An Empirical Study of Issues in GPU Kernel Libraries for LLM Systems — arXiv

An Empirical Study of Issues in GPU Kernel Libraries for LLM Systems — arXiv

Yanjie Gao, Jiayi Bi, Haoxiang Lin and Ling Feng report an empirical study of issues in GPU kernel libraries used by LLM systems. They manually analyzed 300 GitHub issue reports, discussions and available fixes across seven representative libraries. The authors find that 60.33% of the sampled issues originate in the Compiler and Build System or Runtime Environment, arguing that validation needs to cover deployment and execution as well as kernel code. They also describe how kernel specialization can impose implicit constraints on tensor shapes, metadata layouts, memory aliasing and hardware resources. Violations, they say, can cause incorrect outputs or launch failures even for supported operations. The authors argue that current testing and debugging practices often miss low-precision numerical problems and hardware-specific execution failures. Their findings make the reliability of GPU kernel libraries a practical concern for developers: problems at this layer can corrupt outputs, interrupt execution, block compilation, complicate deployment or waste GPU resources. The study’s evidence comes from selected GitHub reports in seven libraries, so its findings describe that sample rather than establish how common these issues are across all GPU kernel libraries. That final point is an Escape Meridian interpretation of the study’s sample and scope.

Publication: afdf0ec0-7234-4e79-887f-198763f4d9e2

attribution — pass: The draft correctly names Yanjie Gao, Jiayi Bi, Haoxiang Lin and Ling Feng as authors on arXiv. It attributes the findings to them, and it labels the representativeness point as Escape Meridian interpretation.

Authors:Yanjie Gao, Jiayi Bi, Haoxiang Lin, Ling Feng

summary — uncertain: The central term 'GPU kernel libraries' is never explained in plain language, and neither are LLM, tensor shapes or memory aliasing. The abstract says these libraries implement core operators that underpin training and inference correctness and efficiency. Without that, a non-specialist cannot see why this layer matters. Smallest repair: add one sentence from the abstract on what these libraries do, and tooltip the terms. The draft also drops 'silently' from 'silently corrupt outputs', which weakens the source's stake.

These libraries underpin the correctness and efficiency of training and inference across diverse GPU platforms, implementing attention, GEMM, quantization, and low-precision operators

date — pass: The originalPublished date of 2026-11-06 matches the arXiv v1 submission date. The feed date of 2026-11-09 is later and is correctly not used.

[Submitted on 6 Nov 2026]

support — pass: The draft's claims match the abstract: 300 issues, seven libraries, the 60.33% figure, the constraints from kernel specialization, and the testing gaps. The consequences sentence attributes to the 'findings' what is actually the abstract's motivating framing; the harms are source-stated, but the attribution is slightly off. The scope interpretation is labeled and conditional. The question follows from finding (3).

Problems at this layer can silently corrupt outputs, hang GPUs, prevent compilation, introduce deployment incompatibilities, or degrade performance

media — pass: The selected media is the verified direct link to the original arXiv abstract page. It is the most direct access to the source.