> 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/atc-2022.md). # ATC 2022 ## Meta Info 2022 USENIX Annual Technical Conference Homepage: Paper list: ## Papers ### DNN Inference * [DVABatch: Diversity-aware Multi-Entry Multi-Exit Batching for Efficient Processing of DNN Services on GPUs](https://www.usenix.org/conference/atc22/presentation/cui) \[[Personal Note](/reading-notes/conference/atc-2022/dvabatch.md), [Code](https://github.com/sjtu-epcc/DVABatch)] * [Serving Heterogeneous Machine Learning Models on Multi-GPU Servers with Spatio-Temporal Sharing](https://www.usenix.org/conference/atc22/presentation/choi-seungbeom) \[[Personal Note](/reading-notes/conference/atc-2022/gpulet.md), [Code](https://github.com/casys-kaist/glet)] * [SOTER: Guarding Black-box Inference for General Neural Networks at the Edge](https://www.usenix.org/conference/atc22/presentation/shen) \[[Personal Note](/reading-notes/conference/atc-2022/soter.md), [Code](https://github.com/hku-systems/SOTER)] ### DNN Training * [Whale: Efficient Giant Model Training over Heterogeneous GPUs](https://www.usenix.org/conference/atc22/presentation/jia-xianyan) \[[Personal Note](/reading-notes/conference/atc-2022/whale.md), [Code](https://github.com/alibaba/EasyParallelLibrary)] ### Resource Manager * [PilotFish: Harvesting Free Cycles of Cloud Gaming with Deep Learning Training](https://www.usenix.org/conference/atc22/presentation/zhang-wei) \[[Personal Note](/reading-notes/conference/atc-2022/pilotfish.md)] * [Memory Harvesting in Multi-GPU Systems with Hierarchical Unified Virtual Memory](https://www.usenix.org/conference/atc22/presentation/choi-sangjin) \[[Personal Note](/reading-notes/conference/atc-2022/memharvester.md)]