> For the complete documentation index, see [llms.txt](https://paper.lingyunyang.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://paper.lingyunyang.com/reading-notes/conference/osdi-2022.md).

# OSDI 2022

## Meta Info

16th USENIX Symposium on Operating Systems Design and Implementation

Homepage: <https://www.usenix.org/conference/osdi22>

Paper list: <https://www.usenix.org/conference/osdi22/technical-sessions>

## Papers

### Automated Parallelism for DNN Training

* [Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning](https://www.usenix.org/conference/osdi22/presentation/zheng-lianmin) \[[Code](https://github.com/alpa-projects/alpa)]
* [Unity: Accelerating DNN Training Through Joint Optimization of Algebraic Transformations and Parallelization](https://www.usenix.org/conference/osdi22/presentation/unger) \[[Code](https://github.com/flexflow/flexflow)]

### Scheduling for DNN Training

* [Looking Beyond GPUs for DNN Scheduling on Multi-Tenant Clusters](https://www.usenix.org/conference/osdi22/presentation/mohan) \[[Code](https://github.com/msr-fiddle/synergy)]

### Model Serving

* [Orca: A Distributed Serving System for Transformer-Based Generative Models](https://www.usenix.org/conference/osdi22/presentation/yu) \[[Personal Notes](/reading-notes/conference/osdi-2022/orca.md)]
* [Microsecond-scale Preemption for Concurrent GPU-accelerated DNN Inferences](https://www.usenix.org/conference/osdi22/presentation/han) \[[Code](https://github.com/SJTU-IPADS/reef), [Benchmark](https://github.com/SJTU-IPADS/disb), [Artifact](https://github.com/SJTU-IPADS/reef-artifacts/tree/osdi22-ae)]

### Sparse Models

* [SparTA: Deep-Learning Model Sparsity via Tensor-with-Sparsity-Attribute](https://www.usenix.org/conference/osdi22/presentation/zheng-ningxin) \[[Code](https://github.com/microsoft/SparTA)]

### DL Compiler

* [ROLLER: Fast and Efficient Tensor Compilation for Deep Learning](https://www.usenix.org/conference/osdi22/presentation/zhu) \[[Code](https://github.com/microsoft/nnfusion/tree/osdi22_artifact/artifacts)]

### Collaborative ML

* Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning \[[Code](https://github.com/alibaba/MNN), [中文官网](https://www.mnn.zone/m/0.3/)]


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