Dan-Adrian Alistarh

149 Publications

Mark all

[149]
2025 | Published | Journal Article | IST-REx-ID: 19713 | OA
Talaei S, Ansaripour M, Nadiradze G, Alistarh D-A. Hybrid decentralized optimization: Leveraging both first- and zeroth-order optimizers for faster convergence. Proceedings of the39th AAAI Conference on Artificial Intelligence. 2025;39(19):20778-20786. doi:10.1609/aaai.v39i19.34290
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[148]
2025 | Published | Conference Paper | IST-REx-ID: 20038 | OA
Jin T, Humayun AI, Evci U, et al. The journey matters: Average parameter count over pre-training unifies sparse and dense scaling laws. In: 13th International Conference on Learning Representations. ICLR; 2025:85165-85181.
[Published Version] View | Files available | arXiv
 
[147]
2025 | Published | Conference Paper | IST-REx-ID: 20037 | OA
Sawmya S, Kong L, Markov I, Alistarh D-A, Shavit N. Wasserstein distances, neuronal entanglement, and sparsity. In: 13th International Conference on Learning Representations. ICLR; 2025:26244-26274.
[Published Version] View | Files available | arXiv
 
[146]
2025 | Published | Conference Paper | IST-REx-ID: 20032 | OA
Chen J, Yao D, Pervez AA, Alistarh D-A, Locatello F. Scalable mechanistic neural networks. In: 13th International Conference on Learning Representations. ICLR; 2025:63716-63737.
[Published Version] View | Files available | arXiv
 
[145]
2025 | Published | Conference Paper | IST-REx-ID: 20034 | OA
Robert T, Safaryan M, Modoranu I-V, Alistarh D-A. LDAdam: Adaptive optimization from low-dimensional gradient statistics. In: 13th International Conference on Learning Representations. ICLR; 2025:101877-101913.
[Published Version] View | Files available | arXiv
 
[144]
2025 | Published | Conference Paper | IST-REx-ID: 19877 | OA
Frantar E, Castro RL, Chen J, Hoefler T, Alistarh D-A. MARLIN: Mixed-precision auto-regressive parallel inference on Large Language Models. In: Proceedings of the 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming. Association for Computing Machinery; 2025:239-251. doi:10.1145/3710848.3710871
[Published Version] View | Files available | DOI | WoS | arXiv
 
[143]
2025 | Epub ahead of print | Journal Article | IST-REx-ID: 19969 | OA
Alistarh D-A, Rybicki J, Voitovych S. Near-optimal leader election in population protocols on graphs. Distributed Computing. 2025. doi:10.1007/s00446-025-00487-7
[Published Version] View | Files available | DOI | Download Published Version (ext.) | WoS | arXiv
 
[142]
2024 | Published | Conference Paper | IST-REx-ID: 17093 | OA
Zakerinia H, Talaei S, Nadiradze G, Alistarh D-A. Communication-efficient federated learning with data and client heterogeneity. In: Proceedings of the 27th International Conference on Artificial Intelligence and Statistics. Vol 238. ML Research Press; 2024:3448-3456.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[141]
2024 | Published | Conference Paper | IST-REx-ID: 15011 | OA
Kurtic E, Hoefler T, Alistarh D-A. How to prune your language model: Recovering accuracy on the “Sparsity May Cry” benchmark. In: Proceedings of Machine Learning Research. Vol 234. ML Research Press; 2024:542-553.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[140]
2024 | Published | Conference Paper | IST-REx-ID: 18113 | OA
Egiazarian V, Panferov A, Kuznedelev D, Frantar E, Babenko A, Alistarh D-A. Extreme compression of large language models via additive quantization. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:12284-12303.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[139]
2024 | Published | Conference Paper | IST-REx-ID: 18117 | OA
Nikdan M, Tabesh S, Crncevic E, Alistarh D-A. RoSA: Accurate parameter-efficient fine-tuning via robust adaptation. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:38187-38206.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[138]
2024 | Published | Conference Paper | IST-REx-ID: 18975 | OA
Modoranu I-V, Kalinov A, Kurtic E, Frantar E, Alistarh D-A. Error feedback can accurately compress preconditioners. In: 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:35910-35933.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[137]
2024 | Published | Conference Paper | IST-REx-ID: 18977 | OA
Dettmers T, Svirschevski RA, Egiazarian V, et al. 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
 
[136]
2024 | Published | Conference Paper | IST-REx-ID: 18061 | OA
Frantar E, Alistarh D-A. QMoE: Sub-1-bit compression of trillion parameter models. In: Gibbons P, Pekhimenko G, De Sa C, eds. Proceedings of Machine Learning and Systems. Vol 6. ; 2024.
[Published Version] View | Files available | Download Published Version (ext.)
 
