FSZ: Breaking the Prediction-Throughput Trade-off in GPU Lossy Compression
arXiv 2607.15413•bfcdad65eb2fac7f7b6d09829b412ac3b1bcb13cfd2001c8f6ba9d457b2b4846
ADAscaleByzantine fault toleranceFSZGPU collectivesGPU lossy compressionHonest Quorum ProblemIPU renderingJoyNexusLLM agent toolingMCP gatewayModel Context Protocol (MCP)NCCLSQUIROaccess controldependency-aware autoscalingisolationlow-latency networkingmicroservice placementmulti-tenant trainingpost-quantum considerationsquantum-classical schedulingserverless autoscalingsession affinity
Paper metadata
- arXiv ID
- 2607.15413
- 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
- bfcdad65eb2fac7f7b6d09829b412ac3b1bcb13cfd2001c8f6ba9d457b2b4846
- Enrichment time
- 2026-07-20T08:52:18Z
- AI-assisted enrichment
- Yes
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