# 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](/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/)] --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://paper.lingyunyang.com/reading-notes/conference/osdi-2022.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.