inference-fleet-sim: A Queueing-Theory-Grounded Fleet Capacity Planner for LLM Inference
2026-03-18T08:52:26Z•0437e83ea23d3baa971ef88a16a86e9c33428965001d7634320906c43885fc0c
CKKSCOCO-EFCPU-GPU integrationCompress-and-RouteGPU accelerationGPU fleet planningLLM inferenceODINP99 SLOSlideFormerTriton kernelsbiased compressioncommunication efficiencycost optimizationdataflow optimizationdiscrete-event simulationdistributed learninggradient codinghomomorphic encryptionmemory managementpre-silicon validation","NoC"queueing theoryreplay-driven validationsingle-GPU fine-tuningstraggler mitigation
What happened
This feed aggregates systems and ML infrastructure research (Mar 18 2026) focusing on GPU fleet planning and inference (inference-fleet-sim, FleetOpt, Compress-and-Route), single-GPU fine-tuning (SlideFormer), communication-efficient distributed learning (COCO-EF biased compression + gradient coding), GPU-accelerated homomorphic encryption (CKKS dataflow/performance analysis), CPU–GPU integration and replay-driven validation (ODIN architecture), HPC search algorithms for genomics (P-DFS), PIC code GPU acceleration (ECsim), parallel Newton methods for sequence-parallelization, and a BFT-consent
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_dc
- Record identifier
- 0437e83ea23d3baa971ef88a16a86e9c33428965001d7634320906c43885fc0c
- Enrichment time
- 2026-03-18T08:52:26Z
- AI-assisted enrichment
- Yes
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