Xe-Forge: Multi-Stage LLM-Powered Kernel Optimization for Intel GPU
arXiv 2605.26118•aeece70e96094ea43c244dd8e63d420b740a771f3568b0687210c52cfdfd71e4
agentic-aiapi-routingbspcommunication-efficiencyconcurrencyedge-aiembedded-systemsfederated-learningformal-semanticsgpu-fault-resiliencehardware-in-the-loopintel-gpukernel-optimizationknowledge-basekv-cachellm-augmentationllm-servingmulti-tenant-gpunvidia-mpsopenmpprivacyresiliencesplit-federatedthird-party-servicestriton
Paper metadata
- arXiv ID
- 2605.26118
- 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
- aeece70e96094ea43c244dd8e63d420b740a771f3568b0687210c52cfdfd71e4
- Enrichment time
- 2026-05-27T08:52:16Z
- AI-assisted enrichment
- Yes
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