Efficient Training on Multiple Consumer GPUs with RoundPipe
arXiv 2604.27085•699c5bb384e35ead883216994710b5d3214886a12d34e349d367efcc9e948d99
CFD simulationDMR/ProteoFlexTenderGPU kernelsGPU offloadingHyperledger FabricLLM trainingLoRAMPI spawningPresburger arithmeticQwen3-235BRoundPipeTendermintWCET optimizationZipCCLblockchain optimizationcommunication collectivesdistributed trainingdynamic resource managementexecute-order-validate (EOV)lossless compressionmixed-criticality systemsorder-execute blockchainspipeline parallelismpopulation protocols
Paper metadata
- arXiv ID
- 2604.27085
- 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
- 699c5bb384e35ead883216994710b5d3214886a12d34e349d367efcc9e948d99
- Enrichment time
- 2026-05-01T08:52:25Z
- AI-assisted enrichment
- Yes
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