PULSE collocates skip-connected encoder-decoder layers and uses a skip-aware DP partitioner plus ILP scheduler to reduce communication 89% and raise throughput up to 2.3x versus prior pipeline strategies for diffusion models.
Ganger, Tianqi Chen, and Zhihao Jia
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
Heterogeneous parallelism decouples module layouts in multimodal LLM training via boundary communicators, yielding up to 49.3% TFLOPS/GPU gains in colocated mode and 13% throughput in non-colocated mode with convergence parity.
MegaScale-Omni delivers 1.27x-7.57x higher throughput for dynamic multimodal LLM training by decoupling encoder and LLM parallelism, using unified colocation, and applying adaptive workload balancing.
citing papers explorer
-
Pulse: Training Acceleration for Large Diffusion Models with Automatic Pipeline Parallelism
PULSE collocates skip-connected encoder-decoder layers and uses a skip-aware DP partitioner plus ILP scheduler to reduce communication 89% and raise throughput up to 2.3x versus prior pipeline strategies for diffusion models.
-
Heterogeneous Parallelism for Multimodal Large Language Model Training
Heterogeneous parallelism decouples module layouts in multimodal LLM training via boundary communicators, yielding up to 49.3% TFLOPS/GPU gains in colocated mode and 13% throughput in non-colocated mode with convergence parity.
-
MegaScale-Omni: A Hyper-Scale, Workload-Resilient System for MultiModal LLM Training in Production
MegaScale-Omni delivers 1.27x-7.57x higher throughput for dynamic multimodal LLM training by decoupling encoder and LLM parallelism, using unified colocation, and applying adaptive workload balancing.