Domain-specific translator and optimizer for massive on- chip parallelism

Future supercomputers will rely on massive on-chip parallelism that requires dramatic changes be made to node architecture. Node architecture will become more heterogeneous and hierarchical, with software-managed on-chip memory becoming more prevalent. To meet the performance expectations, application software will undergo extensive redesign. In response, support from programming models is crucial to help scientists adopt new technologies without requiring significant programming effort. In this dissertation, we address the programming issues of a massively parallel single chip processor with a software-managed memory. We propose the Mint programming model and domain-specific compiler as a means of simplifying application development. Mint abstracts away the programmer's view of the hardware by providing a high-level interface to low-level architecture-specific optimizations. The Mint model requires modest recoding of the application and is based on a small number of compiler directives, which are sufficient to take advantage of massive parallelism. We have implemented the Mint model on a concrete instance of a massively parallel single chip processor: the Nvidia GPU (Graphics Processing Unit). The Mint source-to-source translator accepts C source with Mint annotations and generates CUDA C. The translator includes a domain-specific optimizer targeting stencil methods. Stencil methods arise in image processing applications and in a wide range of partial differential equation solvers. The Mint optimizer performs data locality optimizations, and uses on-chip memory to reduce memory accesses, particularly useful for stencil methods. We have demonstrated the effectiveness of Mint on a set of widely used stencil kernels and three real-world applications. The applications include an earthquake-induced seismic wave propagation code, an interest point detection algorithm for volume datasets and a model for signal propagation in cardiac tissue. In cases where hand-coded implementations are available, we have verified that Mint delivered competitive performance. Mint realizes around 80% of the performance of the hand-optimized CUDA implementations of the kernels and applications on the Tesla C1060 and C2050 GPUs. By facilitating the management of parallelism and the memory hierarchy on the chip at a high-level, Mint enables computational scientists to accelerate their software development time. Furthermore, by performing domain-specific optimizations, Mint delivers high performance for stencil methods.