[135]
2024 | Published | Conference Paper | IST-REx-ID: 18062 | OA
Frantar E, Ruiz CR, Houlsby N, Alistarh D-A, Evci U. 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
 
[134]
2024 | Published | Conference Paper | IST-REx-ID: 17329 | OA
Alistarh D-A, Chatterjee K, Karrabi M, Lazarsfeld JM. Game dynamics and equilibrium computation in the population protocol model. In: Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing. Association for Computing Machinery; 2024:40-49. doi:10.1145/3662158.3662768
[Published Version] View | Files available | DOI
 
[133]
2024 | Published | Conference Paper | IST-REx-ID: 18976 | OA
Islamov R, Safaryan M, Alistarh D-A. AsGrad: A sharp unified analysis of asynchronous-SGD algorithms. In: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. Vol 238. ML Research Press; 2024:649-657.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[132]
2024 | Published | Conference Paper | IST-REx-ID: 18121 | OA
Moakhar AS, Iofinova EB, Frantar E, Alistarh D-A. SPADE: Sparsity-guided debugging for deep neural networks. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:45955-45987.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[131]
2024 | Published | Conference Paper | IST-REx-ID: 17456 | OA
Markov I, Alimohammadi K, Frantar E, Alistarh D-A. L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning. In: Gibbons P, Pekhimenko G, De Sa C, eds. Proceedings of Machine Learning and Systems . Vol 6. Association for Computing Machinery; 2024.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[130]
2024 | Published | Conference Paper | IST-REx-ID: 19518 | OA
Wu D, Modoranu I-V, Safaryan M, Kuznedelev D, Alistarh D-A. The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[129]
2024 | Published | Conference Paper | IST-REx-ID: 19510 | OA
Modoranu I-V, Safaryan M, Malinovsky G, et al. MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[128]
2024 | Published | Conference Paper | IST-REx-ID: 19511 | OA
Ashkboos S, Mohtashami A, Croci ML, et al. QuaRot: Outlier-free 4-bit inference in rotated LLMs. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[127]
2024 | Published | Conference Paper | IST-REx-ID: 19519 | OA
Malinovskii V, Mazur D, Ilin I, et al. PV-tuning: Beyond straight-through estimation for extreme LLM compression. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.
[Published Version] View | Files available | arXiv
 
[126]
2024 | Published | Conference Paper | IST-REx-ID: 17332 | OA
Kokorin I, Yudov V, Aksenov V, Alistarh D-A. Wait-free trees with asymptotically-efficient range queries. In: 2024 IEEE International Parallel and Distributed Processing Symposium. IEEE; 2024:169-179. doi:10.1109/IPDPS57955.2024.00023
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[125]
2024 | Published | Conference Paper | IST-REx-ID: 18070
Chatterjee B, Kungurtsev V, Alistarh D-A. Federated SGD with local asynchrony. In: Proceedings of the 44th International Conference on Distributed Computing Systems. IEEE; 2024:857-868. doi:10.1109/ICDCS60910.2024.00084
View | DOI | WoS
 
[124]
2024 | Research Data Reference | IST-REx-ID: 19884 | OA
Frantar E, Castro R, Chen J, Hoefler T, Alistarh D-A. MARLIN: Mixed-precision auto-regressive parallel inference on Large Language Models. 2024. doi:10.5281/ZENODO.14213091
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[123]
2023 | Published | Journal Article | IST-REx-ID: 13179 | OA
Koval N, Khalanskiy D, Alistarh D-A. CQS: A formally-verified framework for fair and abortable synchronization. Proceedings of the ACM on Programming Languages. 2023;7. doi:10.1145/3591230
[Published Version] View | Files available | DOI
 
[122]
2023 | Published | Journal Article | IST-REx-ID: 12330 | OA
Aksenov V, Alistarh D-A, Drozdova A, Mohtashami A. The splay-list: A distribution-adaptive concurrent skip-list. Distributed Computing. 2023;36:395-418. doi:10.1007/s00446-022-00441-x
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[121]
2023 | Published | Conference Paper | IST-REx-ID: 12735 | OA
Koval N, Alistarh D-A, Elizarov R. Fast and scalable channels in Kotlin Coroutines. In: Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Association for Computing Machinery; 2023:107-118. doi:10.1145/3572848.3577481
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[120]
2023 | Published | Conference Paper | IST-REx-ID: 14460 | OA
Nikdan M, Pegolotti T, Iofinova EB, Kurtic E, Alistarh D-A. SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:26215-26227.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[119]
2023 | Published | Conference Paper | IST-REx-ID: 17378 | OA
Frantar E, Ashkboos S, Hoefler T, Alistarh D-A. 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
 
[118]
2023 | Published | Conference Paper | IST-REx-ID: 14458 | OA
Frantar E, Alistarh D-A. SparseGPT: Massive language models can be accurately pruned in one-shot. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:10323-10337.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[117]
2023 | Published | Journal Article | IST-REx-ID: 12566 | OA
Alistarh D-A, Ellen F, Rybicki J. Wait-free approximate agreement on graphs. Theoretical Computer Science. 2023;948(2). doi:10.1016/j.tcs.2023.113733
[Published Version] View | Files available | DOI | WoS
 
[116]
2023 | Published | Conference Paper | IST-REx-ID: 13053 | OA
Krumes A, Vladu A, Kurtic E, Lampert C, Alistarh D-A. CrAM: A Compression-Aware Minimizer. In: 11th International Conference on Learning Representations . OpenReview; 2023.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[115]
2023 | Published | Conference Paper | IST-REx-ID: 15363 | OA
Safaryan M, Krumes A, Alistarh D-A. Knowledge distillation performs partial variance reduction. In: 36th Conference on Neural Information Processing Systems. Vol 36. ; 2023.
[Published Version] View | Files available | arXiv
 
[114]
2023 | Published | Conference Paper | IST-REx-ID: 14771 | OA
Iofinova EB, Krumes A, Alistarh D-A. Bias in pruned vision models: In-depth analysis and countermeasures. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE; 2023:24364-24373. doi:10.1109/cvpr52729.2023.02334
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[113]
2023 | Published | Conference Paper | IST-REx-ID: 14461 | OA
Markov I, Vladu A, Guo Q, Alistarh D-A. Quantized distributed training of large models with convergence guarantees. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:24020-24044.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[112]
2023 | Published | Journal Article | IST-REx-ID: 14364 | OA
Alistarh D-A, Aspnes J, Ellen F, Gelashvili R, Zhu L. Why extension-based proofs fail. SIAM Journal on Computing. 2023;52(4):913-944. doi:10.1137/20M1375851
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[111]
2023 | Published | Conference Paper | IST-REx-ID: 13262 | OA
Fedorov A, Hashemi D, Nadiradze G, Alistarh D-A. Provably-efficient and internally-deterministic parallel Union-Find. In: Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures. Association for Computing Machinery; 2023:261-271. doi:10.1145/3558481.3591082
[Published Version] View | Files available | DOI | WoS | arXiv
 
[110]
2023 | Published | Conference Paper | IST-REx-ID: 14260 | OA
Koval N, Fedorov A, Sokolova M, Tsitelov D, Alistarh D-A. Lincheck: A practical framework for testing concurrent data structures on JVM. In: 35th International Conference on Computer Aided Verification . Vol 13964. Springer Nature; 2023:156-169. doi:10.1007/978-3-031-37706-8_8
[Published Version] View | Files available | DOI | WoS
 
[109]
2023 | Research Data Reference | IST-REx-ID: 14995 | OA
Koval N, Fedorov A, Sokolova M, Tsitelov D, Alistarh D-A. Lincheck: A practical framework for testing concurrent data structures on JVM. 2023. doi:10.5281/ZENODO.7877757
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[108]
2022 | Published | Conference Paper | IST-REx-ID: 11181 | OA
Brown TA, Sigouin W, Alistarh D-A. 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. Association for Computing Machinery; 2022:385-399. doi:10.1145/3503221.3508410
[Published Version] View | Files available | DOI | WoS
 
