Dan-Adrian Alistarh

Alistarh Group

141 Publications

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[141]
2024 | Published | Conference Paper | IST-REx-ID: 17093 | OA
Zakerinia, Hossein, Shayan Talaei, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Communication-Efficient Federated Learning with Data and Client Heterogeneity.” In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, 238:3448–56. ML Research Press, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[140]
2024 | Published | Conference Paper | IST-REx-ID: 17332 | OA
Kokorin, Ilya, Victor Yudov, Vitaly Aksenov, and Dan-Adrian Alistarh. “Wait-Free Trees with Asymptotically-Efficient Range Queries.” In 2024 IEEE International Parallel and Distributed Processing Symposium, 169–79. IEEE, 2024. https://doi.org/10.1109/IPDPS57955.2024.00023.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[139]
2024 | Published | Conference Paper | IST-REx-ID: 15011 | OA
Kurtic, Eldar, Torsten Hoefler, and Dan-Adrian Alistarh. “How to Prune Your Language Model: Recovering Accuracy on the ‘Sparsity May Cry’ Benchmark.” In Proceedings of Machine Learning Research, 234:542–53. ML Research Press, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[138]
2024 | Published | Conference Paper | IST-REx-ID: 18070
Chatterjee, Bapi, Vyacheslav Kungurtsev, and Dan-Adrian Alistarh. “Federated SGD with Local Asynchrony.” In Proceedings of the 44th International Conference on Distributed Computing Systems, 857–68. IEEE, 2024. https://doi.org/10.1109/ICDCS60910.2024.00084.
View | DOI
 
[137]
2024 | Published | Conference Paper | IST-REx-ID: 18113 | OA
Egiazarian, Vage, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, and Dan-Adrian Alistarh. “Extreme Compression of Large Language Models via Additive Quantization.” In Proceedings of the 41st International Conference on Machine Learning, 235:12284–303. ML Research Press, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[136]
2024 | Published | Conference Paper | IST-REx-ID: 18117 | OA
Nikdan, Mahdi, Soroush Tabesh, Elvir Crncevic, and Dan-Adrian Alistarh. “RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.” In Proceedings of the 41st International Conference on Machine Learning, 235:38187–206. ML Research Press, 2024.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[135]
2024 | Published | Conference Paper | IST-REx-ID: 18975 | OA
Modoranu, Ionut-Vlad, Aleksei Kalinov, Eldar Kurtic, Elias Frantar, and Dan-Adrian Alistarh. “Error Feedback Can Accurately Compress Preconditioners.” In 41st International Conference on Machine Learning, 235:35910–33. ML Research Press, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[134]
2024 | Published | Conference Paper | IST-REx-ID: 18977 | OA
Dettmers, Tim, Ruslan A. Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, and Dan-Adrian Alistarh. “SpQR: A Sparse-Quantized Representation for near-Lossless LLM Weight Compression.” In 12th International Conference on Learning Representations. OpenReview, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[133]
2024 | Published | Conference Paper | IST-REx-ID: 19519 | OA
Malinovskii, Vladimir, PV-tuning: Beyond straight-through estimation for extreme LLM compression. 38th Conference on Neural Information Processing Systems 37. 2024
[Published Version] View | Files available | arXiv
 
[132]
2024 | Published | Conference Paper | IST-REx-ID: 19511 | OA
Ashkboos, Saleh, QuaRot: Outlier-free 4-bit inference in rotated LLMs. 38th Conference on Neural Information Processing Systems 37. 2024
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[131]
2024 | Published | Conference Paper | IST-REx-ID: 18061 | OA
Frantar, Elias, and Dan-Adrian Alistarh. “QMoE: Sub-1-Bit Compression of Trillion Parameter Models.” In Proceedings of Machine Learning and Systems, edited by P. Gibbons, G. Pekhimenko, and C. De Sa, Vol. 6, 2024.
[Published Version] View | Files available | Download Published Version (ext.)
 
[130]
2024 | Published | Conference Paper | IST-REx-ID: 18062 | OA
Frantar, Elias, Carlos Riquelme Ruiz, Neil Houlsby, Dan-Adrian Alistarh, and Utku Evci. “Scaling Laws for Sparsely-Connected Foundation Models.” In The Twelfth International Conference on Learning Representations, 2024.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[129]
2024 | Published | Conference Paper | IST-REx-ID: 17456 | OA
Markov, Ilia, Kaveh Alimohammadi, Elias Frantar, and Dan-Adrian Alistarh. “L-GreCo: Layerwise-Adaptive Gradient Compression for Efficient Data-Parallel Deep Learning.” In Proceedings of Machine Learning and Systems , edited by P. Gibbons, G. Pekhimenko, and C. De Sa, Vol. 6. Association for Computing Machinery, 2024.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[128]
2024 | Published | Conference Paper | IST-REx-ID: 17329 | OA
Alistarh, Dan-Adrian, Krishnendu Chatterjee, Mehrdad Karrabi, and John M Lazarsfeld. “Game Dynamics and Equilibrium Computation in the Population Protocol Model.” In Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing, 40–49. Association for Computing Machinery, 2024. https://doi.org/10.1145/3662158.3662768.
[Published Version] View | Files available | DOI
 
[127]
2024 | Published | Conference Paper | IST-REx-ID: 18976 | OA
Islamov, Rustem, Mher Safaryan, and Dan-Adrian Alistarh. “AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms.” In Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, 238:649–57. ML Research Press, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[126]
2024 | Published | Conference Paper | IST-REx-ID: 19518 | OA
Wu, Diyuan, The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information. 38th Conference on Neural Information Processing Systems 37. 2024
[Preprint] View | Download Preprint (ext.) | arXiv
 
[125]
2024 | Published | Conference Paper | IST-REx-ID: 19510 | OA
Modoranu, Ionut-Vlad, MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence. 38th Conference on Neural Information Processing Systems 37. 2024
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[124]
2024 | Published | Conference Paper | IST-REx-ID: 18121 | OA
Moakhar, Arshia Soltani, Eugenia B Iofinova, Elias Frantar, and Dan-Adrian Alistarh. “SPADE: Sparsity-Guided Debugging for Deep Neural Networks.” In Proceedings of the 41st International Conference on Machine Learning, 235:45955–87. ML Research Press, 2024.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[123]
2023 | Published | Journal Article | IST-REx-ID: 13179 | OA
Koval, Nikita, Dmitry Khalanskiy, and Dan-Adrian Alistarh. “CQS: A Formally-Verified Framework for Fair and Abortable Synchronization.” Proceedings of the ACM on Programming Languages. Association for Computing Machinery , 2023. https://doi.org/10.1145/3591230.
[Published Version] View | Files available | DOI
 
[122]
2023 | Published | Conference Paper | IST-REx-ID: 13262 | OA
Fedorov, Alexander, Diba Hashemi, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Provably-Efficient and Internally-Deterministic Parallel Union-Find.” In Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures, 261–71. Association for Computing Machinery, 2023. https://doi.org/10.1145/3558481.3591082.
[Published Version] View | Files available | DOI | arXiv
 
[121]
2023 | Published | Conference Paper | IST-REx-ID: 14260 | OA
Koval, Nikita, Alexander Fedorov, Maria Sokolova, Dmitry Tsitelov, and Dan-Adrian Alistarh. “Lincheck: A Practical Framework for Testing Concurrent Data Structures on JVM.” In 35th International Conference on Computer Aided Verification , 13964:156–69. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-37706-8_8.
[Published Version] View | Files available | DOI
 
[120]
2023 | Published | Journal Article | IST-REx-ID: 12330 | OA
Aksenov, Vitalii, Dan-Adrian Alistarh, Alexandra Drozdova, and Amirkeivan Mohtashami. “The Splay-List: A Distribution-Adaptive Concurrent Skip-List.” Distributed Computing. Springer Nature, 2023. https://doi.org/10.1007/s00446-022-00441-x.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[119]
2023 | Published | Conference Paper | IST-REx-ID: 12735 | OA
Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. “Fast and Scalable Channels in Kotlin Coroutines.” In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 107–18. Association for Computing Machinery, 2023. https://doi.org/10.1145/3572848.3577481.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[118]
2023 | Research Data Reference | IST-REx-ID: 14995 | OA
Koval, Nikita, Alexander Fedorov, Maria Sokolova, Dmitry Tsitelov, and Dan-Adrian Alistarh. “Lincheck: A Practical Framework for Testing Concurrent Data Structures on JVM.” Zenodo, 2023. https://doi.org/10.5281/ZENODO.7877757.
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[117]
2023 | Published | Conference Paper | IST-REx-ID: 14460 | OA
Nikdan, Mahdi, Tommaso Pegolotti, Eugenia B Iofinova, Eldar Kurtic, and Dan-Adrian Alistarh. “SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.” In Proceedings of the 40th International Conference on Machine Learning, 202:26215–27. ML Research Press, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[116]
2023 | Published | Conference Paper | IST-REx-ID: 17378 | OA
Frantar, Elias, Saleh Ashkboos, Torsten Hoefler, and Dan-Adrian Alistarh. “OPTQ: Accurate Post-Training Quantization for Generative Pre-Trained Transformers.” In 11th International Conference on Learning Representations . International Conference on Learning Representations, 2023.
[Published Version] View | Files available
 
[115]
2023 | Published | Conference Paper | IST-REx-ID: 14458 | OA
Frantar, Elias, and Dan-Adrian Alistarh. “SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.” In Proceedings of the 40th International Conference on Machine Learning, 202:10323–37. ML Research Press, 2023.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[114]
2023 | Published | Conference Paper | IST-REx-ID: 14461 | OA
Markov, Ilia, Adrian Vladu, Qi Guo, and Dan-Adrian Alistarh. “Quantized Distributed Training of Large Models with Convergence Guarantees.” In Proceedings of the 40th International Conference on Machine Learning, 202:24020–44. ML Research Press, 2023.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[113]
2023 | Published | Journal Article | IST-REx-ID: 14364 | OA
Alistarh, Dan-Adrian, James Aspnes, Faith Ellen, Rati Gelashvili, and Leqi Zhu. “Why Extension-Based Proofs Fail.” SIAM Journal on Computing. Society for Industrial and Applied Mathematics, 2023. https://doi.org/10.1137/20M1375851.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[112]
2023 | Published | Journal Article | IST-REx-ID: 12566 | OA
Alistarh, Dan-Adrian, Faith Ellen, and Joel Rybicki. “Wait-Free Approximate Agreement on Graphs.” Theoretical Computer Science. Elsevier, 2023. https://doi.org/10.1016/j.tcs.2023.113733.
[Published Version] View | Files available | DOI | WoS
 
[111]
2023 | Published | Conference Paper | IST-REx-ID: 13053 | OA
Krumes, Alexandra, Adrian Vladu, Eldar Kurtic, Christoph Lampert, and Dan-Adrian Alistarh. “CrAM: A Compression-Aware Minimizer.” In 11th International Conference on Learning Representations . OpenReview, 2023.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[110]
2023 | Published | Conference Paper | IST-REx-ID: 15363 | OA
Safaryan, Mher, Alexandra Krumes, and Dan-Adrian Alistarh. “Knowledge Distillation Performs Partial Variance Reduction.” In 36th Conference on Neural Information Processing Systems, Vol. 36, 2023.
[Published Version] View | Files available | arXiv
 
[109]
2023 | Published | Conference Paper | IST-REx-ID: 14771 | OA
Iofinova, Eugenia B, Alexandra Krumes, and Dan-Adrian Alistarh. “Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures.” In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 24364–73. IEEE, 2023. https://doi.org/10.1109/cvpr52729.2023.02334.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[108]
2022 | Published | Conference Paper | IST-REx-ID: 11181 | OA
Brown, Trevor A, William Sigouin, and Dan-Adrian Alistarh. “PathCAS: An Efficient Middle Ground for Concurrent Search Data Structures.” In Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 385–99. Association for Computing Machinery, 2022. https://doi.org/10.1145/3503221.3508410.
[Published Version] View | Files available | DOI | WoS
 
[107]
2022 | Published | Conference Paper | IST-REx-ID: 17088 | OA
Kurtic, Eldar, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, and Dan-Adrian Alistarh. “The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.” In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 4163–81. Association for Computational Linguistics, 2022. https://doi.org/10.18653/v1/2022.emnlp-main.279.
[Published Version] View | Files available | DOI | arXiv
 
