GPUs are increasingly playing vital roles in the modern technology industry. Improving the GPU performance involves optimizing its architectural design and fine-tuning its software code. However, to achieve this, engineers must investigate codes from as many GPU-related applications as possible to identify code portions that need fine-tuning. Moreover, this effort requires engineers to have good domain knowledge, and their work is made more arduous because the source codes of applications are normally confidential. To this end, we introduce ShaderAnalyzer, a solution leveraging graph mining and machine learning to analyze GPU-executed low-level machine codes and identify their fine-tuning opportunities. Our approach includes representing machine code with graph structure and subsequently identifying frequently occurring substructures within the codes. Optimizing the execution of these substructures can enhance the overall performance of the GPU. In addition, our model leverages these frequent patterns to further facilitate engineers’ tasks by selecting representative patterns to predict and investigate low-efficiency ones. We conduct comprehensive experiments to evaluate the performance of our solution, and the results have been validated by our industry partners. ShaderAnalyzer is an end-to-end framework that helps engineers identify code segments with the highest potential for performance gains after fine-tuning and offers valuable insights for hardware architects in future products design.
