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heterogeneous hardware

topic2 events
papersTODAY 04:00 UTC

Concertina: Adaptive Pipeline Parallelism for Long-Context LLM Training

A new arXiv paper introduces Concertina, a data-centric approach to pipeline parallelism aimed at making long-context LLM training more efficient. The authors argue that sequence parallelism carries heavy communication costs, and that pipeline parallelism's benefits depend on how the model and data are partitioned. Concertina adaptively splits pipeline stages to better handle long sequences on heterogeneous hardware.

long-context/training/pipeline-parallelism/distributed-training
papersTODAY 04:00 UTC

OpWeave: Operator-Level Disaggregation for Heterogeneous LLM Serving

A new arXiv paper introduces OpWeave, a system that breaks LLM inference into finer-grained operators rather than coarse stages, extending recent work that separates attention from FFN or MoE execution during decoding. The authors argue this operator-level disaggregation improves how workloads are matched to heterogeneous hardware during serving.

llm-serving/inference/efficiency/arxiv