[106]
2022 | Published | Conference Paper | IST-REx-ID: 11180 | OA
Postnikova, Anastasiia, Nikita Koval, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Multi-Queues Can Be State-of-the-Art Priority Schedulers.” In Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 353–67. Association for Computing Machinery, 2022. https://doi.org/10.1145/3503221.3508432.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[105]
2022 | Research Data Reference | IST-REx-ID: 13076 | OA
Postnikova, Anastasiia, Nikita Koval, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Multi-Queues Can Be State-of-the-Art Priority Schedulers.” Zenodo, 2022. https://doi.org/10.5281/ZENODO.5733408.
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[104]
2022 | Published | Conference Paper | IST-REx-ID: 11844 | OA
Alistarh, Dan-Adrian, Joel Rybicki, and Sasha Voitovych. “Near-Optimal Leader Election in Population Protocols on Graphs.” In Proceedings of the Annual ACM Symposium on Principles of Distributed Computing, 246–56. Association for Computing Machinery, 2022. https://doi.org/10.1145/3519270.3538435.
[Published Version] View | Files available | DOI | arXiv
 
[103]
2022 | Published | Conference Paper | IST-REx-ID: 17087 | OA
Frantar, Elias, Sidak Pal Singh, and Dan-Adrian Alistarh. “Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning.” In 36th Conference on Neural Information Processing Systems, Vol. 35. ML Research Press, 2022.
[Submitted Version] View | Files available | arXiv
 
[102]
2022 | Published | Conference Paper | IST-REx-ID: 17059 | OA
Frantar, Elias, and Dan-Adrian Alistarh. “SPDY: Accurate Pruning with Speedup Guarantees.” In 39th International Conference on Machine Learning, 162:6726–43. ML Research Press, 2022.
[Published Version] View | Files available | WoS
 
[101]
2022 | Published | Conference Paper | IST-REx-ID: 12780 | OA
Markov, Ilia, Hamidreza Ramezanikebrya, and Dan-Adrian Alistarh. “CGX: Adaptive System Support for Communication-Efficient Deep Learning.” In Proceedings of the 23rd ACM/IFIP International Middleware Conference, 241–54. Association for Computing Machinery, 2022. https://doi.org/10.1145/3528535.3565248.
[Published Version] View | Files available | DOI | arXiv
 
[100]
2022 | Published | Conference Paper | IST-REx-ID: 11184 | OA
Alistarh, Dan-Adrian, Rati Gelashvili, and Joel Rybicki. “Fast Graphical Population Protocols.” In 25th International Conference on Principles of Distributed Systems, edited by Quentin Bramas, Vincent Gramoli, and Alessia Milani, Vol. 217. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022. https://doi.org/10.4230/LIPIcs.OPODIS.2021.14.
[Published Version] View | Files available | DOI | arXiv
 
[99]
2022 | Published | Journal Article | IST-REx-ID: 8286 | OA
Alistarh, Dan-Adrian, Dynamic averaging load balancing on cycles. Algorithmica 84 (4). 2022
[Published Version] View | Files available | DOI | WoS | arXiv
 
[98]
2022 | Published | Conference Paper | IST-REx-ID: 12299 | OA
Iofinova, Eugenia B, Alexandra Krumes, Mark Kurtz, and Dan-Adrian Alistarh. “How Well Do Sparse ImageNet Models Transfer?” In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 12256–66. Institute of Electrical and Electronics Engineers, 2022. https://doi.org/10.1109/cvpr52688.2022.01195.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[97]
2021 | Published | Conference Paper | IST-REx-ID: 10853 | OA
Fedorov, Alexander, Nikita Koval, and Dan-Adrian Alistarh. “A Scalable Concurrent Algorithm for Dynamic Connectivity.” In Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures, 208–20. Association for Computing Machinery, 2021. https://doi.org/10.1145/3409964.3461810.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[96]
2021 | Published | Journal Article | IST-REx-ID: 10180 | OA
Hoefler, Torsten, Dan-Adrian Alistarh, Tal Ben-Nun, Nikoli Dryden, and Elena-Alexandra Peste. “Sparsity in Deep Learning: Pruning and Growth for Efficient Inference and Training in Neural Networks.” Journal of Machine Learning Research. Journal of Machine Learning Research, 2021.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[95]
2021 | Published | Conference Paper | IST-REx-ID: 9823 | OA
Alistarh, Dan-Adrian, Faith Ellen, and Joel Rybicki. “Wait-Free Approximate Agreement on Graphs.” In Structural Information and Communication Complexity, 12810:87–105. Springer Nature, 2021. https://doi.org/10.1007/978-3-030-79527-6_6.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[94]
2021 | Published | Conference Paper | IST-REx-ID: 9951
Alistarh, Dan-Adrian, Martin Töpfer, and Przemysław Uznański. “Comparison Dynamics in Population Protocols.” In Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, 55–65. Association for Computing Machinery, 2021. https://doi.org/10.1145/3465084.3467915.
View | DOI | WoS
 
[93]
2021 | Published | Journal Article | IST-REx-ID: 9571 | OA
Ramezani-Kebrya, Ali, NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization. Journal of Machine Learning Research 22 (114). 2021
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[92]
2021 | Published | Conference Paper | IST-REx-ID: 10218 | OA
Alistarh, Dan-Adrian, Rati Gelashvili, and Joel Rybicki. “Brief Announcement: Fast Graphical Population Protocols.” In 35th International Symposium on Distributed Computing, Vol. 209. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021. https://doi.org/10.4230/LIPIcs.DISC.2021.43.
[Published Version] View | Files available | DOI | arXiv
 
[91]
2021 | Published | Conference Paper | IST-REx-ID: 11463 | OA
Frantar, Elias, M-FAC: Efficient matrix-free approximations of second-order information. 35th Conference on Neural Information Processing Systems 34. 2021
[Published Version] View | Download Published Version (ext.) | arXiv
 
[90]
2021 | Published | Conference Paper | IST-REx-ID: 10217 | OA
Alistarh, Dan-Adrian, Lower bounds for shared-memory leader election under bounded write contention. 35th International Symposium on Distributed Computing 209. 2021
[Published Version] View | Files available | DOI
 
[89]
2021 | Published | Conference Paper | IST-REx-ID: 11464 | OA
Alistarh, Dan-Adrian, Towards tight communication lower bounds for distributed optimisation. 35th Conference on Neural Information Processing Systems 34. 2021
[Published Version] View | Download Published Version (ext.) | arXiv
 
[88]
2021 | Published | Journal Article | IST-REx-ID: 8723 | OA
Li, Shigang, Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging. IEEE Transactions on Parallel and Distributed Systems 32 (7). 2021
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[87]
2021 | Published | Conference Paper | IST-REx-ID: 9620 | OA
Alistarh, Dan-Adrian, Collecting coupons is faster with friends. Structural Information and Communication Complexity 12810. 2021
[Preprint] View | Files available | DOI
 
[86]
2021 | Published | Conference Paper | IST-REx-ID: 9543 | OA
Davies, Peter, New bounds for distributed mean estimation and variance reduction. 9th International Conference on Learning Representations. 2021
[Published Version] View | Download Published Version (ext.) | arXiv
 
[85]
2021 | Published | Conference Paper | IST-REx-ID: 13147 | OA
Alimisis, Foivos, Communication-efficient distributed optimization with quantized preconditioners. Proceedings of the 38th International Conference on Machine Learning 139. 2021
[Published Version] View | Files available | arXiv
 
[84]
2021 | Published | Conference Paper | IST-REx-ID: 11436 | OA
Kungurtsev, Vyacheslav, Malcolm Egan, Bapi Chatterjee, and Dan-Adrian Alistarh. “Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees.” In 35th AAAI Conference on Artificial Intelligence, AAAI 2021, 35:8209–16. AAAI Press, 2021.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[83]
2021 | Published | Conference Paper | IST-REx-ID: 10435 | OA
Nadiradze, Giorgi, Amirmojtaba Sabour, Peter Davies, Shigang Li, and Dan-Adrian Alistarh. “Asynchronous Decentralized SGD with Quantized and Local Updates.” In 35th Conference on Neural Information Processing Systems. Neural Information Processing Systems Foundation, 2021.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[82]
2021 | Published | Conference Paper | IST-REx-ID: 11452 | OA
Alimisis, Foivos, Peter Davies, Bart Vandereycken, and Dan-Adrian Alistarh. “Distributed Principal Component Analysis with Limited Communication.” In Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, 4:2823–34. Neural Information Processing Systems Foundation, 2021.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[81]
2021 | Published | Conference Paper | IST-REx-ID: 10432 | OA
Nadiradze, Giorgi, Ilia Markov, Bapi Chatterjee, Vyacheslav Kungurtsev, and Dan-Adrian Alistarh. “Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent.” In Proceedings of the AAAI Conference on Artificial Intelligence, 35:9037–45, 2021.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[80]
2021 | Published | Conference Paper | IST-REx-ID: 11458 | OA
Krumes, Alexandra, Eugenia B Iofinova, Adrian Vladu, and Dan-Adrian Alistarh. “AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks.” In 35th Conference on Neural Information Processing Systems, 34:8557–70. Neural Information Processing Systems Foundation, 2021.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[79]
2020 | Published | Conference Paper | IST-REx-ID: 7635
Koval, Nikita, Mariia Sokolova, Alexander Fedorov, Dan-Adrian Alistarh, and Dmitry Tsitelov. “Testing Concurrency on the JVM with Lincheck.” In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, 423–24. Association for Computing Machinery, 2020. https://doi.org/10.1145/3332466.3374503.
View | DOI
 
[78]
2020 | Published | Conference Paper | IST-REx-ID: 8191
Alistarh, Dan-Adrian, Trevor A Brown, and Nandini Singhal. “Memory Tagging: Minimalist Synchronization for Scalable Concurrent Data Structures.” In Annual ACM Symposium on Parallelism in Algorithms and Architectures, 37–49. Association for Computing Machinery, 2020. https://doi.org/10.1145/3350755.3400213.
View | DOI | WoS
 
[77]
2020 | Published | Conference Paper | IST-REx-ID: 8383
Alistarh, Dan-Adrian, James Aspnes, Faith Ellen, Rati Gelashvili, and Leqi Zhu. “Brief Announcement: Why Extension-Based Proofs Fail.” In Proceedings of the 39th Symposium on Principles of Distributed Computing, 54–56. Association for Computing Machinery, 2020. https://doi.org/10.1145/3382734.3405743.
View | DOI
 
[76]
2020 | Conference Paper | IST-REx-ID: 9415 | OA
Kurtz, Mark, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William Leiserson, et al. “Inducing and Exploiting Activation Sparsity for Fast Neural Network Inference.” In 37th International Conference on Machine Learning, ICML 2020, 119:5533–43, 2020.
[Published Version] View | Files available
 
[75]
2020 | Published | Conference Paper | IST-REx-ID: 7605 | OA
Alistarh, Dan-Adrian, In search of the fastest concurrent union-find algorithm. 23rd International Conference on Principles of Distributed Systems 153. 2020
[Published Version] View | Files available | DOI | arXiv
 
[74]
2020 | Published | Journal Article | IST-REx-ID: 8268 | OA
Gurel, Nezihe Merve, Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications. IEEE Transactions on Signal Processing 68. 2020
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[73]
2020 | Published | Conference Paper | IST-REx-ID: 8725 | OA
Aksenov, Vitaly, Dan-Adrian Alistarh, Alexandra Drozdova, and Amirkeivan Mohtashami. “The Splay-List: A Distribution-Adaptive Concurrent Skip-List.” In 34th International Symposium on Distributed Computing, 179:3:1-3:18. LIPIcs. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020. https://doi.org/10.4230/LIPIcs.DISC.2020.3.
[Published Version] View | Files available | DOI | arXiv
 
