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Systems for ML/AI & ML/AI for Systems paper reading list: A curated reading list of computer science research for work at the intersection of machine learning and systems. PR are welcome.
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# Marco's SysML reading list A curated reading list of computer science research for work at the intersection of machine learning and systems. PR are welcome. ## Review A Berkeley View of Systems Challenges for AI https://arxiv.org/pdf/1712.05855.pdf Strategies and Principles of Distributed Machine Learning on Big Data https://arxiv.org/abs/1512.09295 ## Background Deep learning Nature volume 521, 2015 https://www.nature.com/articles/nature14539 ## Measurement Multi-tenant GPU Clusters for Deep LearningWorkloads: Analysis and Implications https://www.microsoft.com/en-us/research/uploads/prod/2018/05/gpu_sched_tr.pdf ## Frameworks PyTorch: An Imperative Style, High-Performance Deep Learning Library NeurIPS 2019 https://arxiv.org/pdf/1912.01703 TensorFlow: A System for Large-Scale Machine Learning OSDI 2016 https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf Ray: A Distributed Framework for Emerging AI Applications OSDI 2018 https://www.usenix.org/system/files/osdi18-moritz.pdf ## Tuning HyperDrive: Exploring Hyperparameters with POP Scheduling MiddleWare 2017 https://dl.acm.org/citation.cfm?id=3135994 Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads VLDB 2018 http://www.vldb.org/pvldb/vol11/p607-li.pdf Automating Model Search for Large Scale Machine Learning SoCC 2015 http://dl.acm.org/authorize?N91362 Google Vizier: A Service for Black-Box Optimization KDD 2017 https://dl.acm.org/citation.cfm?id=3098043 Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization Journal of Machine Learning Research 18 (2018) https://arxiv.org/pdf/1603.06560.pdf Hyperopt: a Python library for model selection and hyperparameter optimization Computational Science & Discovery, 8(1) 2015 http://iopscience.iop.org/article/10.1088/1749-4699/8/1/014008 Auto-Keras: Efficient Neural Architecture Search with Network Morphism https://arxiv.org/pdf/1806.10282v2.pdf ## Runtime execution Cavs: An Efficient Runtime System for Dynamic Neural Networks ATC 2018 https://www.usenix.org/system/files/conference/atc18/atc18-xu-shizhen.pdf TVM: An Automated End-to-End Optimizing Compiler for Deep Learning OSDI 2018 https://www.usenix.org/system/files/osdi18-chen.pdf PipeDream: Fast and Efficient Pipeline Parallel DNN Training https://arxiv.org/pdf/1806.03377.pdf STRADS: A Distributed Framework for Scheduled Model Parallel Machine Learning EuroSys 2016 https://dl.acm.org/citation.cfm?id=2901331 Dynamic Control Flow in Large-Scale Machine Learning EuroSys 2018 https://dl.acm.org/citation.cfm?id=3190551 Improving the Expressiveness of Deep Learning Frameworks with Recursion EuroSys 2018 https://dl.acm.org/citation.cfm?id=3190530 Continuum: A Platform for Cost-Aware, Low-Latency Continual Learning SoCC 2018 https://dl.acm.org/citation.cfm?id=3267817 KeystoneML: Optimizing Pipelines for Large-ScaleAdvanced Analytics ICDE 2017 https://amplab.cs.berkeley.edu/wp-content/uploads/2017/01/ICDE_2017_CameraReady_475.pdf Owl: A General-Purpose Numerical Library in OCaml https://arxiv.org/pdf/1707.09616.pdf ## Distributed learning MAST: Global Scheduling of ML Training across Geo-Distributed Datacenters at Hyperscale USENIX 2024 Large Scale Distributed Deep Networks NIPS 2012 https://ai.google/research/pubs/pub40565.pdf Managed Communication and Consistency for Fast Data-Parallel Iterative Analytics SoCC 2015 http://dl.acm.org/authorize?N91363 Ako: Decentralised Deep Learning with Partial Gradient Exchange SOCC 2016 https://lsds.doc.ic.ac.uk/sites/default/files/ako-socc16.pdf Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters ATC 2017 https://www.usenix.org/system/files/conference/atc17/atc17-zhang.pdf Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training SoCC 2018 https://dl.acm.org/citation.cfm?id=3267840 MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems ML Sys
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