Replication in Graph Partitioning and Scheduling Problems
2026-05-04T08:52:21Z•b427a7a2b01980de36d1ee67685b4307dde72857067e858fdec74504d65bc0a3
AIE-MLDAG-schedulingGEMMGPU-clustersIPUKV-cacheLLM-Emu','serving-emulation','profile-driven-sampling' , 'emulatLLM-servingMixture-of-ExpertsMoEPopdistPoplar-SDKSAGATempusVersal-AI-Edgeapproximation-complexitycomputational-fluid-dynamicsedge-inferenceexpert-placementgraph-partitioninginteger-linear-programmingreplicationsatellite-constellationspace-data-centersworkflow-scheduling
What happened
Collection of recent systems and algorithms papers (arXiv May 2026) focusing on distributed ML/AI execution, hardware acceleration, and resource-placement/scheduling tradeoffs. Key contributions include: theoretical and empirical analysis of replication for graph partitioning and DAG scheduling (showing large cost reductions but increased approximation hardness); adapting ML-accelerated CFD training to Graphcore IPUs (Poplar/Popdist optimizations); Space-XNet for two-level placement of MoE layers/experts across satellite constellations to cut inference latency; SAGA, a workflow-atomic GPU-club
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_dc
- Record identifier
- b427a7a2b01980de36d1ee67685b4307dde72857067e858fdec74504d65bc0a3
- Enrichment time
- 2026-05-04T08:52:21Z
- AI-assisted enrichment
- Yes
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