[72]
2020 | Published | Conference Paper | IST-REx-ID: 8722 | OA
Li, Shigang, Tal Ben-Nun Tal Ben-Nun, Salvatore Di Girolamo, Dan-Adrian Alistarh, and Torsten Hoefler. “Taming Unbalanced Training Workloads in Deep Learning with Partial Collective Operations.” In Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 45–61. Association for Computing Machinery, 2020. https://doi.org/10.1145/3332466.3374528.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[71]
2020 | Published | Conference Paper | IST-REx-ID: 7636 | OA
Brown, Trevor A, Aleksandar Prokopec, and Dan-Adrian Alistarh. “Non-Blocking Interpolation Search Trees with Doubly-Logarithmic Running Time.” In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 276–91. Association for Computing Machinery, 2020. https://doi.org/10.1145/3332466.3374542.
[Published Version] View | DOI | Download Published Version (ext.) | WoS
 
[70]
2020 | Published | Conference Paper | IST-REx-ID: 15086 | OA
Faghri, Fartash , Iman Tabrizian, Ilia Markov, Dan-Adrian Alistarh, Daniel Roy, and Ali Ramezani-Kebrya. “Adaptive Gradient Quantization for Data-Parallel SGD.” In Advances in Neural Information Processing Systems, Vol. 33. Neural Information Processing Systems Foundation, 2020.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[69]
2020 | Published | Conference Paper | IST-REx-ID: 9632 | OA
Singh, Sidak Pal, WoodFisher: Efficient second-order approximation for neural network compression. 33. 2020
[Published Version] View | Download Published Version (ext.) | arXiv
 
[68]
2020 | Published | Conference Paper | IST-REx-ID: 9631 | OA
Aksenov, Vitaly, Scalable belief propagation via relaxed scheduling. 33. 2020
[Published Version] View | Download Published Version (ext.) | arXiv
 
[67]
2020 | Published | Conference Paper | IST-REx-ID: 8724 | OA
Konstantinov, Nikola H, Elias Frantar, Dan-Adrian Alistarh, and Christoph Lampert. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” In Proceedings of the 37th International Conference on Machine Learning, 119:5416–25. ML Research Press, 2020.
[Published Version] View | Files available | arXiv
 
[66]
2020 | Published | Conference Paper | IST-REx-ID: 15077 | OA
Alistarh, Dan-Adrian, Dynamic averaging load balancing on cycles. 47th International Colloquium on Automata, Languages, and Programming 168. 2020
[Published Version] View | Files available | DOI | arXiv
 
[65]
2019 | Published | Conference Paper | IST-REx-ID: 7437 | OA
Yu, Chen, Hanlin Tang, Cedric Renggli, Simon Kassing, Ankit Singla, Dan-Adrian Alistarh, Ce Zhang, and Ji Liu. “Distributed Learning over Unreliable Networks.” In 36th International Conference on Machine Learning, ICML 2019, 2019–June:12481–512. IMLS, 2019.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
[64]
2019 | Published | Conference Paper | IST-REx-ID: 7122
Khirirat, Sarit, Mikael Johansson, and Dan-Adrian Alistarh. “Gradient Compression for Communication-Limited Convex Optimization.” In 2018 IEEE Conference on Decision and Control. IEEE, 2019. https://doi.org/10.1109/cdc.2018.8619625.
View | DOI | WoS
 
[63]
2019 | Published | Conference Paper | IST-REx-ID: 7228
Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. “Scalable FIFO Channels for Programming via Communicating Sequential Processes.” In 25th Anniversary of Euro-Par, 11725:317–33. Springer Nature, 2019. https://doi.org/10.1007/978-3-030-29400-7_23.
View | DOI | WoS
 
[62]
2019 | Published | Conference Poster | IST-REx-ID: 6485
Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. Lock-Free Channels for Programming via Communicating Sequential Processes. Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming. ACM, 2019. https://doi.org/10.1145/3293883.3297000.
View | DOI | WoS
 
[61]
2019 | Published | Conference Paper | IST-REx-ID: 7542 | OA
Wendler, Chris, Dan-Adrian Alistarh, and Markus Püschel. “Powerset Convolutional Neural Networks,” 32:927–38. Neural Information Processing Systems Foundation, 2019.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 
[60]
2019 | Published | Conference Paper | IST-REx-ID: 6676 | OA
Alistarh, Dan-Adrian, Why extension-based proofs fail. Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing. 2019
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[59]
2019 | Published | Conference Paper | IST-REx-ID: 6673 | OA
Alistarh, Dan-Adrian, Efficiency guarantees for parallel incremental algorithms under relaxed schedulers. 31st ACM Symposium on Parallelism in Algorithms and Architectures. 2019
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[58]
2019 | Published | Conference Paper | IST-REx-ID: 7201 | OA
Renggli, Cedric, SparCML: High-performance sparse communication for machine learning. International Conference for High Performance Computing, Networking, Storage and Analysis, SC. 2019
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[57]
2018 | Published | Journal Article | IST-REx-ID: 6001
Alistarh, Dan-Adrian, William Leiserson, Alexander Matveev, and Nir Shavit. “ThreadScan: Automatic and Scalable Memory Reclamation.” ACM Transactions on Parallel Computing. Association for Computing Machinery, 2018. https://doi.org/10.1145/3201897.
View | Files available | DOI
 
[56]
2018 | Published | Conference Paper | IST-REx-ID: 6031
Stojanov, Alen, Tyler Michael Smith, Dan-Adrian Alistarh, and Markus Puschel. “Fast Quantized Arithmetic on X86: Trading Compute for Data Movement.” In 2018 IEEE International Workshop on Signal Processing Systems, Vol. 2018–October. IEEE, 2018. https://doi.org/10.1109/SiPS.2018.8598402.
View | DOI | WoS
 
[55]
2018 | Published | Conference Paper | IST-REx-ID: 7812 | OA
Polino, Antonio, Razvan Pascanu, and Dan-Adrian Alistarh. “Model Compression via Distillation and Quantization.” In 6th International Conference on Learning Representations, 2018.
[Published Version] View | Files available | arXiv
 
[54]
2018 | Published | Conference Paper | IST-REx-ID: 6558 | OA
Alistarh, Dan-Adrian, Zeyuan Allen-Zhu, and Jerry Li. “Byzantine Stochastic Gradient Descent.” In Advances in Neural Information Processing Systems, 2018:4613–23. Neural Information Processing Systems Foundation, 2018.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 
[53]
2018 | Published | Conference Paper | IST-REx-ID: 7116 | OA
Grubic, Demjan, Leo Tam, Dan-Adrian Alistarh, and Ce Zhang. “Synchronous Multi-GPU Training for Deep Learning with Low-Precision Communications: An Empirical Study.” In Proceedings of the 21st International Conference on Extending Database Technology, 145–56. OpenProceedings, 2018. https://doi.org/10.5441/002/EDBT.2018.14.
[Published Version] View | Files available | DOI
 
[52]
2018 | Published | Conference Paper | IST-REx-ID: 7123 | OA
Alistarh, Dan-Adrian, James Aspnes, and Rati Gelashvili. “Space-Optimal Majority in Population Protocols.” In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, 2221–39. ACM, 2018. https://doi.org/10.1137/1.9781611975031.144.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[51]
2018 | Published | Conference Paper | IST-REx-ID: 5963 | OA
Alistarh, Dan-Adrian, Relaxed schedulers can efficiently parallelize iterative algorithms. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC '18. 2018
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[50]
2018 | Published | Conference Paper | IST-REx-ID: 5962 | OA
Alistarh, Dan-Adrian, The convergence of stochastic gradient descent in asynchronous shared memory. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC '18. 2018
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[49]
2018 | Published | Conference Paper | IST-REx-ID: 5961
Alistarh, Dan-Adrian, A brief tutorial on distributed and concurrent machine learning. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC '18. 2018
View | DOI | WoS
 
[48]
2018 | Published | Conference Paper | IST-REx-ID: 5964 | OA
Aksenov, Vitaly, Brief Announcement: Performance prediction for coarse-grained locking. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC '18. 2018
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[47]
2018 | Published | Conference Paper | IST-REx-ID: 5966 | OA
Alistarh, Dan-Adrian, The transactional conflict problem. Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA '18. 2018
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[46]
2018 | Published | Conference Paper | IST-REx-ID: 5965 | OA
Alistarh, Dan-Adrian, Distributionally linearizable data structures. Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA '18. 2018
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[45]
2018 | Published | Conference Paper | IST-REx-ID: 6589 | OA
Alistarh, Dan-Adrian, Torsten Hoefler, Mikael Johansson, Nikola H Konstantinov, Sarit Khirirat, and Cedric Renggli. “The Convergence of Sparsified Gradient Methods.” In Advances in Neural Information Processing Systems 31, Volume 2018:5973–83. Neural Information Processing Systems Foundation, 2018.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
[44]
2018 | Published | Journal Article | IST-REx-ID: 536 | OA
Alistarh, Dan-Adrian, James Aspnes, Valerie King, and Jared Saia. “Communication-Efficient Randomized Consensus.” Distributed Computing. Springer, 2018. https://doi.org/10.1007/s00446-017-0315-1.
[Published Version] View | Files available | DOI
 
[43]
2017 | Published | Conference Paper | IST-REx-ID: 487
Baig, Ghufran, Bozidar Radunovic, Dan-Adrian Alistarh, Matthew Balkwill, Thomas Karagiannis, and Lili Qiu. “Towards Unlicensed Cellular Networks in TV White Spaces.” In Proceedings of the 2017 13th International Conference on Emerging Networking EXperiments and Technologies, 2–14. ACM, 2017. https://doi.org/10.1145/3143361.3143367.
View | DOI
 
[42]
2017 | Published | Conference Paper | IST-REx-ID: 787 | OA
Alistarh, Dan-Adrian, James Aspnes, David Eisenstat, Ronald Rivest, and Rati Gelashvili. “Time-Space Trade-Offs in Population Protocols,” 2560–79. SIAM, 2017. https://doi.org/doi.org/10.1137/1.9781611974782.169.
View | DOI | Download None (ext.)
 
[41]
2017 | Published | Conference Paper | IST-REx-ID: 788 | OA
Alistarh, Dan-Adrian, Bartłomiej Dudek, Adrian Kosowski, David Soloveichik, and Przemysław Uznański. “Robust Detection in Leak-Prone Population Protocols,” 10467 LNCS:155–71. Springer, 2017. https://doi.org/10.1007/978-3-319-66799-7_11.
View | DOI | Download None (ext.) | arXiv
 
[40]
2017 | Published | Conference Paper | IST-REx-ID: 789
Alistarh, Dan-Adrian, William Leiserson, Alexander Matveev, and Nir Shavit. “Forkscan: Conservative Memory Reclamation for Modern Operating Systems,” 483–98. ACM, 2017. https://doi.org/10.1145/3064176.3064214.
View | DOI
 
[39]
2017 | Published | Conference Paper | IST-REx-ID: 790
Kara, Kaan, Dan-Adrian Alistarh, Gustavo Alonso, Onur Mutlu, and Ce Zhang. “FPGA-Accelerated Dense Linear Machine Learning: A Precision-Convergence Trade-Off,” 160–67. IEEE, 2017. https://doi.org/10.1109/FCCM.2017.39.
View | DOI
 
[38]
2017 | Published | Conference Paper | IST-REx-ID: 791 | OA
Alistarh, Dan-Adrian, Justin Kopinsky, Jerry Li, and Giorgi Nadiradze. “The Power of Choice in Priority Scheduling.” In Proceedings of the ACM Symposium on Principles of Distributed Computing, Part F129314:283–92. ACM, 2017. https://doi.org/10.1145/3087801.3087810.
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[37]
2017 | Published | Conference Paper | IST-REx-ID: 432 | OA
Zhang, Hantian, ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning. Proceedings of Machine Learning Research 70. 2017
[Submitted Version] View | Files available
 
[36]
2017 | Published | Conference Paper | IST-REx-ID: 431 | OA
Alistarh, Dan-Adrian, QSGD: Communication-efficient SGD via gradient quantization and encoding. 2017. 2017
[Submitted Version] View | Download Submitted Version (ext.) | arXiv
 
[35]
2016 | Published | Conference Paper | IST-REx-ID: 785
Haider, Syed, William Hasenplaugh, and Dan-Adrian Alistarh. “Lease/Release: Architectural Support for Scaling Contended Data Structures,” Vol. 12-16-March-2016. ACM, 2016. https://doi.org/10.1145/2851141.2851155.
View | DOI
 
[34]
2016 | Published | Journal Article | IST-REx-ID: 786 | OA
Alistarh, Dan-Adrian, Keren Censor Hillel, and Nir Shavit. “Are Lock Free Concurrent Algorithms Practically Wait Free .” Journal of the ACM. ACM, 2016. https://doi.org/10.1145/2903136.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[33]
2015 | Published | Conference Paper | IST-REx-ID: 776
Alistarh, Dan-Adrian, Justin Kopinsky, Jerry Li, and Nir Shavit. “The SprayList: A Scalable Relaxed Priority Queue,” 2015–January:11–20. ACM, 2015. https://doi.org/10.1145/2688500.2688523.
View | DOI
 
[32]
2015 | Published | Conference Paper | IST-REx-ID: 777
Alistarh, Dan-Adrian, Jennifer Iglesias, and Milan Vojnović. “Streaming Min-Max Hypergraph Partitioning,” 2015–January:1900–1908. Neural Information Processing Systems, 2015.
View | Download None (ext.)
 
