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arXiv · 2505.03780

GPU Performance Portability needs Autotuning

Burkhard Ringlein·Thomas Parnell·Radu Stoica
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Abstract

As LLMs grow in complexity, achieving state-of-the-art performance requires tight co-design across algorithms, software, and hardware. Today's reliance on a single dominant platform limits portability, creates vendor lock-in, and raises barriers for new AI hardware. In this work, we make the case for combining just-in-time (JIT) compilation with comprehensive kernel parameter autotuning to enable portable LLM inference with state-of-the-art performance without code changes. Focusing on performance-critical LLM kernels, we demonstrate that this approach explores up to 15x more kernel parameter configurations, produces significantly more diverse code across multiple dimensions, and even outperforms vendor-optimized implementations by up to 230%, all while reducing kernel code size by 70x and eliminating manual code optimizations. Our results highlight autotuning as a promising path to unlocking model portability across GPU vendors.

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Burkhard Ringlein, Thomas Parnell, Radu Stoica. 2025-04-30. GPU Performance Portability needs Autotuning. https://arxiv.org/abs/2505.03780

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