SHF: Small: High-Level Programming Models for GPUs

现代图形处理器单元（gpu）的性能在几年前还被归为超级计算机级别。随着GPU内核集成到异构多核处理器的趋势，GPU正在成为未来主流处理器性能增长的重要来源。不幸的是，gpu是出了名的难以编程，特别是对于不规则的并行计算。该项目旨在通过支持高级编译技术的高级编程模型来解决gpu编程的挑战。所建议的工作的智力优点是，它推进了gpu和其他加速器架构的编译技术和编程模型的最新状态。该项目更广泛的影响是扩大gpu对更广泛的计算问题的适用性，反过来，通过支持更容易编程的gpu的高级编程模型，帮助gpu对更广泛的用户社区有用。该项目侧重于使用嵌套数据并行（NDP）和支持全局平坦化转换，通过将不规则的并行性编译为平坦的数据并行性来支持不规则的并行性。虽然NDP为多种不规则并行计算提供了高级优雅的编程模型，但直接的实现无法与手写的GPU代码竞争。该项目的目标是开发和评估一组用于将NDP代码编译为GPU的技术，目的是使NDP与手写CUDA和OpenCL代码竞争。这项工作将在Blelloch的NESL语言编译器的背景下进行，NESL语言是一种小型的一阶函数语言，体现了NDP的核心概念。NESL为提出的研究提供了一个小而富有表现力的背景。通过对各种不规则并行算法的手写CUDA和OpenCL解决方案进行基准测试来评估这项工作。

Modern Graphics-Processor Units (GPUs) are capable of performance that, just a few years ago, would have been classified as supercomputer-level. With the trend of integrating GPU cores into heterogeneous multicore processors, GPUs are becoming an important source of future performance growth in mainstream processors. Unfortunately, GPUs are notoriously hard to program, especially for irregular parallel computations. This project aims to address the challenges of programming GPUs by supporting higher-level programming models with advanced compilation techniques. The intellectual merits of the proposed work are that it advances the state of the art in compilation techniques and programming models for GPUs and other accelerator architectures. The broader impact of the project is to widen the applicability of GPUs to a wider range of computational problems and, in turn, to help make GPUs useful to a broader community of users by supporting higher-level programming models for GPUs that are easier to program.The project focuses on the use of Nested Data Parallelism (NDP) and the supporting global flattening transformation, which supports irregular parallelism by compiling it down to flat data parallelism. While NDP provides a high-level elegant programming model for many kinds of irregular parallel computations, a straightforward implementation is not competitive with hand-written GPU code. The goal of this project it to develop and evaluate a collection of techniques for compiling NDP code to GPU with the objective of making NDP competitive with hand-written CUDA and OpenCL code. The work will be carried out in the context of a compiler for Blelloch's NESL language, which is a small first-order functional language that embodies the core concepts of NDP. NESL provides a small, but expressive, context for the proposed research. The work is evaluated by benchmarking against handwritten CUDA and OpenCL solutions for various irregular parallel algorithms.