arXiv (All)
open access
PipeLink: Contract-Guided Linking of GPU Kernel Components
The fastest machine-learning kernels are compositions of components, such as loaders, matrix-multiply mainloops, epilogues and communication stages, that run concurrently on one GPU streaming multiprocessor and share its on-chip memory, barriers, warps and registers. Because these components hard-co
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
- KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation#1117786
- mKernel: Fast Multi-GPU, Multi-Node Fused Kernels#1071362
- mKernel: Fast Multi-GPU, Multi-Node Fused Kernels#919318
- PipeMax: Enhancing Offline LLM Inference on Commodity GPU Servers#155244
- KernelOPT: Dispatch-Aware Agentic Search for GPU Kernel Optimization#1078130
Record · ID 1468804
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