Energy-aware code motion for GPU shader processors

Abstract

Graphics processing units (GPUs) are now being widely adopted in system-on-a-chip designs, and they are often used in embedded systems for manipulating computer graphics or even for general-purpose computation. Energy management is of concern to both hardware and software designers. In this article, we present an energy-aware code-motion framework for a compiler to generate concentrated accesses to input and output (I/O) buffers inside a GPU. Our solution attempts to gather the I/O buffer accesses into clusters, thereby extending the time period during which the I/O buffers are clock or power gated. We performed experiments in which the energy consumption was simulated by incorporating our compiler-analysis and code-motion framework into an in-house compiler tool. The experimental results demonstrated that our mechanisms were effective in reducing the energy consumption of the shader processor by an average of 13.1% and decreasing the energy-delay product by 2.2%.

Specialization Opportunities in Graphical Workloads

“…have suggested using modular shader components [41] to aid both productivity and performance by allowing more opportunities for shader specialization and allowing better pipeline resource binding to avoid large amounts of unused shader parameters being defined (as this paper's results show is common). Research has also been done into code motion techniques to optimize power consumption rather than performance [42]. Other papers explore offline compiler optimizations for GLSL shaders [7], and analysing memory usage patterns [8], but little other work has examined the data redundancy and code specialization opportunities of real-world graphics workloads.…”