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SHF: Small: High-Level Programming Models for GPUs

SHF：小型：GPU 高级编程模型

基本信息

批准号：
1718540
负责人：
John Reppy
金额：
$ 39.04万
依托单位：
University of Chicago
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2021-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718540&HistoricalAwards=false
关键词：
SHF Small Level Programming Models

项目摘要

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.

现代图形处理器单元（GPU）的性能在几年前还被归类为超级计算机级别。随着将GPU核心集成到异构多核处理器中的趋势，GPU正在成为主流处理器未来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。该项目旨在通过使用高级编译技术支持更高级别的编程模型来解决GPU编程的挑战。所提出的工作的智力价值在于，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水平。该项目的更广泛影响是将GPU的适用性扩展到更广泛的计算问题，反过来，通过支持更易于编程的GPU高级编程模型，帮助使GPU对更广泛的用户群体有用。该项目侧重于嵌套数据并行化（NDP）的使用和支持的全局扁平化转换，其通过将其编译为平面数据并行来支持不规则并行。虽然NDP为许多种不规则的并行计算提供了高级优雅的编程模型，但直接的实现与手工编写的GPU代码相比并不具有竞争力。该项目的目标是开发和评估一系列将NDP代码编译到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语言的编译器的背景下进行，这是一个小型的一阶函数语言，体现了NDP的核心概念。NESL为拟议的研究提供了一个小的，但富有表现力的背景。通过对手写CUDA和OpenCL各种不规则并行算法的解决方案进行基准测试来评估这项工作。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

From folklore to fact: comparing implementations of stacks and continuations

从民间传说到事实：比较堆栈和延续的实现

DOI：
10.1145/3385412.3385994
发表时间：
2020
期刊：
ACM SIGPLAN International Conference on Programming Language Design and Implementation
影响因子：
0
作者：
Farvardin, Kavon;Reppy, John
通讯作者：
Reppy, John

Shapes and flattening

形状和扁平化