[31]
2015 | Published | Conference Paper | IST-REx-ID: 778 | OA
Alistarh, Dan-Adrian, Justin Kopinsky, Petr Kuznetsov, Srivatsan Ravi, and Nir Shavit. “Inherent Limitations of Hybrid Transactional Memory,” 9363:185–99. Springer, 2015. https://doi.org/10.1007/978-3-662-48653-5_13.
View | DOI | Download None (ext.) | arXiv
 
[30]
2015 | Published | Conference Paper | IST-REx-ID: 779
Alistarh, Dan-Adrian, Alexander Matveev, William Leiserson, and Nir Shavit. “ThreadScan: Automatic and Scalable Memory Reclamation,” 2015–June:123–32. ACM, 2015. https://doi.org/10.1145/2755573.2755600.
View | Files available | DOI
 
[29]
2015 | Published | Conference Paper | IST-REx-ID: 780 | OA
Alistarh, Dan-Adrian, and Rati Gelashvili. “Polylogarithmic-Time Leader Election in Population Protocols,” 9135:479–91. Springer, 2015. https://doi.org/10.1007/978-3-662-47666-6_38.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[28]
2015 | Published | Conference Paper | IST-REx-ID: 781
Alistarh, Dan-Adrian, Rati Gelashvili, and Milan Vojnović. “Fast and Exact Majority in Population Protocols,” 2015–July:47–56. ACM, 2015. https://doi.org/10.1145/2767386.2767429.
View | DOI
 
[27]
2015 | Published | Conference Paper | IST-REx-ID: 782
Alistarh, Dan-Adrian, Thomas Sauerwald, and Milan Vojnović. “Lock-Free Algorithms under Stochastic Schedulers,” 2015–July:251–60. ACM, 2015. https://doi.org/10.1145/2767386.2767430.
View | DOI
 
[26]
2015 | Published | Conference Paper | IST-REx-ID: 783 | OA
Alistarh, Dan-Adrian, Rati Gelashvili, and Adrian Vladu. “How to Elect a Leader Faster than a Tournament,” 2015–July:365–74. ACM, 2015. https://doi.org/10.1145/2767386.2767420.
View | DOI | Download None (ext.)
 
[25]
2015 | Published | Conference Paper | IST-REx-ID: 784
Alistarh, Dan-Adrian, Hitesh Ballani, Paolo Costa, Adam Funnell, Joshua Benjamin, Philip Watts, and Benn Thomsen. “A High-Radix, Low-Latency Optical Switch for Data Centers,” 367–68. ACM, 2015. https://doi.org/10.1145/2785956.2790035.
View | DOI
 
[24]
2014 | Published | Conference Paper | IST-REx-ID: 768
Alistarh, Dan-Adrian, James Aspnes, Michael Bender, Rati Gelashvili, and Seth Gilbert. “Dynamic Task Allocation in Asynchronous Shared Memory,” 416–35. SIAM, 2014. https://doi.org/10.1137/1.9781611973402.31.
View | DOI
 
[23]
2014 | Published | Journal Article | IST-REx-ID: 769
Alistarh, Dan-Adrian, James Aspnes, Keren Censor Hillel, Seth Gilbert, and Rachid Guerraoui. “Tight Bounds for Asynchronous Renaming.” Journal of the ACM. ACM, 2014. https://doi.org/10.1145/2597630.
View | DOI
 
[22]
2014 | Published | Conference Paper | IST-REx-ID: 770
Alistarh, Dan-Adrian, Patrick Eugster, Maurice Herlihy, Alexander Matveev, and Nir Shavit. “StackTrack: An Automated Transactional Approach to Concurrent Memory Reclamation.” ACM, 2014. https://doi.org/10.1145/2592798.2592808.
View | DOI
 
[21]
2014 | Published | Conference Paper | IST-REx-ID: 771
Alistarh, Dan-Adrian, Oksana Denysyuk, Luís Rodrígues, and Nir Shavit. “Balls-into-Leaves: Sub-Logarithmic Renaming in Synchronous Message-Passing Systems,” 232–41. ACM, 2014. https://doi.org/10.1145/2611462.2611499.
View | DOI
 
[20]
2014 | Published | Conference Paper | IST-REx-ID: 772 | OA
Alistarh, Dan-Adrian, Keren Censor Hillel, and Nir Shavit. “Are Lock-Free Concurrent Algorithms Practically Wait-Free?,” 714–23. ACM, 2014. https://doi.org/10.1145/2591796.2591836.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[19]
2014 | Published | Conference Paper | IST-REx-ID: 773
Alistarh, Dan-Adrian, James Aspnes, Valerie King, and Jared Saia. “Communication-Efficient Randomized Consensus.” edited by Fabian Kuhn, 8784:61–75. Springer, 2014. https://doi.org/10.1007/978-3-662-45174-8_5.
View | DOI
 
[18]
2014 | Published | Conference Paper | IST-REx-ID: 774
Alistarh, Dan-Adrian, Keren Censor Hille, and Nir Shavit. “Brief Announcement: Are Lock-Free Concurrent Algorithms Practically Wait-Free?,” 50–52. ACM, 2014. https://doi.org/10.1145/2611462.2611502.
View | DOI
 
[17]
2014 | Published | Conference Paper | IST-REx-ID: 775 | OA
Alistarh, Dan-Adrian, Justin Kopinsky, Alexander Matveev, and Nir Shavit. “The Levelarray: A Fast, Practical Long-Lived Renaming Algorithm,” 348–57. IEEE, 2014. https://doi.org/10.1109/ICDCS.2014.43.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[16]
2013 | Published | Conference Paper | IST-REx-ID: 765
Alistarh, Dan-Adrian, James Aspnes, George Giakkoupis, and Philipp Woelfel. “Randomized Loose Renaming in O(Loglogn) Time,” 200–209. ACM, 2013. https://doi.org/10.1145/2484239.2484240.
View | DOI
 
[15]
2012 | Published | Conference Paper | IST-REx-ID: 762
Alistarh, Dan-Adrian, Rachid Guerraoui, Petr Kuznetsov, and Giuliano Losa. “On the Cost of Composing Shared-Memory Algorithms,” 298–307. ACM, 2012. https://doi.org/10.1145/2312005.2312057.
View | DOI
 
[14]
2012 | Published | Conference Paper | IST-REx-ID: 763
Alistarh, Dan-Adrian, Hagit Attiya, Rachid Guerraoui, and Corentin Travers. “Early Deciding Synchronous Renaming in O(Log f) Rounds or Less,” 7355 LNCS:195–206. Springer, 2012. https://doi.org/10.1007/978-3-642-31104-8_17.
View | DOI
 
[13]
2012 | Published | Journal Article | IST-REx-ID: 764
Alistarh, Dan-Adrian, Seth Gilbert, Rachid Guerraoui, and Corentin Travers. “Of Choices, Failures and Asynchrony: The Many Faces of Set Agreement.” Algorithmica (New York). Springer, 2012. https://doi.org/10.1007/s00453-011-9581-7.
View | DOI
 
[12]
2012 | Published | Conference Paper | IST-REx-ID: 766
Alistarh, Dan-Adrian, Michael Bender, Seth Gilbert, and Rachid Guerraoui. “How to Allocate Tasks Asynchronously,” 331–40. IEEE, 2012. https://doi.org/10.1109/FOCS.2012.41.
View | DOI
 
[11]
2012 | Published | Journal Article | IST-REx-ID: 767
Alistarh, Dan-Adrian, Seth Gilbert, Rachid Guerraoui, and Corentin Travers. “Generating Fast Indulgent Algorithms.” Theory of Computing Systems. Elsevier, 2012. https://doi.org/10.1007/s00224-012-9407-2.
View | DOI
 
[10]
2011 | Published | Conference Paper | IST-REx-ID: 757
Alistarh, Dan-Adrian, Seth Gilbert, Rachid Guerraoui, and Corentin Travers. “Generating Fast Indulgent Algorithms,” 6522 LNCS:41–52. Springer, 2011. https://doi.org/10.1007/978-3-642-17679-1_4.
View | DOI
 
[9]
2011 | Published | Conference Paper | IST-REx-ID: 759
Alistarh, Dan-Adrian, James Aspnes, Seth Gilbert, and Rachid Guerraoui. “The Complexity of Renaming,” 718–27. IEEE, 2011. https://doi.org/10.1109/FOCS.2011.66.
View | DOI
 
[8]
2011 | Published | Conference Paper | IST-REx-ID: 760
Alistarh, Dan-Adrian, and James Aspnes. “Sub-Logarithmic Test-and-Set against a Weak Adversary,” 6950 LNCS:97–109. Springer, 2011. https://doi.org/10.1007/978-3-642-24100-0_7.
View | DOI
 
[7]
2011 | Published | Conference Paper | IST-REx-ID: 761
Alistarh, Dan-Adrian, James Aspnes, Keren Censor Hillel, Seth Gilbert, and Morteza Zadimoghaddam. “Optimal-Time Adaptive Strong Renaming, with Applications to Counting,” 239–48. ACM, 2011. https://doi.org/10.1145/1993806.1993850.
View | DOI
 
[6]
2010 | Published | Conference Paper | IST-REx-ID: 754
Alistarh, Dan-Adrian, Hagit Attiya, Seth Gilbert, Andrei Giurgiu, and Rachid Guerraoui. “Fast Randomized Test-and-Set and Renaming,” 6343 LNCS:94–108. Springer, 2010. https://doi.org/10.1007/978-3-642-15763-9_9.
View | DOI
 
[5]
2010 | Published | Conference Paper | IST-REx-ID: 755
Alistarh, Dan-Adrian, Seth Gilbert, Rachid Guerraoui, and Morteza Zadimoghaddam. “How Efficient Can Gossip Be? (On the Cost of Resilient Information Exchange),” 6199 LNCS:115–26. Springer, 2010. https://doi.org/10.1007/978-3-642-14162-1_10.
View | DOI
 
[4]
2010 | Published | Conference Paper | IST-REx-ID: 756
Alistarh, Dan-Adrian, Seth Gilbert, Rachid Guerraoui, Žarko Milošević, and Calvin Newport. “Securing Every Bit: Authenticated Broadcast in Radio Networks,” 50–59. ACM, 2010. https://doi.org/10.1145/1810479.1810489.
View | DOI
 
[3]
2010 | Published | Conference Paper | IST-REx-ID: 758
Alistarh, Dan-Adrian, Seth Gilbert, Rachid Guerraoui, and Corentin Travers. “Brief Announcement: New Bounds for Partially Synchronous Set Agreement,” 6343 LNCS:404–5. Springer, 2010. https://doi.org/10.1007/978-3-642-15763-9_40.
View | DOI
 
[2]
2009 | Published | Conference Paper | IST-REx-ID: 752
Alistarh, Dan-Adrian, Seth Gilbert, Rachid Guerraoui, and Corentin Travers. “Of Choices, Failures and Asynchrony: The Many Faces of Set Agreement,” 5878 LNCS:943–53. Springer, 2009. https://doi.org/10.1007/978-3-642-10631-6_95.
View | DOI
 
