DataKernelBench: Can LLMs Optimize Database Queries on GPUs?
Quick summary
arXiv:2608.25061v2 Announce Type: replace-cross Abstract: GPUs increasingly accelerate database systems, but query-specific peak performance still often relies on hand-written kernels. Existing LLM kernel benchmarks focus on machine learning operators, leaving irregular, heterogeneous, data-movement-heavy database-style operators untested. We introduce DataKernelBench, which translates SQL into validated PyTorch TorchPlan programs and evaluates LLMs that optimize either the core tensor-bounded snippet or the full query in CUDA or Triton through execution-guided repair. Across ten proprietary a
Key takeaways
- arXiv:2608.25061v2 Announce Type: replace-cross Abstract: GPUs increasingly accelerate database systems, but query-specific peak performance still often relies on hand-written kernels.
- Existing LLM kernel benchmarks focus on machine learning operators, leaving irregular, heterogeneous, data-movement-heavy database-style operators untested.
- We introduce DataKernelBench, which translates SQL into validated PyTorch TorchPlan programs and evaluates LLMs that optimize either the core tensor-bounded snippet or the full query in CUDA or Triton through execution-guided repair.
Why it matters
“DataKernelBench: Can LLMs Optimize Database Queries on GPUs?” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
Member comments