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

• 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

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.

该奖项是根据2009年《美国复苏和再投资法案》(Public Law 111-5)资助的。在计算行业向主流并行计算的过渡中，图形处理单元(GPU)已成为一种很有前途的替代方案。要使应用程序从其潜力中受益，需要让普通程序员能够使用GPU编程。提出了两种互补的GPU编程模型-OpenMP和并行操作符数据流图(PO-DFGs)，前者广泛用于共享内存并行编程，后者自然地代表了当前和新兴应用领域中的算法。为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、使程序的存储器访问特性更服从于GPU的存储器系统的流优化、最小化主机和GPU存储器之间的数据传输、以及特定于GPU体系结构的优化。这项研究有助于GPGPU编程从使用低级API的手动应用程序移植到使用高级并行编程模型的演变。