MICPAT: Micro-architecture Independent Characteristics Profiling Analysis Tool for GPU Programs

With the rapid evolution of GPU architectures, analyzing and optimizing the performance of GPU rendering programs has become increasingly complex and crucial. To tackle the challenges posed by this diversity and ensure microarchitecture-independent performance insights, this paper presents a comprehensive study of a GPU characteristic analysis tool called MICPAT. This tool assists users in observing the performance of microarchitecture-independent features in GPU programs before and after optimization. It extracts a series of key characteristics, including instruction mix, basic block count, instruction frequency, and CPU-to-GPU memory transfer allocation. Through comprehensive analysis of these characteristics, MICPAT provides developers with deep insights into their program behavior and performance. This enables developers to fully understand their programs and accurately identify optimization opportunities and performance bottlenecks. Taking the instruction mix characteristic as an example, it reveals the distribution of different types of instructions in the program, providing strong support for developers to perform performance optimization at the instruction level. The analysis of basic block count and program execution block size further reveals the parallelism and concurrency characteristics of the program. Additionally, MICPAT conducts a detailed analysis of instruction frequency, which helps developers optimize instruction-level performance and improve algorithm design. By using MICPAT, developers can collect and analyze characteristic data, evaluate the program's utilization of the memory hierarchy, and identify potential areas for performance optimization. To verify the effectiveness and practicality of MICPAT, this paper also presents experimental analyses using 100 GPU rendering programs, the Octane renderer, and the Rodinia and Parboil benchmark suites. Through microarchitecture-independent feature analysis, valuable insights into instruction mix, basic block count, and instruction frequency were obtained. These experimental results fully demonstrate the powerful capabilities and microarchitecture independence of MICPAT in GPU characteristic analysis. Therefore, by thoroughly analyzing program characteristics, developers can obtain important information about program performance and behavior, providing a strong reference for program optimization and performance improvement. The open source repo is https://zenodo.org/records/13623324.