SHF: Small: Techniques and Frameworks for Exploiting Recent SIMD Architectural Advances

单指令多数据（SIMD）并行性已经在普通处理器中出现了几十年，并被广泛应用于密集矩阵和其他常规问题。最近的体系结构趋势，例如增加SIMD通道的宽度以及指令集，提供了显著增强的功能。对于动态或不规则应用程序，需要有效地使用新的体系结构特性，这些特性在过去不容易在SIMD处理器上并行执行。PI提出了全面的研究计划，将各种类型的非结构化和/或不规则应用程序映射到现代SIMD新颖的体系结构特征，并从CUDA和OpenCL编写的程序中开发优化的编译器转换。本研究建议使用新颖的SIMD处理器指令（如分散和收集）来解决开发和执行非结构化和/或不规则应用程序的挑战。PI计划设计来自CUDA和OpenCL程序的编译器转换方法，以增加连续访问的数量，减少需要访问数据的缓存线的数量。这些新颖的变换方法针对的是诸如非结构化网格核、稀疏矩阵计算、图和树遍历等应用。PI计划继续开发一个基于操作符重载的库。

Single Instruction Multiple Data (SIMD) parallelism has been available in common processors for several decades now, and has been widely exploited for dense matrix and other regular problems. Recent architectural trends, such as increasing width of SIMD lanes coupled with instruction sets, provide significantly enhanced functionality. There is a need to effectively use the new architectural features for dynamic or irregular applications, which have not been easy to perform on parallel on SIMD processors in the past. The PI proposes comprehensive research program on mapping various classes of unstructured and/or irregular applications to modern SIMD novel architectural features and developing optimized compiler transformations from programs written in CUDA and OpenCL. This research proposal proposes to address the challenge of developing and executing unstructured and/or irregular applications using novel SIMD processors' instructions such as scatter and gather. The PI plans to design compiler transformation methods from CUDA and OpenCL programs to increase the number of contiguous accesses and decrease the number of cache lines from which data needs to be accessed. These novel transformation methods are targeting applications such as unstructured grid kernels, sparse matrix computations, and graph and tree traversals. The PI plans to continue development of an Operator Overloading based library.