Files
cs249r_book/mlsysim/docs/references.bib
Vijay Janapa Reddi 05bdff6e68 Citation-reference audit: prose rewrites + author-form cleanup
Codex chapter-by-chapter audit findings applied across vol1 and vol2:
prose rewrites where the cited source supported only a narrower or
adjacent claim, plus new audit tooling
(book/tools/scripts/build_citation_reference_packets.py and the
companion workflow doc).

Author-form cleanup pass on top of the audit:
- 19 narrative @key conversions where prose already named the author
  (e.g., "Sambasivan et al. describe ... [@sambasivan2021]" -> narrative
  "@sambasivan2021 describe ...", rendering "Sambasivan et al. (2021)
  describe ...") - removes the citeproc duplication that the
  manual-bracket regex did not catch.
- 4 [-@key] suppressed-author conversions for possessive eponyms
  (Han et al., Vaswani et al., Patarasuk and Yuan, Linnainmaa).
- 2 [-@key] conversions where the prose names an author/entity that
  matches the cited author (Patterson and Hennessy's iron law,
  Google's data centers + Google whitepaper).
- 1 Horowitz footnote rewritten to narrative so the inline year
  replaces a duplicated "(ISSCC 2014, ...) (Horowitz 2014)" pair.
- 1 narrative @russell2021 anchoring "As Russell argues" that was
  previously a bare attribution.
- 1 pre-existing narrative fix for "Graham et al. report ..." in
  collective_communication that had no cite on the line.

Pre-commit cleanups landing with this commit:
- subramanya2019diskann: add publisher = Curran Associates (NeurIPS
  proceedings, per bib-check rule 3).
- Remove orphaned @misc{Wu2016} GNMT bib entry in vol2; it had no
  citations and collided case-insensitively with @inproceedings{wu2016}
  (the cited Quantized CNNs paper) under bibtex-tidy's normalization.
- vol1/training/training.qmd: drop the dead UtilizationGap LEGO cell
  (gpu_real_tflops_*, cluster_*_tflops_*) and the dead
  TrainingModels.{gpt3_gpu_years,gpt3_compute_cost}_str exports plus
  their unused upstream values.
- vol2/distributed_training/distributed_training.qmd: anchor the
  orphaned [^fn-parameter-server] footnote on the body-prose mention
  of "parameter-server systems" rather than deleting the definition.

manual-bracket hook (book-check-refs) green at this tip.
2026-05-23 13:35:14 -04:00

577 lines
19 KiB
BibTeX

@inproceedings{abadi2016,
title = {Deep Learning With Differential Privacy},
author = {Abadi, Martin and Chu, Andy and Goodfellow, Ian and others},
year = {2016},
booktitle = {Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security},
publisher = {ACM},
pages = {308--318},
doi = {10.1145/2976749.2978318},
url = {https://doi.org/10.1145/2976749.2978318},
source = {Crossref},
}
@inproceedings{agrawal2025,
title = {Taming {Throughput-Latency} Tradeoff in {LLM} Inference with {Sarathi-Serve}},
author = {
Agrawal, Amey and Kedia, Nitin and Panwar, Ashish and Mohan, Jayashree and Kwatra, Nipun and
Gulavani, Bhargav and Tumanov, Alexey and Ramjee, Ramachandran
},
year = {2024},
month = jul,
booktitle = {18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24)},
publisher = {USENIX Association},
pages = {117--134},
isbn = {978-1-939133-40-3},
url = {https://www.usenix.org/conference/osdi24/presentation/agrawal},
}
@misc{amodei2018ai,
title = {{AI} and Compute},
author = {Amodei, Dario and Hernandez, Danny},
year = {2018},
url = {https://openai.com/research/ai-and-compute},
howpublished = {OpenAI Blog},
}
@book{barroso2019,
title = {The Datacenter as a Computer},
author = {Barroso, Luiz Andr{\'e} and H{\"o}lzle, Urs and Ranganathan, Parthasarathy},
year = {2018},
publisher = {Springer International Publishing},
series = {Synthesis Lectures on Computer Architecture},
doi = {10.1007/978-3-031-01761-2},
isbn = {9783031006333, 9783031017612},
issn = {1935-3235, 1935-3243},
url = {https://doi.org/10.1007/978-3-031-01761-2},
subtitle = {Designing Warehouse-Scale Machines},
source = {Crossref},
edition = {3rd},
}
@article{chowdhery2022palm,
title = {{PaLM}: Scaling Language Modeling with Pathways},
author = {Chowdhery, Aakanksha and Narang, Sharan and Devlin, Jacob and others},
year = {2023},
journal = {Journal of Machine Learning Research},
volume = {24},
number = {240},
pages = {1--113},
}
@article{daly2006,
title = {A Higher Order Estimate of the Optimum Checkpoint Interval for Restart Dumps},
author = {Daly, John T.},
year = {2006},
journal = {Future Gener. Comput. Syst.},
publisher = {Elsevier BV},
volume = {22},
number = {3},
pages = {303--312},
doi = {10.1016/j.future.2004.11.016},
issn = {0167-739X},
url = {https://doi.org/10.1016/j.future.2004.11.016},
source = {Crossref},
}
@inproceedings{dao2022,
title = {FlashAttention: Fast and Memory-Efficient Exact Attention With IO-Awareness},
author = {Dao, Tri and Fu, Daniel Y. and Ermon, Stefano and Rudra, Atri and R{\'e}, Christopher},
year = {2022},
booktitle = {Advances in Neural Information Processing Systems 35},
publisher = {Neural Information Processing Systems Foundation, Inc. (NeurIPS)},
pages = {16344--16359},
doi = {10.52202/068431-1189},
url = {https://doi.org/10.52202/068431-1189},
source = {Crossref},
}
@article{dean2012large,
title = {Large Scale Distributed Deep Networks},
author = {Dean, Jeffrey and Corrado, Greg S. and Monga, Rajat and others},
year = {2012},
journal = {Advances in Neural Information Processing Systems},
volume = {25},
}
@article{dean2013,
title = {The Tail at Scale},
author = {Dean, Jeffrey and Barroso, Luiz Andr{\'e}},
year = {2013},
journal = {Communications of the ACM},
publisher = {Association for Computing Machinery (ACM)},
volume = {56},
number = {2},
pages = {74--80},
doi = {10.1145/2408776.2408794},
issn = {0001-0782, 1557-7317},
url = {https://doi.org/10.1145/2408776.2408794},
source = {Crossref},
}
@inproceedings{eisenman2022checknrun,
title = {Check-N-Run: a Checkpointing System for Training Deep Learning Recommendation Models},
author = {
Eisenman, Assaf and Matam, Kiran Kumar and Ingram, Steven and Mudigere, Dheevatsa and
Krishnamoorthi, Raghuraman and Nair, Krishnakumar and Smelyanskiy, Misha and Annavaram, Murali
},
year = {2022},
booktitle = {
Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI)
},
publisher = {USENIX Association},
}
@article{fedus2022switch,
title = {Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity},
author = {Fedus, William and Zoph, Barret and Shazeer, Noam},
year = {2022},
journal = {Journal of Machine Learning Research},
volume = {23},
number = {120},
pages = {1--39},
url = {https://www.jmlr.org/papers/v23/21-0998.html},
}
@inbook{gholami2022,
title = {A Survey of Quantization Methods for Efficient Neural Network Inference},
author = {Gholami, Amir and Kim, Sehoon and Dong, Zhen and others},
year = {2021},
journal = {arXiv preprint arXiv:2103.13630},
booktitle = {Low-Power Computer Vision},
publisher = {Chapman and Hall/CRC},
pages = {291--326},
doi = {10.1201/9781003162810-13},
isbn = {9781003162810},
url = {https://doi.org/10.1201/9781003162810-13},
source = {Crossref},
}
@article{han2015deep,
title = {
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and
Huffman Coding
},
author = {Han, Song and Mao, Huizi and Dally, William J.},
year = {2015},
journal = {arXiv preprint arXiv:1510.00149},
}
@book{hennessy2019architecture,
title = {Computer Architecture: A Quantitative Approach},
author = {Hennessy, John L. and Patterson, David A.},
year = {2019},
publisher = {Morgan Kaufmann},
isbn = {978-0128119051},
edition = {6th},
}
@inproceedings{hoffmann2022chinchilla,
title = {Training Compute-Optimal Large Language Models},
author = {Hoffmann, Jordan and Borgeaud, Sebastian and Mensch, Arthur and others},
year = {2022},
journal = {arXiv preprint arXiv:2203.15556},
booktitle = {Advances in Neural Information Processing Systems 35},
publisher = {Neural Information Processing Systems Foundation, Inc. (NeurIPS)},
pages = {30016--30030},
doi = {10.52202/068431-2176},
url = {https://doi.org/10.52202/068431-2176},
source = {Crossref},
}
@inproceedings{isaev2023,
title = {
Calculon: A Methodology and Tool for High-Level Co-Design of Systems and Large Language Models
},
author = {Isaev, Mikhail and McDonald, Nic and Dennison, Larry and Vuduc, Richard},
year = {2023},
booktitle = {
Proceedings of the International Conference for High Performance Computing, Networking, Storage
and Analysis
},
publisher = {ACM},
pages = {1--14},
doi = {10.1145/3581784.3607102},
url = {https://doi.org/10.1145/3581784.3607102},
source = {Crossref},
}
@inproceedings{jouppi2017,
title = {In-Datacenter Performance Analysis of a Tensor Processing Unit},
author = {Jouppi, Norman P. and Young, Cliff and Patil, Nishant and others},
year = {2017},
booktitle = {Proceedings of the 44th Annual International Symposium on Computer Architecture},
publisher = {ACM},
pages = {1--12},
doi = {10.1145/3079856.3080246},
url = {https://doi.org/10.1145/3079856.3080246},
source = {Crossref},
}
@article{kaplan2020scaling,
title = {Scaling Laws for Neural Language Models},
author = {Kaplan, Jared and McCandlish, Sam and Henighan, Tom and others},
year = {2020},
journal = {arXiv preprint arXiv:2001.08361},
}
@article{korthikanti2023,
title = {Reducing Activation Recomputation in Large Transformer Models},
author = {
Korthikanti, Vijay Anand and Casper, Jared and Lym, Sangkug and McAfee, Lawrence and Andersch,
Michael and Shoeybi, Mohammad and Catanzaro, Bryan
},
year = {2023},
journal = {Proceedings of Machine Learning and Systems},
volume = {5},
url = {
https://proceedings.mlsys.org/paper_files/paper/2023/hash/80083951326cf5b35e5100260d64ed81-Abstract-mlsys2023.html
},
}
@inproceedings{kwon2023,
title = {Efficient Memory Management for Large Language Model Serving With PagedAttention},
author = {Kwon, Woosuk and Li, Zhuohan and Zhuang, Siyuan and others},
year = {2023},
booktitle = {Proceedings of the 29th Symposium on Operating Systems Principles},
publisher = {ACM},
pages = {611--626},
doi = {10.1145/3600006.3613165},
url = {https://doi.org/10.1145/3600006.3613165},
source = {Crossref},
}
@article{leiserson1985,
title = {Fat-Trees: Universal Networks for Hardware-Efficient Supercomputing},
author = {Leiserson, Charles E.},
year = {1985},
journal = {IEEE Transactions on Computers},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
volume = {C-34},
number = {10},
pages = {892--901},
doi = {10.1109/tc.1985.6312192},
issn = {0018-9340},
url = {https://doi.org/10.1109/tc.1985.6312192},
source = {Crossref},
}
@article{lepikhin2020gshard,
title = {{GShard}: Scaling Giant Models with Conditional Computation and Automatic Sharding},
author = {
Lepikhin, Dmitry and Lee, HyoukJoong and Xu, Yuanzhong and Chen, Dehao and Firat, Orhan and
Huang, Yanping and Krikun, Maxim and Shazeer, Noam and Chen, Zhifeng
},
year = {2020},
journal = {arXiv preprint arXiv:2006.16668},
url = {https://arxiv.org/abs/2006.16668},
}
@inproceedings{leviathan2023fast,
title = {Fast Inference from Transformers via Speculative Decoding},
author = {Leviathan, Yaniv and Kalman, Matan and Matias, Yossi},
year = {2023},
booktitle = {Proceedings of the 40th International Conference on Machine Learning (ICML)},
publisher = {PMLR},
url = {https://arxiv.org/abs/2211.17192},
}
@article{little1961,
title = {A Proof for the Queuing Formula: <I>L</i> = \ensuremath{\Lambda}<I>W</i>},
author = {Little, John D. C.},
year = {1961},
journal = {Oper. Res.},
publisher = {Institute for Operations Research and the Management Sciences (INFORMS)},
volume = {9},
number = {3},
pages = {383--387},
doi = {10.1287/opre.9.3.383},
issn = {0030-364X, 1526-5463},
url = {https://doi.org/10.1287/opre.9.3.383},
source = {Crossref},
}
@article{mattson2020,
title = {MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance},
author = {Mattson, Peter and Cheng, Christine and Diamos, Gregory and others},
year = {2020},
journal = {IEEE Micro},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
volume = {40},
number = {2},
pages = {8--16},
doi = {10.1109/mm.2020.2974843},
issn = {0272-1732, 1937-4143},
url = {https://doi.org/10.1109/mm.2020.2974843},
source = {Crossref},
}
@book{mlsysbook2025,
title = {
Machine Learning Systems: Principles and Practices of Engineering Artificially Intelligent
Systems
},
author = {Reddi, Vijay Janapa and others},
year = {2025},
publisher = {Harvard University},
url = {https://mlsysbook.ai},
}
@article{mohan2021,
title = {Analyzing and Mitigating Data Stalls in DNN Training},
author = {Mohan, Jayashree and Phanishayee, Amar and Raniwala, Ashish and Chidambaram, Vijay},
year = {2021},
journal = {Proc. VLDB Endow.},
publisher = {Association for Computing Machinery (ACM)},
volume = {14},
number = {5},
pages = {771--784},
doi = {10.14778/3446095.3446100},
issn = {2150-8097},
url = {https://doi.org/10.14778/3446095.3446100},
source = {Crossref},
}
@article{murray2021tf,
title = {{tf.data}: A Machine Learning Data Processing Framework},
author = {Murray, Derek G. and Simsa, Jiri and Klimovic, Ana and Indyk, Ihor},
year = {2021},
journal = {Proceedings of the VLDB Endowment},
publisher = {VLDB Endowment},
volume = {14},
number = {12},
}
@inproceedings{narayanan2021,
title = {Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM},
author = {Narayanan, Deepak and Shoeybi, Mohammad and Casper, Jared and others},
year = {2021},
booktitle = {
Proceedings of the International Conference for High Performance Computing, Networking, Storage
and Analysis
},
publisher = {ACM},
pages = {1--15},
doi = {10.1145/3458817.3476209},
url = {https://doi.org/10.1145/3458817.3476209},
source = {Crossref},
}
@misc{nvidia2023h100,
title = {{NVIDIA H100 Tensor Core GPU} Datasheet},
author = {{NVIDIA Corporation}},
year = {2023},
note = {Accessed: 2024-06-15},
howpublished = {\url{https://www.nvidia.com/en-us/data-center/h100/}},
}
@inproceedings{parashar2019,
title = {Timeloop: A Systematic Approach to DNN Accelerator Evaluation},
author = {
Parashar, Angshuman and Raina, Priyanka and Shao, Yakun Sophia and Chen, Yu-Hsin and Emer, Joel
and others
},
year = {2019},
booktitle = {2019 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)},
publisher = {IEEE},
pages = {304--315},
doi = {10.1109/ispass.2019.00042},
url = {https://doi.org/10.1109/ispass.2019.00042},
source = {Crossref},
}
@article{patarasuk2009,
title = {Bandwidth optimal all-reduce algorithms for clusters of workstations},
author = {Patarasuk, Pitch and Yuan, Xin},
year = {2009},
journal = {Journal of Parallel and Distributed Computing},
publisher = {Elsevier},
volume = {69},
number = {2},
pages = {117--124},
doi = {10.1016/j.jpdc.2008.09.002},
url = {https://doi.org/10.1016/j.jpdc.2008.09.002},
}
@inproceedings{patel2024,
title = {Splitwise: Efficient Generative LLM Inference Using Phase Splitting},
author = {
Patel, Pratyush and Choukse, Esha and Zhang, Chaojie and Shah, Aashaka and Goiri, {\'I}{\~n}igo
and Maleki, Saeed and Bianchini, Ricardo
},
year = {2024},
booktitle = {2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA)},
publisher = {IEEE},
pages = {118--132},
doi = {10.1109/isca59077.2024.00019},
url = {https://doi.org/10.1109/isca59077.2024.00019},
source = {Crossref},
}
@article{patterson2021carbon,
title = {Carbon Emissions and Large Neural Network Training},
author = {Patterson, David and Gonzalez, Joseph and Le, Quoc and others},
year = {2021},
journal = {arXiv preprint arXiv:2104.10350},
}
@inproceedings{pope2023llm,
title = {Efficiently Scaling Transformer Inference},
author = {Pope, Reiner and Douglas, Sholto and Chowdhery, Aakanksha and others},
year = {2023},
booktitle = {Proceedings of Machine Learning and Systems (MLSys)},
publisher = {mlsys.org},
volume = {5},
url = {
https://proceedings.mlsys.org/paper_files/paper/2023/hash/c4be71ab8d24cdfb45e3d06dbfca2780-Abstract-mlsys2023.html
},
}
@inproceedings{rajbhandari2020,
title = {ZeRO: Memory Optimizations Toward Training Trillion Parameter Models},
author = {Rajbhandari, Samyam and Rasley, Jeff and Ruwase, Olatunji and He, Yuxiong},
year = {2020},
booktitle = {
SC20: International Conference for High Performance Computing, Networking, Storage and Analysis
},
publisher = {IEEE},
pages = {1--16},
doi = {10.1109/sc41405.2020.00024},
url = {https://doi.org/10.1109/sc41405.2020.00024},
source = {Crossref},
}
@inproceedings{rasley2020,
title = {DeepSpeed},
author = {Rasley, Jeff and Rajbhandari, Samyam and Ruwase, Olatunji and He, Yuxiong},
year = {2020},
booktitle = {
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \&amp; Data
Mining
},
publisher = {ACM},
pages = {3505--3506},
doi = {10.1145/3394486.3406703},
url = {https://doi.org/10.1145/3394486.3406703},
subtitle = {System Optimizations Enable Training Deep Learning Models With Over 100 Billion Parameters},
source = {Crossref},
}
@article{shazeer2017outrageously,
title = {Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer},
author = {Shazeer, Noam and Mirhoseini, Azalia and Maziarz, Krzysztof and others},
year = {2017},
journal = {arXiv preprint arXiv:1701.06538},
}
@article{shoeybi2019megatron,
title = {{Megatron-LM}: Training Multi-Billion Parameter Language Models Using Model Parallelism},
author = {
Shoeybi, Mohammad and Patwary, Mostofa and Puri, Raul and LeGresley, Patrick and Casper, Jared
and Catanzaro, Bryan
