Yanghua Peng
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| title | conference | date |
|---|---|---|
| Optimus: Accelerating Large-Scale Multi-Modal LLM Training by Bubble Exploitation | USENIX ATC '25 | 2025-07-07 |
Reports a system called Optimus that cuts end-to-end MLLM training time by ~20.5–21.3% on 3,072 GPUs (ViT-22B encoder + GPT-175B backbone) by turning idle GPU… | ||
| ByteCheckpoint: A Unified Checkpointing System for Large Foundation Model Development | NSDI '25 | 2025-04-28 |
ByteCheckpoint implements a parallelism-agnostic checkpoint representation and load-time resharding so checkpoints saved on one GPU layout can be read into… | ||
| MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs | 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI '24) | 2024-04-16 |
MegaScale is a production system that trains LLMs on more than 10,000 GPUs and achieves concrete gains: 55.2% Model FLOPs Utilization when training a 175B… | ||
| BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing | NSDI 2023 | 2023-04-17 |
Distributed GNN training system BGL builds a dynamic cache engine and a two-level CPU+GPU cache that together cut feature-retrieval traffic and reduce… | ||
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