# OSDI 2022 ## Meta Info 16th USENIX Symposium on Operating Systems Design and Implementation Homepage: Paper list: ## 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](https://paper.lingyunyang.com/reading-notes/conference/osdi-2022/orca)] * [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/)]