[1]
2008 | Published | Conference Paper | IST-REx-ID: 753
Alistarh, Dan-Adrian, Seth Gilbert, Rachid Guerraoui, and Corentin Travers. “How to Solve Consensus in the Smallest Window of Synchrony,” 5218 LNCS:32–46. Springer, 2008. https://doi.org/10.1007/978-3-540-87779-0_3.
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[141]
2024 | Published | Conference Paper | IST-REx-ID: 17093 | OA
Zakerinia, Hossein, Shayan Talaei, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Communication-Efficient Federated Learning with Data and Client Heterogeneity.” In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, 238:3448–56. ML Research Press, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[140]
2024 | Published | Conference Paper | IST-REx-ID: 17332 | OA
Kokorin, Ilya, Victor Yudov, Vitaly Aksenov, and Dan-Adrian Alistarh. “Wait-Free Trees with Asymptotically-Efficient Range Queries.” In 2024 IEEE International Parallel and Distributed Processing Symposium, 169–79. IEEE, 2024. https://doi.org/10.1109/IPDPS57955.2024.00023.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[139]
2024 | Published | Conference Paper | IST-REx-ID: 15011 | OA
Kurtic, Eldar, Torsten Hoefler, and Dan-Adrian Alistarh. “How to Prune Your Language Model: Recovering Accuracy on the ‘Sparsity May Cry’ Benchmark.” In Proceedings of Machine Learning Research, 234:542–53. ML Research Press, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[138]
2024 | Published | Conference Paper | IST-REx-ID: 18070
Chatterjee, Bapi, Vyacheslav Kungurtsev, and Dan-Adrian Alistarh. “Federated SGD with Local Asynchrony.” In Proceedings of the 44th International Conference on Distributed Computing Systems, 857–68. IEEE, 2024. https://doi.org/10.1109/ICDCS60910.2024.00084.
View | DOI
 
[137]
2024 | Published | Conference Paper | IST-REx-ID: 18113 | OA
Egiazarian, Vage, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, and Dan-Adrian Alistarh. “Extreme Compression of Large Language Models via Additive Quantization.” In Proceedings of the 41st International Conference on Machine Learning, 235:12284–303. ML Research Press, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[136]
2024 | Published | Conference Paper | IST-REx-ID: 18117 | OA
Nikdan, Mahdi, Soroush Tabesh, Elvir Crncevic, and Dan-Adrian Alistarh. “RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.” In Proceedings of the 41st International Conference on Machine Learning, 235:38187–206. ML Research Press, 2024.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[135]
2024 | Published | Conference Paper | IST-REx-ID: 18975 | OA
Modoranu, Ionut-Vlad, Aleksei Kalinov, Eldar Kurtic, Elias Frantar, and Dan-Adrian Alistarh. “Error Feedback Can Accurately Compress Preconditioners.” In 41st International Conference on Machine Learning, 235:35910–33. ML Research Press, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[134]
2024 | Published | Conference Paper | IST-REx-ID: 18977 | OA
Dettmers, Tim, Ruslan A. Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, and Dan-Adrian Alistarh. “SpQR: A Sparse-Quantized Representation for near-Lossless LLM Weight Compression.” In 12th International Conference on Learning Representations. OpenReview, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[133]
2024 | Published | Conference Paper | IST-REx-ID: 19519 | OA
Malinovskii, Vladimir, PV-tuning: Beyond straight-through estimation for extreme LLM compression. 38th Conference on Neural Information Processing Systems 37. 2024
[Published Version] View | Files available | arXiv
 
[132]
2024 | Published | Conference Paper | IST-REx-ID: 19511 | OA
Ashkboos, Saleh, QuaRot: Outlier-free 4-bit inference in rotated LLMs. 38th Conference on Neural Information Processing Systems 37. 2024
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[131]
2024 | Published | Conference Paper | IST-REx-ID: 18061 | OA
Frantar, Elias, and Dan-Adrian Alistarh. “QMoE: Sub-1-Bit Compression of Trillion Parameter Models.” In Proceedings of Machine Learning and Systems, edited by P. Gibbons, G. Pekhimenko, and C. De Sa, Vol. 6, 2024.
[Published Version] View | Files available | Download Published Version (ext.)
 
[130]
2024 | Published | Conference Paper | IST-REx-ID: 18062 | OA
Frantar, Elias, Carlos Riquelme Ruiz, Neil Houlsby, Dan-Adrian Alistarh, and Utku Evci. “Scaling Laws for Sparsely-Connected Foundation Models.” In The Twelfth International Conference on Learning Representations, 2024.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[129]
2024 | Published | Conference Paper | IST-REx-ID: 17456 | OA
Markov, Ilia, Kaveh Alimohammadi, Elias Frantar, and Dan-Adrian Alistarh. “L-GreCo: Layerwise-Adaptive Gradient Compression for Efficient Data-Parallel Deep Learning.” In Proceedings of Machine Learning and Systems , edited by P. Gibbons, G. Pekhimenko, and C. De Sa, Vol. 6. Association for Computing Machinery, 2024.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[128]
2024 | Published | Conference Paper | IST-REx-ID: 17329 | OA
Alistarh, Dan-Adrian, Krishnendu Chatterjee, Mehrdad Karrabi, and John M Lazarsfeld. “Game Dynamics and Equilibrium Computation in the Population Protocol Model.” In Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing, 40–49. Association for Computing Machinery, 2024. https://doi.org/10.1145/3662158.3662768.
[Published Version] View | Files available | DOI
 
[127]
2024 | Published | Conference Paper | IST-REx-ID: 18976 | OA
Islamov, Rustem, Mher Safaryan, and Dan-Adrian Alistarh. “AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms.” In Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, 238:649–57. ML Research Press, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[126]
2024 | Published | Conference Paper | IST-REx-ID: 19518 | OA
Wu, Diyuan, The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information. 38th Conference on Neural Information Processing Systems 37. 2024
[Preprint] View | Download Preprint (ext.) | arXiv
 
[125]
2024 | Published | Conference Paper | IST-REx-ID: 19510 | OA
Modoranu, Ionut-Vlad, MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence. 38th Conference on Neural Information Processing Systems 37. 2024
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[124]
2024 | Published | Conference Paper | IST-REx-ID: 18121 | OA
Moakhar, Arshia Soltani, Eugenia B Iofinova, Elias Frantar, and Dan-Adrian Alistarh. “SPADE: Sparsity-Guided Debugging for Deep Neural Networks.” In Proceedings of the 41st International Conference on Machine Learning, 235:45955–87. ML Research Press, 2024.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[123]
2023 | Published | Journal Article | IST-REx-ID: 13179 | OA
Koval, Nikita, Dmitry Khalanskiy, and Dan-Adrian Alistarh. “CQS: A Formally-Verified Framework for Fair and Abortable Synchronization.” Proceedings of the ACM on Programming Languages. Association for Computing Machinery , 2023. https://doi.org/10.1145/3591230.
[Published Version] View | Files available | DOI
 
[122]
2023 | Published | Conference Paper | IST-REx-ID: 13262 | OA
Fedorov, Alexander, Diba Hashemi, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Provably-Efficient and Internally-Deterministic Parallel Union-Find.” In Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures, 261–71. Association for Computing Machinery, 2023. https://doi.org/10.1145/3558481.3591082.
[Published Version] View | Files available | DOI | arXiv
 
[121]
2023 | Published | Conference Paper | IST-REx-ID: 14260 | OA
Koval, Nikita, Alexander Fedorov, Maria Sokolova, Dmitry Tsitelov, and Dan-Adrian Alistarh. “Lincheck: A Practical Framework for Testing Concurrent Data Structures on JVM.” In 35th International Conference on Computer Aided Verification , 13964:156–69. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-37706-8_8.
[Published Version] View | Files available | DOI
 
[120]
2023 | Published | Journal Article | IST-REx-ID: 12330 | OA
Aksenov, Vitalii, Dan-Adrian Alistarh, Alexandra Drozdova, and Amirkeivan Mohtashami. “The Splay-List: A Distribution-Adaptive Concurrent Skip-List.” Distributed Computing. Springer Nature, 2023. https://doi.org/10.1007/s00446-022-00441-x.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[119]
2023 | Published | Conference Paper | IST-REx-ID: 12735 | OA
Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. “Fast and Scalable Channels in Kotlin Coroutines.” In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 107–18. Association for Computing Machinery, 2023. https://doi.org/10.1145/3572848.3577481.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[118]
2023 | Research Data Reference | IST-REx-ID: 14995 | OA
Koval, Nikita, Alexander Fedorov, Maria Sokolova, Dmitry Tsitelov, and Dan-Adrian Alistarh. “Lincheck: A Practical Framework for Testing Concurrent Data Structures on JVM.” Zenodo, 2023. https://doi.org/10.5281/ZENODO.7877757.
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[117]
2023 | Published | Conference Paper | IST-REx-ID: 14460 | OA
Nikdan, Mahdi, Tommaso Pegolotti, Eugenia B Iofinova, Eldar Kurtic, and Dan-Adrian Alistarh. “SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.” In Proceedings of the 40th International Conference on Machine Learning, 202:26215–27. ML Research Press, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[116]
2023 | Published | Conference Paper | IST-REx-ID: 17378 | OA
Frantar, Elias, Saleh Ashkboos, Torsten Hoefler, and Dan-Adrian Alistarh. “OPTQ: Accurate Post-Training Quantization for Generative Pre-Trained Transformers.” In 11th International Conference on Learning Representations . International Conference on Learning Representations, 2023.
[Published Version] View | Files available
 
[115]
2023 | Published | Conference Paper | IST-REx-ID: 14458 | OA
Frantar, Elias, and Dan-Adrian Alistarh. “SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.” In Proceedings of the 40th International Conference on Machine Learning, 202:10323–37. ML Research Press, 2023.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[114]
2023 | Published | Conference Paper | IST-REx-ID: 14461 | OA
Markov, Ilia, Adrian Vladu, Qi Guo, and Dan-Adrian Alistarh. “Quantized Distributed Training of Large Models with Convergence Guarantees.” In Proceedings of the 40th International Conference on Machine Learning, 202:24020–44. ML Research Press, 2023.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[113]
2023 | Published | Journal Article | IST-REx-ID: 14364 | OA
Alistarh, Dan-Adrian, James Aspnes, Faith Ellen, Rati Gelashvili, and Leqi Zhu. “Why Extension-Based Proofs Fail.” SIAM Journal on Computing. Society for Industrial and Applied Mathematics, 2023. https://doi.org/10.1137/20M1375851.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[112]
2023 | Published | Journal Article | IST-REx-ID: 12566 | OA
Alistarh, Dan-Adrian, Faith Ellen, and Joel Rybicki. “Wait-Free Approximate Agreement on Graphs.” Theoretical Computer Science. Elsevier, 2023. https://doi.org/10.1016/j.tcs.2023.113733.
[Published Version] View | Files available | DOI | WoS
 
[111]
2023 | Published | Conference Paper | IST-REx-ID: 13053 | OA
Krumes, Alexandra, Adrian Vladu, Eldar Kurtic, Christoph Lampert, and Dan-Adrian Alistarh. “CrAM: A Compression-Aware Minimizer.” In 11th International Conference on Learning Representations . OpenReview, 2023.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[110]
2023 | Published | Conference Paper | IST-REx-ID: 15363 | OA
Safaryan, Mher, Alexandra Krumes, and Dan-Adrian Alistarh. “Knowledge Distillation Performs Partial Variance Reduction.” In 36th Conference on Neural Information Processing Systems, Vol. 36, 2023.
[Published Version] View | Files available | arXiv
 
[109]
2023 | Published | Conference Paper | IST-REx-ID: 14771 | OA
Iofinova, Eugenia B, Alexandra Krumes, and Dan-Adrian Alistarh. “Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures.” In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 24364–73. IEEE, 2023. https://doi.org/10.1109/cvpr52729.2023.02334.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[108]
2022 | Published | Conference Paper | IST-REx-ID: 11181 | OA
Brown, Trevor A, William Sigouin, and Dan-Adrian Alistarh. “PathCAS: An Efficient Middle Ground for Concurrent Search Data Structures.” In Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 385–99. Association for Computing Machinery, 2022. https://doi.org/10.1145/3503221.3508410.
[Published Version] View | Files available | DOI | WoS
 
[107]
2022 | Published | Conference Paper | IST-REx-ID: 17088 | OA
Kurtic, Eldar, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, and Dan-Adrian Alistarh. “The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.” In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 4163–81. Association for Computational Linguistics, 2022. https://doi.org/10.18653/v1/2022.emnlp-main.279.
[Published Version] View | Files available | DOI | arXiv
 
