# OSDP: Optimal Sharded Data Parallel for Distributed Deep Learning

arXiv: https://arxiv.org/abs/2209.13258 · HTML: https://mlsanity.com/paper/2209.13258/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2022.

## Built on by (most cited first, 12 of 12)

- [Baichuan 2: Open Large-scale Language Models](https://mlsanity.com/paper/2309.10305/) (2023)
- [Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism](https://mlsanity.com/paper/2211.13878/) (2022)
- [Improving Automatic Parallel Training via Balanced Memory Workload Optimization](https://mlsanity.com/paper/2307.02031/) (2023)
- [Quantized Distributed Training of Large Models with Convergence Guarantees](https://mlsanity.com/paper/2302.02390/) (2023)
- [AReaL-Hex: Accommodating Asynchronous RL Training over Heterogeneous GPUs](https://mlsanity.com/paper/2511.00796/) (2025)
- [Angel-PTM: A Scalable and Economical Large-scale Pre-training System in Tencent](https://mlsanity.com/paper/2303.02868/) (2023)
- [Cascadia: An Efficient Cascade Serving System for Large Language Models](https://mlsanity.com/paper/2506.04203/) (2025)
- [BOute: Cost-Efficient LLM Serving with Heterogeneous LLMs and GPUs via Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2602.10729/) (2026)
- [OServe: Accelerating LLM Serving via Spatial-Temporal Workload Orchestration](https://mlsanity.com/paper/2602.12151/) (2026)
- [Autopoiesis: A Self-Evolving System Paradigm for LLM Serving Under Runtime Dynamics](https://mlsanity.com/paper/2604.07144/) (2026)
- [HexAGenT: Efficient Agentic LLM Serving via Workflow- and Heterogeneity-Aware Scheduling](https://mlsanity.com/paper/2605.16637/) (2026)
- [Predictability as a Fine-Grained Measure for Privacy](https://mlsanity.com/paper/2606.20546/) (2026)

## Abstract (arXiv)

Large-scale deep learning models contribute to significant performance improvements on varieties of downstream tasks. Current data and model parallelism approaches utilize model replication and partition techniques to support the distributed training of ultra-large models. However, directly deploying these systems often leads to sub-optimal training efficiency due to the complex model architectures and the strict device memory constraints. In this paper, we propose Optimal Sharded Data Parallel (OSDP), an automated parallel training system that combines the advantages from both data and model parallelism. Given the model description and the device information, OSDP makes trade-offs between the memory consumption and the hardware utilization, thus automatically generates the distributed computation graph and maximizes the overall system throughput. In addition, OSDP introduces operator splitting to further alleviate peak memory footprints during training with negligible overheads, which enables the trainability of larger models as well as the higher throughput. Extensive experimental results of OSDP on multiple different kinds of large-scale models demonstrate that the proposed strategy outperforms the state-of-the-art in multiple regards. Our code is available at https://github.com/Youhe-Jiang/OptimalShardedDataParallel.