SHF: Small: High-level Programming Models and Frameworks for GPGPU-based Computing

SHF：小型：基于 GPGPU 的计算的高级编程模型和框架

基本信息

批准号：
0916817
负责人：
Anand Raghunathan
金额：
$ 47.77万
依托单位：
Purdue University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2009
资助国家：
美国
起止时间：
2009-08-15 至 2013-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916817&HistoricalAwards=false
关键词：
SHF Small level Programming Models

项目摘要

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Graphics Processing Units (GPUs) have emerged as a promising alternative inthe transition of the computing industry to mainstream parallel computing.Enabling applications to benefit from their potential requires that GPU programming be made accessible to the average programmer. This research focuses on the challenges making GPU programming easier through new high-level programming models, and enabling efficient GPU execution through compilation frameworks for these models.Two complementary GPU programming models are proposed --- OpenMP, which is widely used for shared-memory parallel programming, and Parallel Operator Data-Flow Graphs (PO-DFGs), which naturally represent algorithms in a wide range of current and emerging application domains. Various optimization techniques are developed for programs written to these models, including partitioning the program between the host CPU and GPUs, stream optimizations that render the program's memory access characteristics to be more amenable to the GPU's memory system, minimizing data transfer between the host and GPU memory, and GPU architecture-specific optimizations. The research contributes to the evolution of GPGPU programming from manual ports of applications using low-level APIs, to the use of high-level parallel programming models.

该奖项根据 2009 年美国复苏和再投资法案（公法 111-5）提供资助。图形处理单元 (GPU) 已成为计算行业向主流并行计算过渡的一个有前途的替代方案。使应用程序能够从其潜力要求普通程序员能够进行 GPU 编程。本研究重点关注通过新的高级编程模型使 GPU 编程变得更容易，并通过这些模型的编译框架实现高效 GPU 执行的挑战。提出了两种互补的 GPU 编程模型 --- OpenMP，广泛用于共享内存并行编程和并行运算符数据流图 (PO-DFG)，它们自然地代表了当前和新兴应用领域中的各种算法。针对写入这些模型的程序开发了各种优化技术，包括在主机 CPU 和 GPU 之间划分程序、使程序的内存访问特性更适合 GPU 内存系统的流优化、最大限度地减少主机和 GPU 之间的数据传输内存和 GPU 架构特定的优化。该研究有助于 GPGPU 编程从使用低级 API 的应用程序手动端口发展到使用高级并行编程模型。