arXiv (All)
open access
D2K-Bench: Can LLM Agents Turn Expert Designs into Efficient GPU Kernels?
GPU kernels generated by large language model (LLM) agents can remain less efficient than expert implementations, but runtime alone does not reveal how the gap relates to design discovery and implementation. We introduce D2K-Bench, a diagnostic benchmark of 26 tasks and 85 workloads that measures ho
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- MetaKernelBench: Measuring GPU Kernel Knowledge Transfer Beyond Code#1200665
- D2K-Bench: Can LLM Agents Turn Expert Designs into Efficient GPU Kernels?#1181605
- KLineage: Recovering the Missing When of Kernel Optimization by Deoptimizing Experts#1141552
- KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation#1117786
- KernelOPT: Dispatch-Aware Agentic Search for GPU Kernel Optimization#1078130
Record · ID 1187904
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive