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ML for Systems Bibliography

This file contains a formatted version of the ML for Systems bibliography associated with the article "A Taxonomy of ML for Systems Problems" in IEEE Micro. For the full annotated bibliography in bibtex format, see bibliography.bib. To cite, please use:

@article{maas2020_ml_taxonomy,
  author={Martin Maas},
  journal={IEEE Micro},
  title={A Taxonomy of ML for Systems Problems},
  year={2020},
  volume={40},
  number={5},
  pages={8-16},
}

Compilers

Optimizations

  • Learning to Optimize Tensor Programs, Chen, Tianqi and Zheng, Lianmin and Yan, Eddie and Jiang, Ziheng and Moreau, Thierry and Ceze, Luis and Guestrin, Carlos and Krishnamurthy, Arvind, Advances in Neural Information Processing Systems 31

    • Taxonomy Category: Optimization

    • ML Strategies: Supervised Learning, Transfer Learning, Gradient Boosted Trees, TreeGRU

  • Compiler Auto-Vectorization with Imitation Learning, Mendis, Charith and Yang, Cambridge and Pu, Yewen and Amarasinghe, Dr.Saman and Carbin, Michael, Advances in Neural Information Processing Systems 32

    • Taxonomy Category: Discovery, Optimization

    • ML Strategies: Imitation Learning, Graph Neural Networks

Performance Models

  • A Learned Performance Model for the Tensor Processing Unit, Samuel J. Kaufman and Phitchaya Mangpo Phothilimthana and Yanqi Zhou and Mike Burrows, arXiv:2008.01040

    • Taxonomy Category: Extrapolation, Forecasting

    • ML Strategies: Supervised Learning, Graph Neural Networks

  • Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks, Mendis, Charith and Renda, Alex and Amarasinghe, Saman and Carbin, Michael, ICML

    • Taxonomy Category: Extrapolation, Forecasting

    • ML Strategies: Supervised Learning, LSTMs

Computer Architecture

Hardware Design

  • Chip Placement with Deep Reinforcement Learning, Azalia Mirhoseini and Anna Goldie and Mustafa Yazgan and Joe Jiang and Ebrahim Songhori and Shen Wang and Young-Joon Lee and Eric Johnson and Omkar Pathak and Sungmin Bae and Azade Nazi and Jiwoo Pak and Andy Tong and Kavya Srinivasa and William Hang and Emre Tuncer and Anand Babu and Quoc V. Le and James Laudon and Richard Ho and Roger Carpenter and Jeff Dean, arXiv:2004.10746

    • Taxonomy Category: Optimization

    • ML Strategies: Reinforcement Learning

Performance Counters

  • A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions, Alam, Mejbah and Gottschlich, Justin and Tatbul, Nesime and Turek, Javier S and Mattson, Tim and Muzahid, Abdullah, Advances in Neural Information Processing Systems 32

    • Taxonomy Category: Anomaly Detection

    • ML Strategies: Autoencoders

Speculation

  • Learning Memory Access Patterns, Hashemi, Milad and Swersky, Kevin and Smith, Jamie and Ayers, Grant and Litz, Heiner and Chang, Jichuan and Kozyrakis, Christos and Ranganathan, Parthasarathy, Proceedings of the 35th International Conference on Machine Learning

    • Taxonomy Category: Forecasting

    • ML Strategies: Supervised Learning, LSTMs, Clustering

  • Dynamic branch prediction with perceptrons, Jimenez, Daniel A. and Lin, Calvin, Proceedings HPCA Seventh International Symposium on High-Performance Computer Architecture

    • Taxonomy Category: Forecasting

    • ML Strategies: Supervised Learning, Perceptrons

Computer Systems

Distributed Systems

  • Seer: Leveraging Big Data to Navigate the Complexity of Performance Debugging in Cloud Microservices, Gan, Yu and Zhang, Yanqi and Hu, Kelvin and Cheng, Dailun and He, Yuan and Pancholi, Meghna and Delimitrou, Christina, Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems

    • Taxonomy Category: Anomaly Detection

    • ML Strategies: Supervised learning, LSTMS

Scheduling

  • Paragon: QoS-Aware Scheduling for Heterogeneous Datacenters, Delimitrou, Christina and Kozyrakis, Christos, Proceedings of the Eighteenth International Conference on Architectural Support for Programming Languages and Operating Systems

    • Taxonomy Category: Extrapolation

    • ML Strategies: Collaborative Filtering

  • Quasar: Resource-Efficient and QoS-Aware Cluster Management, Delimitrou, Christina and Kozyrakis, Christos, Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems

    • Taxonomy Category: Extrapolation

    • ML Strategies: Collaborative Filtering

  • PES: Proactive Event Scheduling for Responsive and Energy-Efficient Mobile Web Computing, Feng, Yu and Zhu, Yuhao, Proceedings of the 46th International Symposium on Computer Architecture

    • Taxonomy Category: Forecasting

    • ML Strategies: Logistic Regression

  • Learning Scheduling Algorithms for Data Processing Clusters, Mao, Hongzi and Schwarzkopf, Malte and Venkatakrishnan, Shaileshh Bojja and Meng, Zili and Alizadeh, Mohammad, Proceedings of the ACM Special Interest Group on Data Communication

    • Taxonomy Category: Discovery

    • ML Strategies: Reinforcement Learning

  • Device Placement Optimization with Reinforcement Learning, Mirhoseini, Azalia and Pham, Hieu and Le, Quoc V. and Steiner, Benoit and Larsen, Rasmus and Zhou, Yuefeng and Kumar, Naveen and Norouzi, Mohammad and Bengio, Samy and Dean, Jeff, Proceedings of the 34th International Conference on Machine Learning - Volume 70

    • Taxonomy Category: Optimization

    • ML Strategies: Reinforcement Learning, LSTMs

Storage Systems

  • Learning Relaxed Belady for Content Distribution Network Caching, Zhenyu Song and Daniel S. Berger and Kai Li and Wyatt Lloyd, 17th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 20)

    • Taxonomy Category: Discovery

    • ML Strategies: Imitation Learning

  • Multi-Task Learning for Storage Systems, Giulio Zhou and Martin Maas, Workshop on ML for Systems at NeurIPS 2019

    • Taxonomy Category: Extrapolation

    • ML Strategies: Supervised learning, Transformers, Multi-Task Learning

Databases

Index Structures

  • The Case for Learned Index Structures, Kraska, Tim and Beutel, Alex and Chi, Ed H. and Dean, Jeffrey and Polyzotis, Neoklis, Proceedings of the 2018 International Conference on Management of Data

    • Taxonomy Category: Forecasting

    • ML Strategies: Supervised Learning, Neural Networks

Query Optimization

  • Deep Unsupervised Cardinality Estimation, Yang, Zongheng and Liang, Eric and Kamsetty, Amog and Wu, Chenggang and Duan, Yan and Chen, Xi and Abbeel, Pieter and Hellerstein, Joseph M. and Krishnan, Sanjay and Stoica, Ion, Proc. VLDB Endow., 13(3)

    • Taxonomy Category: Forecasting

    • ML Strategies: Autoregressive Models

Language Runtime Systems

Memory Management

  • Learned Garbage Collection, Cen, Lujing and Marcus, Ryan and Mao, Hongzi and Gottschlich, Justin and Alizadeh, Mohammad and Kraska, Tim, Proceedings of the 4th ACM SIGPLAN International Workshop on Machine Learning and Programming Languages

    • Taxonomy Category: Discovery

    • ML Strategies: Reinforcement Learning

  • Learning-Based Memory Allocation for C++ Server Workloads, Maas, Martin and Andersen, David G. and Isard, Michael and Javanmard, Mohammad Mahdi and McKinley, Kathryn S. and Raffel, Colin, Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems

    • Taxonomy Category: Extrapolation, Forecasting

    • ML Strategies: Supervised learning, LSTMs