SHF: Small:Transforming Linear Algebra Libraries

SHF：小型：转换线性代数库

• 批准号: 0917167
• 负责人: Robert van de Geijn
• 金额: $ 40万
• 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: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0917167&HistoricalAwards=false
• 关键词: SHF; Small; Transforming; Linear; Algebra

Ever since the first FORmula TRANslator, compilers have purported totake an algorithm, formulated in human terms, and compile it to anefficient executable. Practice clearly does not live up to thisideal, as programming languages force execution-oriented designdecisions on the programmer, and compiler imperfections necessitatefurther manual optimizations. While the ordinary programmer canignore this problem by using libraries, the library developer cannot.The problem appears more desperate now than ever, since it is notknown what form future architectures will take, combined with a trendtowards pushing complexity away from the architecture (for example tolower power consumption) and onto the program and compiler.The FLAME project already allows linear algebra libraries to bedeveloped and coded at a high level of abstraction that bettercaptures the underlying algorithms. It has already been shown thatthese technique greatly simplify the porting of libraries to differentplatforms ranging from conventional sequential computers to exoticmultiGPU accelerators. However, the APIs for coding at a high levelof abstraction incur a considerable performance penalty for operationsthat in each step perform relatively little computation (e.g., level-2BLAS operations and ``unblocked'' algorithms). As a result, anontrivial part of libflame, the library that has resulted from theproject, still requires coding at a low level.The project will develop a source-to-source translator will takealgorithms expressed at a high level of abstraction and will transformthese into a range of representations, including high-performancelow-level code. This will overcome the final legitimate objection tothe FLAME methodology since coding at a high level of abstraction willno longer carry a performance penalty. The approach will begeneralized so that code transformations that an expert applies byhand in order to target different platforms will be made mechanical.Together, these represent a major departure from traditionalapproaches to library development.

自从第一个FORMULA TRANSLATOR出现以来，编译器就一直声称要获取一个用人类语言表述的算法，并将其编译成一个高效的可执行程序。 实践显然达不到这一点，因为编程语言迫使程序员做出面向执行的设计决策，而编译器的不完善需要进一步的手动优化。 虽然普通程序员可以通过使用库来忽略这个问题，但库开发人员却不能。这个问题现在似乎比以往任何时候都更加紧迫，因为不知道未来的体系结构将采取什么形式，结合了一种将复杂性从建筑中剥离的趋势（例如降低功耗）FLAME项目已经允许在更高的抽象层次上开发和编码线性代数库，从而更好地捕获底层算法。 已经表明，这些技术大大简化了库到不同平台的移植，从传统的顺序计算机到exoticmultiGPU加速器。 然而，用于在高抽象级别编码的API对于在每个步骤中执行相对较少计算的操作（例如，2级BLAS操作和“未阻塞”算法）。 因此，libflame的一个非平凡部分，这个项目产生的库，仍然需要在低层次上编码。该项目将开发一个源到源翻译器，它将把在高抽象层次上表达的算法转换成一系列的表示，包括高性能的低层次代码。 这将克服对FLAME方法的最后合理反对，因为在高抽象级别上编码将不再带来性能损失。 这种方法将被推广，使得专家为了针对不同平台而手工应用的代码转换将变得机械化。总之，这些代表了对库开发的传统方法的重大偏离。