HeLoCo: Efficient asynchronous low-communication training under data and device heterogeneity
arXiv 2606.00271•d04f5a563114cd1f190fae984d16a6a2c6eb36f004c42ea6424ac08170133770
CPU-NPU-heterogeneityGNN-trainingGPU-placementKV-cacheLLM-servingMoE-servingRoPE-correctionagentic-inferenceasynchronous-trainingcarbon-aware-schedulingdistributed-trainingenergy-predictiongeometric-consensushardware-variabilitylow-communicationmicromagneticsmomentum-correctionmulti-GPUmulti-agent-systemsnon-IID-dataonline-routingrequest-routingresiliencescientific-workflowssheaf-theory
Paper metadata
- arXiv ID
- 2606.00271
- Version
- Not specified by this published record
- Category
- Computer Science — Distributed, Parallel, and Cluster Computing (cs.DC)
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Evidence and limitations
- Source ID
- arxiv_cs_dc
- Record identifier
- d04f5a563114cd1f190fae984d16a6a2c6eb36f004c42ea6424ac08170133770
- Enrichment time
- 2026-06-02T08:52:26Z
- AI-assisted enrichment
- Yes
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