ShaderPerFormer: Platform-independent Context-aware Shader Performance Predictor

Abstract

The ability to model and predict the execution time of GPU computations is crucial for real-time graphics application development and optimization. While there are many existing methodologies for graphics programmers to provide such estimates, those methods are often vendor-dependent, require the platforms to be tested, or fail to capture the contextual influences among shader instructions. To address this challenge, we propose ShaderPerFormer, a platform-independent, context-aware deep-learning approach to model GPU performance and provide end-to-end performance predictions on a per-shader basis. To provide more accurate predictions, our method contains a separate stage to gather platform-independent shader program trace information. We also provide a dataset consisting of a total of 54,667 fragment shader performance samples on 5 different platforms. Compared to the PILR and SH baseline methods, our approach reduces the average MAPE across five platforms by 8.26% and 25.25%, respectively.