arXiv (All)
open access
Compass: Dissecting Communication and Computation Operators for Efficient LLM Training
Overlapping communication and computation operators is a common practice to hide communication overheads, accelerating large language models (LLMs) training on GPU clusters. Existing systems achieve this through either intra-operator fusion (IntraFusion), which packs operators into a single large ke
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
- FlashCP: Load-Balanced Communication-Efficient Context Parallelism for LLM Training#267627
- TokenWeave: Efficient Compute-Communication Overlap for Distributed LLM Inference#189269
- Just Talk Once: Communication-Efficient Split Federated LLM Fine-Tuning on Edge Devices#821496
- Distributed Generative Inference of LLM at Internet Scales with Multi-Dimensional Communication Optimization#126492
- KernelGenBench: Can LLMs and Agents Write Efficient Kernels Across Operator Sources and Hardware Platforms?#987764
Record · ID 997555
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