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

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。图形处理器（GPU）已经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中受益需要GPU编程能够被普通程序员访问。本文主要研究如何通过新的高级编程模型简化GPU编程，并通过编译框架实现GPU高效执行，提出了两种互补的GPU编程模型--广泛用于共享内存并行编程的OpenMP和并行运算符数据流图（PO-DFG），其自然地代表了广泛的当前和新兴应用领域中的算法。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，使程序的存储器访问特性更适合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的应用程序的手动端口到使用高级并行编程模型的演变。

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.