},
year = {2019},
journal = {arXiv preprint arXiv:1909.08053},
}
@inproceedings{snell2025scaling,
title = {Scaling {LLM} Test-Time Compute Optimally can be More Effective than Scaling Model Parameters},
author = {Snell, Charlie and Lee, Jaehoon and Xu, Kelvin and Kumar, Aviral},
year = {2025},
booktitle = {Proceedings of the 13th International Conference on Learning Representations (ICLR)},
publisher = {OpenReview.net},
note = {Oral presentation. arXiv:2408.03314},
}
@article{williams2009,
title = {Roofline},
author = {Williams, Samuel and Waterman, Andrew and Patterson, David},
year = {2009},
journal = {Communications of the ACM},
publisher = {Association for Computing Machinery (ACM)},
volume = {52},
number = {4},
pages = {65--76},
doi = {10.1145/1498765.1498785},
issn = {0001-0782, 1557-7317},
url = {https://doi.org/10.1145/1498765.1498785},
subtitle = {An Insightful Visual Performance Model for Multicore Architectures},
source = {Crossref},
}
@inproceedings{won2023,
title = {
ASTRA-Sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-Model Training
at Scale
},
author = {
Won, William and Heo, Taekyung and Rashidi, Saeed and Sridharan, Srinivas and Srinivasan,
Sudarshan and Krishna, Tushar
},
year = {2023},
booktitle = {2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)},
publisher = {IEEE},
pages = {283--294},
doi = {10.1109/ispass57527.2023.00035},
url = {https://doi.org/10.1109/ispass57527.2023.00035},
source = {Crossref},
}
@inproceedings{wu2019,
title = {Accelergy: An Architecture-Level Energy Estimation Methodology for Accelerator Designs},
author = {Wu, Yannan Nellie and Emer, Joel S. and Sze, Vivienne},
year = {2019},
booktitle = {2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)},
publisher = {IEEE},
pages = {1--8},
doi = {10.1109/iccad45719.2019.8942149},
url = {https://doi.org/10.1109/iccad45719.2019.8942149},
source = {Crossref},
}
@article{young1974,
title = {A First Order Approximation to the Optimum Checkpoint Interval},
author = {Young, John W.},
year = {1974},
journal = {Communications of the ACM},
publisher = {Association for Computing Machinery (ACM)},
volume = {17},
number = {9},
pages = {530--531},
doi = {10.1145/361147.361115},
issn = {0001-0782, 1557-7317},
url = {https://doi.org/10.1145/361147.361115},
source = {Crossref},
}
@inproceedings{zheng2024sglang,
title = {SGLang: Efficient Execution of Structured Language Model Programs},
author = {Zheng, Lianmin and Yin, Liangsheng and Xie, Zhiqiang and others},
year = {2024},
journal = {arXiv preprint arXiv:2312.07104},
booktitle = {Advances in Neural Information Processing Systems 37},
publisher = {Neural Information Processing Systems Foundation, Inc. (NeurIPS)},
pages = {62557--62583},
doi = {10.52202/079017-2000},
url = {https://doi.org/10.52202/079017-2000},
note = {Introduces RadixAttention for automatic prompt caching via prefix tree matching},
source = {Crossref},
}
@inproceedings{zhong2024distserve,
title = {
{DistServe}: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model
Serving
},
author = {
Zhong, Yinmin and Liu, Shengyu and Chen, Junda and Hu, Jianbo and Zhu, Yibo and Liu, Xuanzhe
and Jin, Xin and Zhang, Hao
},
year = {2024},
month = jul,
booktitle = {18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24)},
publisher = {USENIX Association},
pages = {193--210},
isbn = {978-1-939133-40-3},
url = {https://www.usenix.org/conference/osdi24/presentation/zhong-yinmin},
}