[106]
2022 | Published | Conference Paper | IST-REx-ID: 11180 | OA
Postnikova, Anastasiia, Nikita Koval, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Multi-Queues Can Be State-of-the-Art Priority Schedulers.” In Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 353–67. Association for Computing Machinery, 2022. https://doi.org/10.1145/3503221.3508432.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[105]
2022 | Research Data Reference | IST-REx-ID: 13076 | OA
Postnikova, Anastasiia, Nikita Koval, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Multi-Queues Can Be State-of-the-Art Priority Schedulers.” Zenodo, 2022. https://doi.org/10.5281/ZENODO.5733408.
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[104]
2022 | Published | Conference Paper | IST-REx-ID: 11844 | OA
Alistarh, Dan-Adrian, Joel Rybicki, and Sasha Voitovych. “Near-Optimal Leader Election in Population Protocols on Graphs.” In Proceedings of the Annual ACM Symposium on Principles of Distributed Computing, 246–56. Association for Computing Machinery, 2022. https://doi.org/10.1145/3519270.3538435.
[Published Version] View | Files available | DOI | arXiv
 
[103]
2022 | Published | Conference Paper | IST-REx-ID: 17087 | OA
Frantar, Elias, Sidak Pal Singh, and Dan-Adrian Alistarh. “Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning.” In 36th Conference on Neural Information Processing Systems, Vol. 35. ML Research Press, 2022.
[Submitted Version] View | Files available | arXiv
 
[102]
2022 | Published | Conference Paper | IST-REx-ID: 17059 | OA
Frantar, Elias, and Dan-Adrian Alistarh. “SPDY: Accurate Pruning with Speedup Guarantees.” In 39th International Conference on Machine Learning, 162:6726–43. ML Research Press, 2022.
[Published Version] View | Files available | WoS
 
[101]
2022 | Published | Conference Paper | IST-REx-ID: 12780 | OA
Markov, Ilia, Hamidreza Ramezanikebrya, and Dan-Adrian Alistarh. “CGX: Adaptive System Support for Communication-Efficient Deep Learning.” In Proceedings of the 23rd ACM/IFIP International Middleware Conference, 241–54. Association for Computing Machinery, 2022. https://doi.org/10.1145/3528535.3565248.
[Published Version] View | Files available | DOI | arXiv
 
[100]
2022 | Published | Conference Paper | IST-REx-ID: 11184 | OA
Alistarh, Dan-Adrian, Rati Gelashvili, and Joel Rybicki. “Fast Graphical Population Protocols.” In 25th International Conference on Principles of Distributed Systems, edited by Quentin Bramas, Vincent Gramoli, and Alessia Milani, Vol. 217. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022. https://doi.org/10.4230/LIPIcs.OPODIS.2021.14.
[Published Version] View | Files available | DOI | arXiv
 
[99]
2022 | Published | Journal Article | IST-REx-ID: 8286 | OA
Alistarh, Dan-Adrian, Dynamic averaging load balancing on cycles. Algorithmica 84 (4). 2022
[Published Version] View | Files available | DOI | WoS | arXiv
 
[98]
2022 | Published | Conference Paper | IST-REx-ID: 12299 | OA
Iofinova, Eugenia B, Alexandra Krumes, Mark Kurtz, and Dan-Adrian Alistarh. “How Well Do Sparse ImageNet Models Transfer?” In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 12256–66. Institute of Electrical and Electronics Engineers, 2022. https://doi.org/10.1109/cvpr52688.2022.01195.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[97]
2021 | Published | Conference Paper | IST-REx-ID: 10853 | OA
Fedorov, Alexander, Nikita Koval, and Dan-Adrian Alistarh. “A Scalable Concurrent Algorithm for Dynamic Connectivity.” In Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures, 208–20. Association for Computing Machinery, 2021. https://doi.org/10.1145/3409964.3461810.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[96]
2021 | Published | Journal Article | IST-REx-ID: 10180 | OA
Hoefler, Torsten, Dan-Adrian Alistarh, Tal Ben-Nun, Nikoli Dryden, and Elena-Alexandra Peste. “Sparsity in Deep Learning: Pruning and Growth for Efficient Inference and Training in Neural Networks.” Journal of Machine Learning Research. Journal of Machine Learning Research, 2021.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[95]
2021 | Published | Conference Paper | IST-REx-ID: 9823 | OA
Alistarh, Dan-Adrian, Faith Ellen, and Joel Rybicki. “Wait-Free Approximate Agreement on Graphs.” In Structural Information and Communication Complexity, 12810:87–105. Springer Nature, 2021. https://doi.org/10.1007/978-3-030-79527-6_6.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[94]
2021 | Published | Conference Paper | IST-REx-ID: 9951
Alistarh, Dan-Adrian, Martin Töpfer, and Przemysław Uznański. “Comparison Dynamics in Population Protocols.” In Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, 55–65. Association for Computing Machinery, 2021. https://doi.org/10.1145/3465084.3467915.
View | DOI | WoS
 
[93]
2021 | Published | Journal Article | IST-REx-ID: 9571 | OA
Ramezani-Kebrya, Ali, NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization. Journal of Machine Learning Research 22 (114). 2021
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[92]
2021 | Published | Conference Paper | IST-REx-ID: 10218 | OA
Alistarh, Dan-Adrian, Rati Gelashvili, and Joel Rybicki. “Brief Announcement: Fast Graphical Population Protocols.” In 35th International Symposium on Distributed Computing, Vol. 209. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021. https://doi.org/10.4230/LIPIcs.DISC.2021.43.
[Published Version] View | Files available | DOI | arXiv
 
[91]
2021 | Published | Conference Paper | IST-REx-ID: 11463 | OA
Frantar, Elias, M-FAC: Efficient matrix-free approximations of second-order information. 35th Conference on Neural Information Processing Systems 34. 2021
[Published Version] View | Download Published Version (ext.) | arXiv
 
[90]
2021 | Published | Conference Paper | IST-REx-ID: 10217 | OA
Alistarh, Dan-Adrian, Lower bounds for shared-memory leader election under bounded write contention. 35th International Symposium on Distributed Computing 209. 2021
[Published Version] View | Files available | DOI
 
[89]
2021 | Published | Conference Paper | IST-REx-ID: 11464 | OA
Alistarh, Dan-Adrian, Towards tight communication lower bounds for distributed optimisation. 35th Conference on Neural Information Processing Systems 34. 2021
[Published Version] View | Download Published Version (ext.) | arXiv
 
[88]
2021 | Published | Journal Article | IST-REx-ID: 8723 | OA
Li, Shigang, Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging. IEEE Transactions on Parallel and Distributed Systems 32 (7). 2021
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[87]
2021 | Published | Conference Paper | IST-REx-ID: 9620 | OA
Alistarh, Dan-Adrian, Collecting coupons is faster with friends. Structural Information and Communication Complexity 12810. 2021
[Preprint] View | Files available | DOI
 
[86]
2021 | Published | Conference Paper | IST-REx-ID: 9543 | OA
Davies, Peter, New bounds for distributed mean estimation and variance reduction. 9th International Conference on Learning Representations. 2021
[Published Version] View | Download Published Version (ext.) | arXiv
 
[85]
2021 | Published | Conference Paper | IST-REx-ID: 13147 | OA
Alimisis, Foivos, Communication-efficient distributed optimization with quantized preconditioners. Proceedings of the 38th International Conference on Machine Learning 139. 2021
[Published Version] View | Files available | arXiv
 
[84]
2021 | Published | Conference Paper | IST-REx-ID: 11436 | OA
Kungurtsev, Vyacheslav, Malcolm Egan, Bapi Chatterjee, and Dan-Adrian Alistarh. “Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees.” In 35th AAAI Conference on Artificial Intelligence, AAAI 2021, 35:8209–16. AAAI Press, 2021.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[83]
2021 | Published | Conference Paper | IST-REx-ID: 10435 | OA
Nadiradze, Giorgi, Amirmojtaba Sabour, Peter Davies, Shigang Li, and Dan-Adrian Alistarh. “Asynchronous Decentralized SGD with Quantized and Local Updates.” In 35th Conference on Neural Information Processing Systems. Neural Information Processing Systems Foundation, 2021.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[82]
2021 | Published | Conference Paper | IST-REx-ID: 11452 | OA
Alimisis, Foivos, Peter Davies, Bart Vandereycken, and Dan-Adrian Alistarh. “Distributed Principal Component Analysis with Limited Communication.” In Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, 4:2823–34. Neural Information Processing Systems Foundation, 2021.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[81]
2021 | Published | Conference Paper | IST-REx-ID: 10432 | OA
Nadiradze, Giorgi, Ilia Markov, Bapi Chatterjee, Vyacheslav Kungurtsev, and Dan-Adrian Alistarh. “Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent.” In Proceedings of the AAAI Conference on Artificial Intelligence, 35:9037–45, 2021.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[80]
2021 | Published | Conference Paper | IST-REx-ID: 11458 | OA
Krumes, Alexandra, Eugenia B Iofinova, Adrian Vladu, and Dan-Adrian Alistarh. “AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks.” In 35th Conference on Neural Information Processing Systems, 34:8557–70. Neural Information Processing Systems Foundation, 2021.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[79]
2020 | Published | Conference Paper | IST-REx-ID: 7635
Koval, Nikita, Mariia Sokolova, Alexander Fedorov, Dan-Adrian Alistarh, and Dmitry Tsitelov. “Testing Concurrency on the JVM with Lincheck.” In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, 423–24. Association for Computing Machinery, 2020. https://doi.org/10.1145/3332466.3374503.
View | DOI
 
[78]
2020 | Published | Conference Paper | IST-REx-ID: 8191
Alistarh, Dan-Adrian, Trevor A Brown, and Nandini Singhal. “Memory Tagging: Minimalist Synchronization for Scalable Concurrent Data Structures.” In Annual ACM Symposium on Parallelism in Algorithms and Architectures, 37–49. Association for Computing Machinery, 2020. https://doi.org/10.1145/3350755.3400213.
View | DOI | WoS
 
[77]
2020 | Published | Conference Paper | IST-REx-ID: 8383
Alistarh, Dan-Adrian, James Aspnes, Faith Ellen, Rati Gelashvili, and Leqi Zhu. “Brief Announcement: Why Extension-Based Proofs Fail.” In Proceedings of the 39th Symposium on Principles of Distributed Computing, 54–56. Association for Computing Machinery, 2020. https://doi.org/10.1145/3382734.3405743.
View | DOI
 
[76]
2020 | Conference Paper | IST-REx-ID: 9415 | OA
Kurtz, Mark, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William Leiserson, et al. “Inducing and Exploiting Activation Sparsity for Fast Neural Network Inference.” In 37th International Conference on Machine Learning, ICML 2020, 119:5533–43, 2020.
[Published Version] View | Files available
 
[75]
2020 | Published | Conference Paper | IST-REx-ID: 7605 | OA
Alistarh, Dan-Adrian, In search of the fastest concurrent union-find algorithm. 23rd International Conference on Principles of Distributed Systems 153. 2020
[Published Version] View | Files available | DOI | arXiv
 
[74]
2020 | Published | Journal Article | IST-REx-ID: 8268 | OA
Gurel, Nezihe Merve, Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications. IEEE Transactions on Signal Processing 68. 2020
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[73]
2020 | Published | Conference Paper | IST-REx-ID: 8725 | OA
Aksenov, Vitaly, Dan-Adrian Alistarh, Alexandra Drozdova, and Amirkeivan Mohtashami. “The Splay-List: A Distribution-Adaptive Concurrent Skip-List.” In 34th International Symposium on Distributed Computing, 179:3:1-3:18. LIPIcs. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020. https://doi.org/10.4230/LIPIcs.DISC.2020.3.
[Published Version] View | Files available | DOI | arXiv
 
