Minos: Systematically Classifying Performance and Power Characteristics of GPU Workloads on HPC Clusters

2026-04-07T08:52:41Za7f59d44a6edafbd7145d6f38779795dea0a3e197790812bf864412cc63a7197
autonomous-sreavailabilityblockchainconsensus-finalitydag-bftdiffusion-servingelasticsearchenergy-aware-schedulinggenservegpu-power-profilinghpcincident-responseledger-stigmergylemonsharkllm-tool-uselp-gemm','performance-optimization','memory-layout-propagation'minosnbi-slurmon-chain-coordinationperl-packagepreemptionslo-aware-schedulingslurmsupply-chain-risktelemetry-ingestion

What happened

This feed aggregates multiple arXiv papers on systems, distributed protocols, and ML infrastructure. Key contributions include Minos (low-cost profiling and classification of GPU/HPC workloads to predict power and performance), an autonomous LLM-driven ES Guardian Agent that manages Elasticsearch clusters end-to-end using multi-source telemetry and automated remediation, Lemonshark (an asynchronous DAG-BFT protocol enabling earlier transaction finality and lower latency), a formal framework (Ledger-State Stigmergy) for indirect on-chain coordination via ledger state, GENSERVE (heterogeneity- &

Why it matters

A reviewed impact interpretation has not been published for this record.

Evidence and limitations

Source ID
arxiv_cs_dc
Record identifier
a7f59d44a6edafbd7145d6f38779795dea0a3e197790812bf864412cc63a7197
Enrichment time
2026-04-07T08:52:41Z
AI-assisted enrichment
Yes

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Record · Minos: Systematically Classifying Performance and Power Characteristics of GPU Workloads on HPC Clusters · Baitaphish