Correct but Slow: An Empirical Study of the GPU Kernel Evaluation Gap in Modern Domain-Specific Languages

Jul 15, 2026

A study of 22 Triton and TileLang GPU kernels reveals that correctness-based evaluation can overlook severe performance issues—one TileLang LayerNorm kernel, for example, passes correctness checks but is over 300× slower than the PyTorch baseline. The underlying causes of inefficiency vary by kernel family, with some due to repairable authoring defects and others stemming from code-generation or autotuning limitations. The authors propose two lightweight screening methods—library-relative efficiency and roofline utilization—that successfully identify all functionally correct but inefficient kernels in their evaluation.

Why it matters: This work exposes a critical flaw in current GPU kernel evaluation practices and offers practical tools to ensure that functionally correct kernels also meet performance expectations.

Full story at: arXiv Software Engineering ↗