RRFP introduces a readiness-driven runtime for pipeline parallelism that uses schedules as hints and ready-set arbitration to improve utilization under runtime variability, reporting up to 2.77x speedup on multimodal workloads.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.DC 2representative citing papers
InfiniPipe proposes elastic pipeline parallelism and stage-aware chunk-level adaptive checkpointing to achieve 1.69x speedup over state-of-the-art for variable-length long-context LLM training.
citing papers explorer
-
A Readiness-Driven Runtime for Pipeline-Parallel Training under Runtime Variability
RRFP introduces a readiness-driven runtime for pipeline parallelism that uses schedules as hints and ready-set arbitration to improve utilization under runtime variability, reporting up to 2.77x speedup on multimodal workloads.
-
InfiniPipe: Elastic Pipeline Parallelism for Efficient Variable-Length Long-Context LLM Training
InfiniPipe proposes elastic pipeline parallelism and stage-aware chunk-level adaptive checkpointing to achieve 1.69x speedup over state-of-the-art for variable-length long-context LLM training.