SHF: Small: Tools for Productive High-performance Computing with GPUs

点击翻译按钮获取中文摘要

Graphical Processing Units (GPUs) are widely and cheaply available and have become increasingly powerful relative to general-purpose CPUs. Therefore, they are attractive targets for compute-intensive applications in computational science and data science. However, development of software to run on GPUs is time-consuming and requires expertise held by only a very small fraction of the application developer community. This project is developing a collection of tools to assist in the productive development of high-performance software for GPUs, so that the barrier to effective use of GPUs by the scientific community can be lowered.A central idea being pursued in this research is the identification of primary hardware resource bottlenecks that limit performance of a GPU kernel, to guide the modification of the kernel in a manner that seeks to alleviate the identified bottleneck. Abstract kernel emulation along with sensitivity analysis with respect to hardware resource latency/throughput parameters are used for bottleneck identification. Three usage scenarios are targeted: (1) OpenMP offload, (2) domain-specific code generators, and (3) CUDA/OpenCL kernels. The offload model introduced in OpenMP 4.0 is an attractive approach for transforming existing legacy codes as well as for newly developed codes, to facilitate productivity and portability. Domain-specific library generators exploit pattern-specific semantics in order to perform optimizing transformations that are beyond the scope of general-purpose optimizing compilers. Tensor contractions and stencils are two domains of particular emphasis. For all targeted usage scenarios, the collection of tools is intended to assist developers improve the performance of GPU code through a combination of model-driven search and auto-tuning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.