[107]
2022 | Published | Conference Paper | IST-REx-ID: 17088 | OA
Kurtic E, Campos D, Nguyen T, et al. 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. Association for Computational Linguistics; 2022:4163-4181. doi: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 A, Koval N, Nadiradze G, Alistarh D-A. 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. Association for Computing Machinery; 2022:353-367. doi: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 A, Koval N, Nadiradze G, Alistarh D-A. Multi-queues can be state-of-the-art priority schedulers. 2022. doi:10.5281/ZENODO.5733408
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[104]
2022 | Published | Conference Paper | IST-REx-ID: 17087 | OA
Frantar E, Singh SP, Alistarh D-A. 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
 
[103]
2022 | Published | Conference Paper | IST-REx-ID: 17059 | OA
Frantar E, Alistarh D-A. SPDY: Accurate pruning with speedup guarantees. In: 39th International Conference on Machine Learning. Vol 162. ML Research Press; 2022:6726-6743.
[Published Version] View | Files available | WoS
 
[102]
2022 | Published | Conference Paper | IST-REx-ID: 11184 | OA
Alistarh D-A, Gelashvili R, Rybicki J. Fast graphical population protocols. In: Bramas Q, Gramoli V, Milani A, eds. 25th International Conference on Principles of Distributed Systems. Vol 217. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2022. doi:10.4230/LIPIcs.OPODIS.2021.14
[Published Version] View | Files available | DOI | arXiv
 
[101]
2022 | Published | Conference Paper | IST-REx-ID: 12299 | OA
Iofinova EB, Krumes A, Kurtz M, Alistarh D-A. How well do sparse ImageNet models transfer? In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Institute of Electrical and Electronics Engineers; 2022:12256-12266. doi:10.1109/cvpr52688.2022.01195
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[100]
2022 | Published | Journal Article | IST-REx-ID: 8286 | OA
Alistarh D-A, Nadiradze G, Sabour A. Dynamic averaging load balancing on cycles. Algorithmica. 2022;84(4):1007-1029. doi:10.1007/s00453-021-00905-9
[Published Version] View | Files available | DOI | WoS | arXiv
 
[99]
2022 | Published | Conference Paper | IST-REx-ID: 12780 | OA
Markov I, Ramezanikebrya H, Alistarh D-A. CGX: Adaptive system support for communication-efficient deep learning. In: Proceedings of the 23rd ACM/IFIP International Middleware Conference. Association for Computing Machinery; 2022:241-254. doi:10.1145/3528535.3565248
[Published Version] View | Files available | DOI | WoS | arXiv
 
[98]
2022 | Published | Conference Paper | IST-REx-ID: 11844 | OA
Alistarh D-A, Rybicki J, Voitovych S. Near-optimal leader election in population protocols on graphs. In: Proceedings of the Annual ACM Symposium on Principles of Distributed Computing. Association for Computing Machinery; 2022:246-256. doi:10.1145/3519270.3538435
[Published Version] View | Files available | DOI | WoS | arXiv
 
[97]
2021 | Published | Conference Paper | IST-REx-ID: 10853 | OA
Fedorov A, Koval N, Alistarh D-A. A scalable concurrent algorithm for dynamic connectivity. In: Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures. Association for Computing Machinery; 2021:208-220. doi:10.1145/3409964.3461810
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[96]
2021 | Published | Conference Paper | IST-REx-ID: 9951
Alistarh D-A, Töpfer M, Uznański P. Comparison dynamics in population protocols. In: Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing. Association for Computing Machinery; 2021:55-65. doi:10.1145/3465084.3467915
View | DOI | WoS
 
[95]
2021 | Published | Conference Paper | IST-REx-ID: 10218 | OA
Alistarh D-A, Gelashvili R, Rybicki J. Brief announcement: Fast graphical population protocols. In: 35th International Symposium on Distributed Computing. Vol 209. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2021. doi:10.4230/LIPIcs.DISC.2021.43
[Published Version] View | Files available | DOI | arXiv
 
