GhostServe applies erasure coding to KV cache in host memory for fast recovery from failures in LLM serving, cutting checkpointing latency up to 2.7x and recovery latency 2.1x versus prior methods.
Understanding stragglers in large model training using what-if analysis
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
fields
cs.DC 3years
2026 3roles
background 2representative citing papers
ResiHP introduces a workload-aware failure detector and dynamic scheduler for hybrid-parallel LLM training that achieves 1.04-4.39x higher throughput than prior resilient systems under failures on a 256-GPU cluster.
citing papers explorer
-
GhostServe: A Lightweight Checkpointing System in the Shadow for Fault-Tolerant LLM Serving
GhostServe applies erasure coding to KV cache in host memory for fast recovery from failures in LLM serving, cutting checkpointing latency up to 2.7x and recovery latency 2.1x versus prior methods.
-
ResiHP: Taming LLM Training Failures with Dynamic Hybrid Parallelism
ResiHP introduces a workload-aware failure detector and dynamic scheduler for hybrid-parallel LLM training that achieves 1.04-4.39x higher throughput than prior resilient systems under failures on a 256-GPU cluster.
- From Detection to Recovery: Operational Analysis on LLM Pre-training with 504 GPUs