研究生和本科生进行计算机体系结构方面的培训，以及将RRAM技术纳入教学大纲的存储系统课程。该协会还亲自参与促进妇女和代表不足的少数群体参与计算机科学和工程的地方项目，并不断努力增加罗切斯特大学CS和欧洲经委会项目的当地少数群体的入学人数。

Mathematical optimization plays a vital role in virtually every scientific discipline. A 2013 report by the U.S. National Academy of Sciences (NAS) identifies optimization as one of the seven giants of statistical data analysis on massive data, while the U.S. Department of Energy (DOE) Office of Science reports that mathematical optimization will increasingly be used in the exascale era. Solving large-scale optimization problems on massive datasets can require weeks or months of computation time on modern supercomputers, which regrettably deliver only a small fraction of the peak performance due to significant data movement overheads. Hardware and software innovations that can improve energy efficiency by orders of magnitude are needed before exascale platforms for mathematical optimization can become practical.This project embodies a radically different vision of the future, one where large scale combinatorial optimization problems are mapped onto a memory-centric, non-Von Neuman compute substrate and solved in situ within the memory cells, with orders of magnitude greater performance and energy efficiency than contemporary supercomputers. Recent developments in the resistive random access memory (RRAM) technology are leveraged to build an extremely low power, fast memory substrate for accelerating combinatorial optimization algorithms. RRAM is a memristive, non-volatile memory technology that provides FLASH-like density and DRAM-like read speeds. The project exploits the electrical properties of RRAM in combination with CMOS transistors to enable in situ optimization within the memory cells, thereby eliminating data movement between the memory arrays and the computational units, reducing the energy, and significantly increasing the performance. Novel algorithms will be developed to map problems from different scientific and engineering domains onto the proposed memristive hardware substrate. Software modules and libraries for memory allocation and partitioning; dynamic resource management; and hardware-software co-design will be developed to give the user control of the optimization process at runtime. At the hardware level, the accelerator employs a versatile organization of data arrays constructed from novel RRAM cells, capable not only of storing the data, but also of performing in situ computation on that data. The proposed research holds the promise of bringing about a transformative change in the performance and energy efficiency of exascale optimization frameworks, with tremendous positive fallout to science, technology, and society as a whole. Architecture and software innovations will be disseminated to the broader research community through published papers, as well as tutorials on the proposed framework and mapping algorithms. The educational component of the project involves training both graduate and undergraduate students in computer architecture, as well as a memory systems course that integrates the RRAM technology into the syllabus. The PI is also personally involved in local programs promoting the participation of women and underrepresented minorities in computer science and engineering, and has an ongoing effort to increase the enrollment of local minorities in University of Rochester's CS and ECE programs.