ShaderNet

Abstract

This paper demonstrates ShaderNet --- our graph analytics framework with shader codes, which are machine-level codes and are important for GPU designers to tune the hardware, e.g., adjusting clock speeds and voltages. Due to a wide spectrum of use-cases of modern GPUs, engineers generally find it difficult to manually inspect a large number of shader codes emerging from these applications. To this end, we present a system, ShaderNet, which converts shader codes into graphs, and applies advanced graph mining and machine learning techniques to simplify shader graphs analysis in an effective and explainable manner. By studying shader codes' evolution with temporal graphs analysis and structure mining with frequent subgraphs, we demonstrate several key functionalities of our framework, such as a frame's scene detection, clustering scenes, and a new application's inefficient shaders prediction, which can accelerate GPU's performance tuning. Our code base and demonstration video are at: https://lzlz15.github.io/D_E_M_O/.