[94]
2021 | Published | Conference Paper | IST-REx-ID: 11436 | OA
Kungurtsev V, Egan M, Chatterjee B, Alistarh D-A. Asynchronous optimization methods for efficient training of deep neural networks with guarantees. In: 35th AAAI Conference on Artificial Intelligence, AAAI 2021. Vol 35. AAAI Press; 2021:8209-8216.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[93]
2021 | Published | Conference Paper | IST-REx-ID: 10435 | OA
Nadiradze G, Sabour A, Davies P, Li S, Alistarh D-A. 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
 
[92]
2021 | Published | Conference Paper | IST-REx-ID: 11452 | OA
Alimisis F, Davies P, Vandereycken B, Alistarh D-A. Distributed principal component analysis with limited communication. In: Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems. Vol 4. Neural Information Processing Systems Foundation; 2021:2823-2834.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[91]
2021 | Published | Conference Paper | IST-REx-ID: 10432 | OA
Nadiradze G, Markov I, Chatterjee B, Kungurtsev V, Alistarh D-A. Elastic consistency: A practical consistency model for distributed stochastic gradient descent. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol 35. ; 2021:9037-9045.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[90]
2021 | Published | Conference Paper | IST-REx-ID: 11458 | OA
Krumes A, Iofinova EB, Vladu A, Alistarh D-A. AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. In: 35th Conference on Neural Information Processing Systems. Vol 34. Neural Information Processing Systems Foundation; 2021:8557-8570.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[89]
2021 | Published | Conference Paper | IST-REx-ID: 11463 | OA
Frantar E, Kurtic E, Alistarh D-A. M-FAC: Efficient matrix-free approximations of second-order information. In: 35th Conference on Neural Information Processing Systems. Vol 34. Neural Information Processing Systems Foundation; 2021:14873-14886.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[88]
2021 | Published | Conference Paper | IST-REx-ID: 11464 | OA
Alistarh D-A, Korhonen J. Towards tight communication lower bounds for distributed optimisation. In: 35th Conference on Neural Information Processing Systems. Vol 34. Neural Information Processing Systems Foundation; 2021:7254-7266.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[87]
2021 | Published | Conference Paper | IST-REx-ID: 10217 | OA
Alistarh D-A, Gelashvili R, Nadiradze G. Lower bounds for shared-memory leader election under bounded write contention. In: 35th International Symposium on Distributed Computing. Vol 209. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2021. doi:10.4230/LIPIcs.DISC.2021.4
[Published Version] View | Files available | DOI
 
[86]
2021 | Published | Journal Article | IST-REx-ID: 10180 | OA
Hoefler T, Alistarh D-A, Ben-Nun T, Dryden N, Krumes A. Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks. Journal of Machine Learning Research. 2021;22(241):1-124.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[85]
2021 | Published | Conference Paper | IST-REx-ID: 13147 | OA
Alimisis F, Davies P, Alistarh D-A. Communication-efficient distributed optimization with quantized preconditioners. In: Proceedings of the 38th International Conference on Machine Learning. Vol 139. ML Research Press; 2021:196-206.
[Published Version] View | Files available | arXiv
 
[84]
2021 | Published | Journal Article | IST-REx-ID: 8723 | OA
Li S, Tal Ben-Nun TB-N, Nadiradze G, et al. Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging. IEEE Transactions on Parallel and Distributed Systems. 2021;32(7). doi:10.1109/TPDS.2020.3040606
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[83]
2021 | Published | Conference Paper | IST-REx-ID: 9543 | OA
Davies P, Gurunanthan V, Moshrefi N, Ashkboos S, Alistarh D-A. New bounds for distributed mean estimation and variance reduction. In: 9th International Conference on Learning Representations. ; 2021.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[82]
2021 | Published | Journal Article | IST-REx-ID: 9571 | OA
Ramezani-Kebrya A, Faghri F, Markov I, Aksenov V, Alistarh D-A, Roy DM. NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization. Journal of Machine Learning Research. 2021;22(114):1−43.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[81]
2021 | Published | Conference Paper | IST-REx-ID: 9620 | OA
Alistarh D-A, Davies P. Collecting coupons is faster with friends. In: Structural Information and Communication Complexity. Vol 12810. Springer Nature; 2021:3-12. doi:10.1007/978-3-030-79527-6_1
[Preprint] View | Files available | DOI | WoS
 