[72]
2020 | Published | Conference Paper | IST-REx-ID: 8722 | OA
Li, Shigang, Tal Ben-Nun Tal Ben-Nun, Salvatore Di Girolamo, Dan-Adrian Alistarh, and Torsten Hoefler. “Taming Unbalanced Training Workloads in Deep Learning with Partial Collective Operations.” In Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 45–61. Association for Computing Machinery, 2020. https://doi.org/10.1145/3332466.3374528.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[71]
2020 | Published | Conference Paper | IST-REx-ID: 7636 | OA
Brown, Trevor A, Aleksandar Prokopec, and Dan-Adrian Alistarh. “Non-Blocking Interpolation Search Trees with Doubly-Logarithmic Running Time.” In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 276–91. Association for Computing Machinery, 2020. https://doi.org/10.1145/3332466.3374542.
[Published Version] View | DOI | Download Published Version (ext.) | WoS
 
[70]
2020 | Published | Conference Paper | IST-REx-ID: 15086 | OA
Faghri, Fartash , Iman Tabrizian, Ilia Markov, Dan-Adrian Alistarh, Daniel Roy, and Ali Ramezani-Kebrya. “Adaptive Gradient Quantization for Data-Parallel SGD.” In Advances in Neural Information Processing Systems, Vol. 33. Neural Information Processing Systems Foundation, 2020.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[69]
2020 | Published | Conference Paper | IST-REx-ID: 9632 | OA
Singh, Sidak Pal, WoodFisher: Efficient second-order approximation for neural network compression. 33. 2020
[Published Version] View | Download Published Version (ext.) | arXiv
 
[68]
2020 | Published | Conference Paper | IST-REx-ID: 9631 | OA
Aksenov, Vitaly, Scalable belief propagation via relaxed scheduling. 33. 2020
[Published Version] View | Download Published Version (ext.) | arXiv
 
[67]
2020 | Published | Conference Paper | IST-REx-ID: 8724 | OA
Konstantinov, Nikola H, Elias Frantar, Dan-Adrian Alistarh, and Christoph Lampert. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” In Proceedings of the 37th International Conference on Machine Learning, 119:5416–25. ML Research Press, 2020.
[Published Version] View | Files available | arXiv
 
[66]
2020 | Published | Conference Paper | IST-REx-ID: 15077 | OA
Alistarh, Dan-Adrian, Dynamic averaging load balancing on cycles. 47th International Colloquium on Automata, Languages, and Programming 168. 2020
[Published Version] View | Files available | DOI | arXiv
 
[65]
2019 | Published | Conference Paper | IST-REx-ID: 7437 | OA
Yu, Chen, Hanlin Tang, Cedric Renggli, Simon Kassing, Ankit Singla, Dan-Adrian Alistarh, Ce Zhang, and Ji Liu. “Distributed Learning over Unreliable Networks.” In 36th International Conference on Machine Learning, ICML 2019, 2019–June:12481–512. IMLS, 2019.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
[64]
2019 | Published | Conference Paper | IST-REx-ID: 7122
Khirirat, Sarit, Mikael Johansson, and Dan-Adrian Alistarh. “Gradient Compression for Communication-Limited Convex Optimization.” In 2018 IEEE Conference on Decision and Control. IEEE, 2019. https://doi.org/10.1109/cdc.2018.8619625.
View | DOI | WoS
 
[63]
2019 | Published | Conference Paper | IST-REx-ID: 7228
Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. “Scalable FIFO Channels for Programming via Communicating Sequential Processes.” In 25th Anniversary of Euro-Par, 11725:317–33. Springer Nature, 2019. https://doi.org/10.1007/978-3-030-29400-7_23.
View | DOI | WoS
 
[62]
2019 | Published | Conference Poster | IST-REx-ID: 6485
Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. Lock-Free Channels for Programming via Communicating Sequential Processes. Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming. ACM, 2019. https://doi.org/10.1145/3293883.3297000.
View | DOI | WoS
 
[61]
2019 | Published | Conference Paper | IST-REx-ID: 7542 | OA
Wendler, Chris, Dan-Adrian Alistarh, and Markus Püschel. “Powerset Convolutional Neural Networks,” 32:927–38. Neural Information Processing Systems Foundation, 2019.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 
[60]
2019 | Published | Conference Paper | IST-REx-ID: 6676 | OA
Alistarh, Dan-Adrian, Why extension-based proofs fail. Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing. 2019
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[59]
2019 | Published | Conference Paper | IST-REx-ID: 6673 | OA
Alistarh, Dan-Adrian, Efficiency guarantees for parallel incremental algorithms under relaxed schedulers. 31st ACM Symposium on Parallelism in Algorithms and Architectures. 2019
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[58]
2019 | Published | Conference Paper | IST-REx-ID: 7201 | OA
Renggli, Cedric, SparCML: High-performance sparse communication for machine learning. International Conference for High Performance Computing, Networking, Storage and Analysis, SC. 2019
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[57]
2018 | Published | Journal Article | IST-REx-ID: 6001
Alistarh, Dan-Adrian, William Leiserson, Alexander Matveev, and Nir Shavit. “ThreadScan: Automatic and Scalable Memory Reclamation.” ACM Transactions on Parallel Computing. Association for Computing Machinery, 2018. https://doi.org/10.1145/3201897.
View | Files available | DOI
 
[56]
2018 | Published | Conference Paper | IST-REx-ID: 6031
Stojanov, Alen, Tyler Michael Smith, Dan-Adrian Alistarh, and Markus Puschel. “Fast Quantized Arithmetic on X86: Trading Compute for Data Movement.” In 2018 IEEE International Workshop on Signal Processing Systems, Vol. 2018–October. IEEE, 2018. https://doi.org/10.1109/SiPS.2018.8598402.
View | DOI | WoS
 
[55]
2018 | Published | Conference Paper | IST-REx-ID: 7812 | OA
Polino, Antonio, Razvan Pascanu, and Dan-Adrian Alistarh. “Model Compression via Distillation and Quantization.” In 6th International Conference on Learning Representations, 2018.
[Published Version] View | Files available | arXiv
 
[54]
2018 | Published | Conference Paper | IST-REx-ID: 6558 | OA
Alistarh, Dan-Adrian, Zeyuan Allen-Zhu, and Jerry Li. “Byzantine Stochastic Gradient Descent.” In Advances in Neural Information Processing Systems, 2018:4613–23. Neural Information Processing Systems Foundation, 2018.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 
[53]
2018 | Published | Conference Paper | IST-REx-ID: 7116 | OA
Grubic, Demjan, Leo Tam, Dan-Adrian Alistarh, and Ce Zhang. “Synchronous Multi-GPU Training for Deep Learning with Low-Precision Communications: An Empirical Study.” In Proceedings of the 21st International Conference on Extending Database Technology, 145–56. OpenProceedings, 2018. https://doi.org/10.5441/002/EDBT.2018.14.
[Published Version] View | Files available | DOI
 
[52]
2018 | Published | Conference Paper | IST-REx-ID: 7123 | OA
Alistarh, Dan-Adrian, James Aspnes, and Rati Gelashvili. “Space-Optimal Majority in Population Protocols.” In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, 2221–39. ACM, 2018. https://doi.org/10.1137/1.9781611975031.144.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[51]
2018 | Published | Conference Paper | IST-REx-ID: 5963 | OA
Alistarh, Dan-Adrian, Relaxed schedulers can efficiently parallelize iterative algorithms. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC '18. 2018
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[50]
2018 | Published | Conference Paper | IST-REx-ID: 5962 | OA
Alistarh, Dan-Adrian, The convergence of stochastic gradient descent in asynchronous shared memory. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC '18. 2018
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[49]
2018 | Published | Conference Paper | IST-REx-ID: 5961
Alistarh, Dan-Adrian, A brief tutorial on distributed and concurrent machine learning. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC '18. 2018
View | DOI | WoS
 
[48]
2018 | Published | Conference Paper | IST-REx-ID: 5964 | OA
Aksenov, Vitaly, Brief Announcement: Performance prediction for coarse-grained locking. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC '18. 2018
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[47]
2018 | Published | Conference Paper | IST-REx-ID: 5966 | OA
Alistarh, Dan-Adrian, The transactional conflict problem. Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA '18. 2018
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[46]
2018 | Published | Conference Paper | IST-REx-ID: 5965 | OA
Alistarh, Dan-Adrian, Distributionally linearizable data structures. Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA '18. 2018
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[45]
2018 | Published | Conference Paper | IST-REx-ID: 6589 | OA
Alistarh, Dan-Adrian, Torsten Hoefler, Mikael Johansson, Nikola H Konstantinov, Sarit Khirirat, and Cedric Renggli. “The Convergence of Sparsified Gradient Methods.” In Advances in Neural Information Processing Systems 31, Volume 2018:5973–83. Neural Information Processing Systems Foundation, 2018.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
[44]
2018 | Published | Journal Article | IST-REx-ID: 536 | OA
Alistarh, Dan-Adrian, James Aspnes, Valerie King, and Jared Saia. “Communication-Efficient Randomized Consensus.” Distributed Computing. Springer, 2018. https://doi.org/10.1007/s00446-017-0315-1.
[Published Version] View | Files available | DOI
 
[43]
2017 | Published | Conference Paper | IST-REx-ID: 487
Baig, Ghufran, Bozidar Radunovic, Dan-Adrian Alistarh, Matthew Balkwill, Thomas Karagiannis, and Lili Qiu. “Towards Unlicensed Cellular Networks in TV White Spaces.” In Proceedings of the 2017 13th International Conference on Emerging Networking EXperiments and Technologies, 2–14. ACM, 2017. https://doi.org/10.1145/3143361.3143367.
View | DOI
 
[42]
2017 | Published | Conference Paper | IST-REx-ID: 787 | OA
Alistarh, Dan-Adrian, James Aspnes, David Eisenstat, Ronald Rivest, and Rati Gelashvili. “Time-Space Trade-Offs in Population Protocols,” 2560–79. SIAM, 2017. https://doi.org/doi.org/10.1137/1.9781611974782.169.
View | DOI | Download None (ext.)
 
[41]
2017 | Published | Conference Paper | IST-REx-ID: 788 | OA
Alistarh, Dan-Adrian, Bartłomiej Dudek, Adrian Kosowski, David Soloveichik, and Przemysław Uznański. “Robust Detection in Leak-Prone Population Protocols,” 10467 LNCS:155–71. Springer, 2017. https://doi.org/10.1007/978-3-319-66799-7_11.
View | DOI | Download None (ext.) | arXiv
 
[40]
2017 | Published | Conference Paper | IST-REx-ID: 789
Alistarh, Dan-Adrian, William Leiserson, Alexander Matveev, and Nir Shavit. “Forkscan: Conservative Memory Reclamation for Modern Operating Systems,” 483–98. ACM, 2017. https://doi.org/10.1145/3064176.3064214.
View | DOI
 
[39]
2017 | Published | Conference Paper | IST-REx-ID: 790
Kara, Kaan, Dan-Adrian Alistarh, Gustavo Alonso, Onur Mutlu, and Ce Zhang. “FPGA-Accelerated Dense Linear Machine Learning: A Precision-Convergence Trade-Off,” 160–67. IEEE, 2017. https://doi.org/10.1109/FCCM.2017.39.
View | DOI
 
[38]
2017 | Published | Conference Paper | IST-REx-ID: 791 | OA
Alistarh, Dan-Adrian, Justin Kopinsky, Jerry Li, and Giorgi Nadiradze. “The Power of Choice in Priority Scheduling.” In Proceedings of the ACM Symposium on Principles of Distributed Computing, Part F129314:283–92. ACM, 2017. https://doi.org/10.1145/3087801.3087810.
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[37]
2017 | Published | Conference Paper | IST-REx-ID: 432 | OA
Zhang, Hantian, ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning. Proceedings of Machine Learning Research 70. 2017
[Submitted Version] View | Files available
 
[36]
2017 | Published | Conference Paper | IST-REx-ID: 431 | OA
Alistarh, Dan-Adrian, QSGD: Communication-efficient SGD via gradient quantization and encoding. 2017. 2017
[Submitted Version] View | Download Submitted Version (ext.) | arXiv
 
[35]
2016 | Published | Conference Paper | IST-REx-ID: 785
Haider, Syed, William Hasenplaugh, and Dan-Adrian Alistarh. “Lease/Release: Architectural Support for Scaling Contended Data Structures,” Vol. 12-16-March-2016. ACM, 2016. https://doi.org/10.1145/2851141.2851155.
View | DOI
 
[34]
2016 | Published | Journal Article | IST-REx-ID: 786 | OA
Alistarh, Dan-Adrian, Keren Censor Hillel, and Nir Shavit. “Are Lock Free Concurrent Algorithms Practically Wait Free .” Journal of the ACM. ACM, 2016. https://doi.org/10.1145/2903136.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[33]
2015 | Published | Conference Paper | IST-REx-ID: 776
Alistarh, Dan-Adrian, Justin Kopinsky, Jerry Li, and Nir Shavit. “The SprayList: A Scalable Relaxed Priority Queue,” 2015–January:11–20. ACM, 2015. https://doi.org/10.1145/2688500.2688523.
View | DOI
 
[32]
2015 | Published | Conference Paper | IST-REx-ID: 777
Alistarh, Dan-Adrian, Jennifer Iglesias, and Milan Vojnović. “Streaming Min-Max Hypergraph Partitioning,” 2015–January:1900–1908. Neural Information Processing Systems, 2015.
View | Download None (ext.)
 
