Profiler: An Exploration of Micro-Architecture-Oblivious GPU Program Characteristics Profiling and Analysis Framework
Peng Wang, Kaiyuan Qi, Dong Zhang, Liu Peng · 2025
Given the swift advancements in GPU architectures, the process of assessing and improving GPU rendering performance has grown increasingly sophisticated and vital. To address these challenges and provide microarchitecture-agnostic insights, this paper introduces MICPAT, a tool for GPU characteristic profiling. MICPAT extracts key program characteristics such as instruction composition, basic block count, instruction frequency and memory allocation size across NVIDIA's Kepler, Maxwell, Pascal, and Volta GPU series. By analyzing these microarchitecture-agnostic characteristics, developers gain deep insights into the behavior and performance of their GPU programs. MICPAT supports precompiled applications utilizing CUDA, OpenACC, OpenCL, or CUDA Fortran. Serving as a versatile platform, MICPAT enables consistent analysis across this diverse set of GPU architectures and precompiled application environments. Utilizing Octane renderer, as well as Rodinia and Parboil benchmarks, extensive experimental evaluations across 100 GPU rendering applications have validated MICPAT's efficacy and its microarchitecture-agnostic nature. The open source repo is https://zenodo.org/records/13623324.