arXiv (All)
open access
HOCCL: Offloading Collective Communication from GPU Cores to Accelerate Distributed Training
Large language model training involves massive computation on GPU streaming multiprocessors (SMs), the primary compute units of GPUs. Since SMs host specialized accelerators such as Tensor Cores, their efficient utilization is critical to training efficiency. Unfortunately, existing collective commu
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
- HOCCL: Offloading Collective Communication from GPU Cores to Accelerate Distributed Training#1108656
- HCCL: Collective Communication for Meta Training and Inference Accelerators#423914
- Partition-Aware Scheduling for Mobile Heterogeneous Inference Co-Execution#1083672
- Partition-Aware Scheduling for Mobile Heterogeneous Inference Co-Execution#919365
- Poseidon: DAG-Guided Parallelism Search for LLM Pre-Training on Heterogeneous Clusters#973693
Record · ID 1119407
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