SHF: Small: Algorithm/Architecture Co-Design of Low Power and High Performance Linear Algebra Compute Fabrics

SHF：小型：低功耗和高性能线性代数计算结构的算法/架构协同设计

• 批准号: 1218483
• 负责人: Andreas Gerstlauer
• 金额: $ 49.99万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218483&HistoricalAwards=false
• 关键词: SHF; Small; Algorithm; Architecture; Co

Until recently, the speed of a computer processors could be increasedby packing more transistors into a smaller area and increasing thefrequency. This trend is now unsustainable because of powerconstraints, with only moderate gains going forward even when puttinghundreds or thousands of traditional cores onto a chip. Thus, how toreduce power consumption while increasing performance is one of thecore concerns. It is well-accepted that specialization (designingparts of the processor for a specific task) and heterogeneity(designating different parts of the processor for different tasks) canlead to orders of magnitude improvements in both aspects. However,the question is whether such efficiency can be maintained whileproviding enough flexibility to implement a broad class of operations.Leveraging unique domain expertise, research under this projectaddresses this question for the domain of matrix computations, whichare at the core of many computational advances, both in scientifichigh-performance computing as well as in the embedded, mobile orcyber-physical domains.Observing that the largest benefits can be obtained throughspecialization at the foundations, this project is aimed atco-designing algorithms and architectures to directly realize basiclinear algebra methods in an optimized combination of hardware andsoftware. By designing a specialized Linear Algebra Processor (LAP),it is possible to achieve one to two orders of magnitude improvedefficiencies compared to traditional or proposed computerarchitectures. The questions that the project will answer, through acombination of analysis, simulation, and prototyping, include: (1) Howto best design such LAPs that can efficiently execute the full set oflinear algebra routines; and (2) How LAPs can be scaled, networkedinto clusters and integrated with application software running on oneor more host processors. The broad goal of this project is to developnovel, integrated linear algebra compute fabrics that are co-optimizedand co-designed across all layers ranging from the basic hardwarefoundations all the way to the application programming support throughstandard linear algebra software packages. This project is expectedto result in a leap in computational science and discoverycapabilities, thus enabling novel breakthroughs in industry, for theconsumer, at the national labs, in education and by scientists inacademia.

直到最近，计算机处理器的速度还可以通过在更小的面积上封装更多的晶体管和提高频率来提高。 由于功耗限制，这种趋势现在是不可持续的，即使在芯片上安装10个或数千个传统内核，也只能实现适度的增益。 因此，如何在提高性能的同时降低功耗是核心问题之一. 专业化（为特定任务设计处理器的部分）和异质性（为不同任务指定处理器的不同部分）可以在这两个方面带来数量级的改进，这是公认的。 然而，问题是这样的效率是否可以保持，同时提供足够的灵活性来实现广泛的操作类别。利用独特的领域专业知识，在这个项目下的研究解决了矩阵计算领域的这个问题，这是许多计算进步的核心，无论是在科学高性能计算还是在嵌入式，移动的或网络物理领域。观察到最大的好处可以通过专业化的基础上获得，该项目旨在共同设计算法和架构，以直接实现基本的线性代数方法在硬件和软件的优化组合。 通过设计一个专门的线性代数处理器（Linear Algebra Processor，简称LAE），与传统的或拟议的计算机体系结构相比，有可能实现一到两个数量级的效率提高。 该项目将通过分析、模拟和原型设计的结合来回答以下问题：（1）如何最好地设计能够有效执行全套线性代数例程的LAP;以及（2）如何扩展LAP，将其联网到集群中，并将其与运行在一个或多个主机处理器上的应用软件集成。 该项目的广泛目标是开发新颖的集成线性代数计算结构，这些结构在所有层之间进行共同优化和共同设计，从基本硬件基础一直到通过标准线性代数软件包支持应用程序编程。 该项目预计将导致计算科学和发现能力的飞跃，从而在工业，消费者，国家实验室，教育和学术界科学家中实现新的突破。