Energy Efficient Scheduling of AI/ML Workloads on Multi Instance GPUs with Dynamic Repartitioning
arXiv 2606.25082•60f023437afe6222249304221710f33afa9bd8a6b0a5eb5f387a559c847c2ddc
BFTBitcoinByzantine-fault-toleranceGEMMGPUMIGPairHMMai-mlarxivdata-centerdecentralized-governanceedge-cloudedge-systemsenergy-efficiencygenomicsgrid-interactivep-bitsprobabilistic-computingreinforcement-learningresearchschedulingservice-placementspeculative-decodingtensor-coresuncertainty-quantification
Paper metadata
- arXiv ID
- 2606.25082
- 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
- 60f023437afe6222249304221710f33afa9bd8a6b0a5eb5f387a559c847c2ddc
- Enrichment time
- 2026-06-25T08:52:26Z
- AI-assisted enrichment
- Yes
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