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Formalizing GPU Kernels in Lean for Deep Learning Correctness

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to formally verify the correctness of GPU kernels used in deep learning by modeling Parallel Thread Execution (PTX) in the Lean proof assistant. This approach enables mathematical reasoning about common GPU kernel patterns, including acquire-release semantics, tensor cores, and asynchronous operations. The team successfully used this framework to prove the correctness of implementations for a WGMMA GEMM kernel, FlashAttention, and a multi-kernel Mixture of Experts (MoE) pipeline. AI

IMPACT This formal verification method could improve the reliability and efficiency of deep learning computations on GPUs.

RANK_REASON The item describes a formal verification approach for GPU kernels presented in a paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Formalizing GPU Kernels in Lean for Deep Learning Correctness

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The item describes a formal verification approach for GPU kernels presented in a paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Formalizing PTX in Lean to Prove GPU Kernel Correctness

    We present an approach to mathematically formalizing PTX, an approach that allows modeling and reasoning about patterns common in GPU kernels for Deep Learning, including acquire-release, tensor cores, proxies, async operations, multi-kernel pipelines and data-dependent control f…

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