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

模拟已经成为科学方法的关键组成部分，由此产生的对计算能力的需求不断地推动可用硬件的极限。科学应用程序需要调整以获得高效率，但应用程序科学家手动调整无法跟上底层硬件不断变化的功能的步伐。涉及与计算相关的大量通信或内存操作的应用程序以机器峰值性能的10%以下运行的情况并不少见。该项目的目标是通过开发几种创新的方法来应对这些挑战，以解决计算机的峰值性能与实际应用程序实现的性能之间日益扩大的差距。即：为稠密线性代数、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；瞄准对高性能计算界重要的新兴体系结构，包括具有SIMD扩展的商用处理器、集群、向量处理器和高度并行的机器；为这些操作开发一个中间表示和编译模型，允许将新的操作调优到编译器和编程系统中；通过将这些功能集成到语言中来向用户提供这些能力(MatLab、UPC、Ti)；并探索新内核对更高级别算法设计的影响。该项目将通过标准库(BLAS、LAPACK、MPI、ScaLAPACK、PETSc)以及旨在使高端机器更容易访问的并行语言(UPC和钛)向用户交付经过调优的内核，并将评估我们的工作对使用这些系统的大型科学应用程序的影响。

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.