arXiv:2606.20373v1 Announce Type: cross Abstract: Large Language Models (LLMs) show promise for code compilation tasks, but applying them to runtime performance tuning is difficult due to complex microarchitectural effects and noisy runtime measurements. We present AutoPass, a multi-agent framework for compiler performance tuning that uses compiler and runtime evidence to guide LLM-generated optimization decisions. Rather than treating the compiler as a black box like prior auto-tuning schemes, AutoPass opens up the compiler to the LLM, enabling it to query compiler-internal optimization state
The rapid advancement and increased complexity of LLMs make their application to nuanced tasks like compiler optimization a natural next step, driven by the need for more efficient software and hardware integration.
This development indicates a deeper integration of AI into foundational software infrastructure, potentially accelerating software development efficiency and hardware utilization rates significantly.
LLMs are moving from generic code generation to actively understanding and manipulating internal compiler states, making them powerful tools for performance engineering rather than just code assistants.
- · Software Developers
- · Semiconductor Companies
- · Cloud Providers
- · AI Development Platforms
- · Traditional Compiler Optimization Specialists
- · Companies with Inefficient Codebases
Compiler optimization becomes more automated and effective, leading to faster and more energy-efficient software.
The demand for specialized human compiler engineers shifts towards overseeing and refining AI-driven optimization systems.
This could lead to a virtuous cycle where more efficient code enables faster AI models, further accelerating AI development and its application across industries.
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Read at arXiv cs.AI