high performance computing on graphics processing units: hgpu.org

Single Instruction, Multiple Data (SIMD) technology enhances performance through parallel data processing on CPUs. SIMD platforms are widely adopted across domains ranging from high-performance computing to AI inference. As modern AI workloads increasingly rely on Python-based kernel frameworks to maintain usability and benefit from automatic tuning, Triton has emerged as a representative solution. However, Triton’s autotuning mechanism, designed primarily for NVIDIA GPUs, fails to effectively exploit the architectural features of SIMD CPUs, creating a significant performance gap on these platforms. To address this problem, we introduce a human–machine collaborative design tailored for Triton kernel tuning on SIMD platforms. This design improves both development efficiency and performance by capturing high-level SIMD optimization intent from human users and integrating it seamlessly into machine framework tuning. Based on this collaborative design, we develop a tuning framework composed of a front-end for user intent recognition and a back-end for user-guided, SIMD-aware tuning. Experiments on x86 and RISC-V platforms show an average performance improvement of 31.7% over native Triton tuning, with tuning cost reduced by up to 75.0%.