[31]
2015 | Published | Conference Paper | IST-REx-ID: 778 | OA
Alistarh, Dan-Adrian, Justin Kopinsky, Petr Kuznetsov, Srivatsan Ravi, and Nir Shavit. “Inherent Limitations of Hybrid Transactional Memory,” 9363:185–99. Springer, 2015. https://doi.org/10.1007/978-3-662-48653-5_13.
View | DOI | Download None (ext.) | arXiv
 
[30]
2015 | Published | Conference Paper | IST-REx-ID: 779
Alistarh, Dan-Adrian, Alexander Matveev, William Leiserson, and Nir Shavit. “ThreadScan: Automatic and Scalable Memory Reclamation,” 2015–June:123–32. ACM, 2015. https://doi.org/10.1145/2755573.2755600.
View | Files available | DOI
 
[29]
2015 | Published | Conference Paper | IST-REx-ID: 780 | OA
Alistarh, Dan-Adrian, and Rati Gelashvili. “Polylogarithmic-Time Leader Election in Population Protocols,” 9135:479–91. Springer, 2015. https://doi.org/10.1007/978-3-662-47666-6_38.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[28]
2015 | Published | Conference Paper | IST-REx-ID: 781
Alistarh, Dan-Adrian, Rati Gelashvili, and Milan Vojnović. “Fast and Exact Majority in Population Protocols,” 2015–July:47–56. ACM, 2015. https://doi.org/10.1145/2767386.2767429.
View | DOI
 
[27]
2015 | Published | Conference Paper | IST-REx-ID: 782
Alistarh, Dan-Adrian, Thomas Sauerwald, and Milan Vojnović. “Lock-Free Algorithms under Stochastic Schedulers,” 2015–July:251–60. ACM, 2015. https://doi.org/10.1145/2767386.2767430.
View | DOI
 
[26]
2015 | Published | Conference Paper | IST-REx-ID: 783 | OA
Alistarh, Dan-Adrian, Rati Gelashvili, and Adrian Vladu. “How to Elect a Leader Faster than a Tournament,” 2015–July:365–74. ACM, 2015. https://doi.org/10.1145/2767386.2767420.
View | DOI | Download None (ext.)
 
[25]
2015 | Published | Conference Paper | IST-REx-ID: 784
Alistarh, Dan-Adrian, Hitesh Ballani, Paolo Costa, Adam Funnell, Joshua Benjamin, Philip Watts, and Benn Thomsen. “A High-Radix, Low-Latency Optical Switch for Data Centers,” 367–68. ACM, 2015. https://doi.org/10.1145/2785956.2790035.
View | DOI
 
[24]
2014 | Published | Conference Paper | IST-REx-ID: 768
Alistarh, Dan-Adrian, James Aspnes, Michael Bender, Rati Gelashvili, and Seth Gilbert. “Dynamic Task Allocation in Asynchronous Shared Memory,” 416–35. SIAM, 2014. https://doi.org/10.1137/1.9781611973402.31.
View | DOI
 
[23]
2014 | Published | Journal Article | IST-REx-ID: 769
Alistarh, Dan-Adrian, James Aspnes, Keren Censor Hillel, Seth Gilbert, and Rachid Guerraoui. “Tight Bounds for Asynchronous Renaming.” Journal of the ACM. ACM, 2014. https://doi.org/10.1145/2597630.
View | DOI
 
[22]
2014 | Published | Conference Paper | IST-REx-ID: 770
Alistarh, Dan-Adrian, Patrick Eugster, Maurice Herlihy, Alexander Matveev, and Nir Shavit. “StackTrack: An Automated Transactional Approach to Concurrent Memory Reclamation.” ACM, 2014. https://doi.org/10.1145/2592798.2592808.
View | DOI
 
[21]
2014 | Published | Conference Paper | IST-REx-ID: 771
Alistarh, Dan-Adrian, Oksana Denysyuk, Luís Rodrígues, and Nir Shavit. “Balls-into-Leaves: Sub-Logarithmic Renaming in Synchronous Message-Passing Systems,” 232–41. ACM, 2014. https://doi.org/10.1145/2611462.2611499.
View | DOI
 
[20]
2014 | Published | Conference Paper | IST-REx-ID: 772 | OA
Alistarh, Dan-Adrian, Keren Censor Hillel, and Nir Shavit. “Are Lock-Free Concurrent Algorithms Practically Wait-Free?,” 714–23. ACM, 2014. https://doi.org/10.1145/2591796.2591836.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[19]
2014 | Published | Conference Paper | IST-REx-ID: 773
Alistarh, Dan-Adrian, James Aspnes, Valerie King, and Jared Saia. “Communication-Efficient Randomized Consensus.” edited by Fabian Kuhn, 8784:61–75. Springer, 2014. https://doi.org/10.1007/978-3-662-45174-8_5.
View | DOI
 
[18]
2014 | Published | Conference Paper | IST-REx-ID: 774
Alistarh, Dan-Adrian, Keren Censor Hille, and Nir Shavit. “Brief Announcement: Are Lock-Free Concurrent Algorithms Practically Wait-Free?,” 50–52. ACM, 2014. https://doi.org/10.1145/2611462.2611502.
View | DOI
 
[17]
2014 | Published | Conference Paper | IST-REx-ID: 775 | OA
Alistarh, Dan-Adrian, Justin Kopinsky, Alexander Matveev, and Nir Shavit. “The Levelarray: A Fast, Practical Long-Lived Renaming Algorithm,” 348–57. IEEE, 2014. https://doi.org/10.1109/ICDCS.2014.43.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[16]
2013 | Published | Conference Paper | IST-REx-ID: 765
Alistarh, Dan-Adrian, James Aspnes, George Giakkoupis, and Philipp Woelfel. “Randomized Loose Renaming in O(Loglogn) Time,” 200–209. ACM, 2013. https://doi.org/10.1145/2484239.2484240.
View | DOI
 
[15]
2012 | Published | Conference Paper | IST-REx-ID: 762
Alistarh, Dan-Adrian, Rachid Guerraoui, Petr Kuznetsov, and Giuliano Losa. “On the Cost of Composing Shared-Memory Algorithms,” 298–307. ACM, 2012. https://doi.org/10.1145/2312005.2312057.
View | DOI
 
[14]
2012 | Published | Conference Paper | IST-REx-ID: 763
Alistarh, Dan-Adrian, Hagit Attiya, Rachid Guerraoui, and Corentin Travers. “Early Deciding Synchronous Renaming in O(Log f) Rounds or Less,” 7355 LNCS:195–206. Springer, 2012. https://doi.org/10.1007/978-3-642-31104-8_17.
View | DOI
 
[13]
2012 | Published | Journal Article | IST-REx-ID: 764
Alistarh, Dan-Adrian, Seth Gilbert, Rachid Guerraoui, and Corentin Travers. “Of Choices, Failures and Asynchrony: The Many Faces of Set Agreement.” Algorithmica (New York). Springer, 2012. https://doi.org/10.1007/s00453-011-9581-7.
View | DOI
 
[12]
2012 | Published | Conference Paper | IST-REx-ID: 766
Alistarh, Dan-Adrian, Michael Bender, Seth Gilbert, and Rachid Guerraoui. “How to Allocate Tasks Asynchronously,” 331–40. IEEE, 2012. https://doi.org/10.1109/FOCS.2012.41.
View | DOI
 
[11]
2012 | Published | Journal Article | IST-REx-ID: 767
Alistarh, Dan-Adrian, Seth Gilbert, Rachid Guerraoui, and Corentin Travers. “Generating Fast Indulgent Algorithms.” Theory of Computing Systems. Elsevier, 2012. https://doi.org/10.1007/s00224-012-9407-2.
View | DOI
 
[10]
2011 | Published | Conference Paper | IST-REx-ID: 757
Alistarh, Dan-Adrian, Seth Gilbert, Rachid Guerraoui, and Corentin Travers. “Generating Fast Indulgent Algorithms,” 6522 LNCS:41–52. Springer, 2011. https://doi.org/10.1007/978-3-642-17679-1_4.
View | DOI
 
[9]
2011 | Published | Conference Paper | IST-REx-ID: 759
Alistarh, Dan-Adrian, James Aspnes, Seth Gilbert, and Rachid Guerraoui. “The Complexity of Renaming,” 718–27. IEEE, 2011. https://doi.org/10.1109/FOCS.2011.66.
View | DOI
 
[8]
2011 | Published | Conference Paper | IST-REx-ID: 760
Alistarh, Dan-Adrian, and James Aspnes. “Sub-Logarithmic Test-and-Set against a Weak Adversary,” 6950 LNCS:97–109. Springer, 2011. https://doi.org/10.1007/978-3-642-24100-0_7.
View | DOI
 
[7]
2011 | Published | Conference Paper | IST-REx-ID: 761
Alistarh, Dan-Adrian, James Aspnes, Keren Censor Hillel, Seth Gilbert, and Morteza Zadimoghaddam. “Optimal-Time Adaptive Strong Renaming, with Applications to Counting,” 239–48. ACM, 2011. https://doi.org/10.1145/1993806.1993850.
View | DOI
 
[6]
2010 | Published | Conference Paper | IST-REx-ID: 754
Alistarh, Dan-Adrian, Hagit Attiya, Seth Gilbert, Andrei Giurgiu, and Rachid Guerraoui. “Fast Randomized Test-and-Set and Renaming,” 6343 LNCS:94–108. Springer, 2010. https://doi.org/10.1007/978-3-642-15763-9_9.
View | DOI
 
[5]
2010 | Published | Conference Paper | IST-REx-ID: 755
Alistarh, Dan-Adrian, Seth Gilbert, Rachid Guerraoui, and Morteza Zadimoghaddam. “How Efficient Can Gossip Be? (On the Cost of Resilient Information Exchange),” 6199 LNCS:115–26. Springer, 2010. https://doi.org/10.1007/978-3-642-14162-1_10.
View | DOI
 
[4]
2010 | Published | Conference Paper | IST-REx-ID: 756
Alistarh, Dan-Adrian, Seth Gilbert, Rachid Guerraoui, Žarko Milošević, and Calvin Newport. “Securing Every Bit: Authenticated Broadcast in Radio Networks,” 50–59. ACM, 2010. https://doi.org/10.1145/1810479.1810489.
View | DOI
 
[3]
2010 | Published | Conference Paper | IST-REx-ID: 758
Alistarh, Dan-Adrian, Seth Gilbert, Rachid Guerraoui, and Corentin Travers. “Brief Announcement: New Bounds for Partially Synchronous Set Agreement,” 6343 LNCS:404–5. Springer, 2010. https://doi.org/10.1007/978-3-642-15763-9_40.
View | DOI
 
[2]
2009 | Published | Conference Paper | IST-REx-ID: 752
Alistarh, Dan-Adrian, Seth Gilbert, Rachid Guerraoui, and Corentin Travers. “Of Choices, Failures and Asynchrony: The Many Faces of Set Agreement,” 5878 LNCS:943–53. Springer, 2009. https://doi.org/10.1007/978-3-642-10631-6_95.
View | DOI
 
[1]
2008 | Published | Conference Paper | IST-REx-ID: 753
Alistarh, Dan-Adrian, Seth Gilbert, Rachid Guerraoui, and Corentin Travers. “How to Solve Consensus in the Smallest Window of Synchrony,” 5218 LNCS:32–46. Springer, 2008. https://doi.org/10.1007/978-3-540-87779-0_3.
View | DOI
 
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