PerfDojo: Automated ML Library Generation for Heterogeneous Architectures

📝 Original Info

• Title: PerfDojo: Automated ML Library Generation for Heterogeneous Architectures
• ArXiv ID: 2511.03586
• Date: 2025-11-05
• Authors: 논문에 명시된 저자 정보가 제공되지 않았습니다. 저자 목록을 확인하려면 원문을 참고하시기 바랍니다. —

📝 Abstract

The increasing complexity of machine learning models and the proliferation of diverse hardware architectures (CPUs, GPUs, accelerators) make achieving optimal performance a significant challenge. Heterogeneity in instruction sets, specialized kernel requirements for different data types and model features (e.g., sparsity, quantization), and architecture-specific optimizations complicate performance tuning. Manual optimization is resource-intensive, while existing automatic approaches often rely on complex hardware-specific heuristics and uninterpretable intermediate representations, hindering performance portability. We introduce PerfLLM, a novel automatic optimization methodology leveraging Large Language Models (LLMs) and Reinforcement Learning (RL). Central to this is PerfDojo, an environment framing optimization as an RL game using a human-readable, mathematically-inspired code representation that guarantees semantic validity through transformations. This allows effective optimization without prior hardware knowledge, facilitating both human analysis and RL agent training. We demonstrate PerfLLM's ability to achieve significant performance gains across diverse CPU (x86, Arm, RISC-V) and GPU architectures.

💡 Deep Analysis

📄 Full Content

Reference