ITR/NGS: Automatic Performance Tuning for Large Scale Scientific Applications

ITR/NGS：大规模科学应用的自动性能调整

• 批准号: 0325873
• 负责人: Jack Dongarra
• 金额: $ 155万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-03-01 至 2009-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0325873&HistoricalAwards=false
• 关键词: ITR; NGS; Automatic; Performance; Tuning

Simulation has become a critical component of the scientific method, with the resulting demands forcomputational power continually pushing the limits of available hardware. Scientific applications need tobe tuned to acheive high efficiency, but hand tuning by application scientists cannot keep pace with the ever changing features of the underlying hardware. It is not uncommon for applications that involve a large amount of communication or memory operations relative to computation to run at under 10% of the peak performance of a machine. The goal of the project is to address the widening gap between peak performance of computers and attained performance of real applications by developing several innovative approaches to address such challenges. Namely: design new automatic tuning techniques for dense linear algebra, sparse linear algebra, and inter processor communication kernels; target emerging architectures of importance to the high performance computing community, including commodity processors with SIMD extensions, clusters, vector processors, and highly parallel machines; develop an intermediate representation and compilation model for these operations that will allow tuning of new operations to be incorporated into compilers and programming systems; deliver these capabilities to users by integrating them into languages (Matlab, UPC, Titanium); and explore the impact of new kernels on higher level algorithm design.The project will deliver tuned kernels to users through standard libraries (BLAS, LAPACK, MPI, ScaLAPACK, PETSc) as well as parallel languages designed to make high end machines more accessible (UPC and Titanium), and will evaluate the impact of our work on large scale scientific applications that use these systems.

模拟已经成为科学方法的关键组成部分，由此产生的计算能力需求不断推动可用硬件的极限。科学应用程序需要调整以实现高效率，但应用科学家的手工调整无法跟上底层硬件不断变化的功能。对于涉及相对于计算的大量通信或存储器操作的应用程序来说，以低于机器峰值性能的10%运行并不罕见。 该项目的目标是通过开发若干创新办法来应对计算机峰值性能与真实的应用程序达到的性能之间日益扩大的差距。 即：为密集线性代数、稀疏线性代数和处理器间通信内核设计新的自动调优技术;针对高性能计算社区的重要新兴体系结构，包括具有SIMD扩展的商品处理器、集群、向量处理器和高度并行机;为这些操作开发一个中间表示和编译模型，该模型将允许对新操作进行调优，以将其合并到编译器中，编程系统;通过将这些功能集成到语言中，（Matlab，UPC，Titanium）;并探索新内核对更高层次算法设计的影响。该项目将通过标准库向用户提供经过调整的内核（BLAS、LAPACK、MPI、ScaLAPACK、PETSc）以及旨在使高端机器更易于访问的并行语言（UPC和钛），并将评估我们的工作对使用这些系统的大规模科学应用的影响。