[80]
2021 | Published | Conference Paper | IST-REx-ID: 9823 | OA
Alistarh D-A, Ellen F, Rybicki J. Wait-free approximate agreement on graphs. In: Structural Information and Communication Complexity. Vol 12810. Springer Nature; 2021:87-105. doi:10.1007/978-3-030-79527-6_6
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[79]
2020 | Published | Conference Paper | IST-REx-ID: 8191
Alistarh D-A, Brown TA, Singhal N. Memory tagging: Minimalist synchronization for scalable concurrent data structures. In: Annual ACM Symposium on Parallelism in Algorithms and Architectures. Association for Computing Machinery; 2020:37-49. doi:10.1145/3350755.3400213
View | DOI | WoS
 
[78]
2020 | Conference Paper | IST-REx-ID: 9415 | OA
Kurtz M, Kopinsky J, Gelashvili R, et al. Inducing and exploiting activation sparsity for fast neural network inference. In: 37th International Conference on Machine Learning, ICML 2020. Vol 119. ; 2020:5533-5543.
[Published Version] View | Files available
 
[77]
2020 | Published | Conference Paper | IST-REx-ID: 8725 | OA
Aksenov V, Alistarh D-A, Drozdova A, Mohtashami A. The splay-list: A distribution-adaptive concurrent skip-list. In: 34th International Symposium on Distributed Computing. Vol 179. LIPIcs. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2020:3:1-3:18. doi:10.4230/LIPIcs.DISC.2020.3
[Published Version] View | Files available | DOI | arXiv
 
[76]
2020 | Published | Conference Paper | IST-REx-ID: 8722 | OA
Li S, Tal Ben-Nun TB-N, Girolamo SD, Alistarh D-A, Hoefler T. 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. Association for Computing Machinery; 2020:45-61. doi:10.1145/3332466.3374528
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[75]
2020 | Published | Conference Paper | IST-REx-ID: 7636 | OA
Brown TA, Prokopec A, Alistarh D-A. Non-blocking interpolation search trees with doubly-logarithmic running time. In: Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Association for Computing Machinery; 2020:276-291. doi:10.1145/3332466.3374542
[Published Version] View | DOI | Download Published Version (ext.) | WoS
 
[74]
2020 | Published | Conference Paper | IST-REx-ID: 15086 | OA
Faghri F, Tabrizian I, Markov I, Alistarh D-A, Roy D, Ramezani-Kebrya A. 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
 
[73]
2020 | Published | Conference Paper | IST-REx-ID: 9632 | OA
Singh SP, Alistarh D-A. WoodFisher: Efficient second-order approximation for neural network compression. In: Vol 33. Neural Information Processing Systems Foundation; 2020:18098-18109.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[72]
2020 | Published | Conference Paper | IST-REx-ID: 9631 | OA
Aksenov V, Alistarh D-A, Korhonen J. Scalable belief propagation via relaxed scheduling. In: Vol 33. Neural Information Processing Systems Foundation; 2020:22361-22372.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[71]
2020 | Published | Conference Paper | IST-REx-ID: 7635 | OA
Koval N, Sokolova M, Fedorov A, Alistarh D-A, Tsitelov D. Testing concurrency on the JVM with Lincheck. In: Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Association for Computing Machinery; 2020:423-424. doi:10.1145/3332466.3374503
[Published Version] View | DOI | Download Published Version (ext.)
 
[70]
2020 | Published | Conference Paper | IST-REx-ID: 8724 | OA
Konstantinov NH, Frantar E, Alistarh D-A, Lampert C. On the sample complexity of adversarial multi-source PAC learning. In: Proceedings of the 37th International Conference on Machine Learning. Vol 119. ML Research Press; 2020:5416-5425.
[Published Version] View | Files available | arXiv
 
[69]
2020 | Published | Conference Paper | IST-REx-ID: 7605 | OA
Alistarh D-A, Fedorov A, Koval N. In search of the fastest concurrent union-find algorithm. In: 23rd International Conference on Principles of Distributed Systems. Vol 153. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2020:15:1-15:16. doi:10.4230/LIPIcs.OPODIS.2019.15
[Published Version] View | Files available | DOI | arXiv
 
[68]
2020 | Published | Conference Paper | IST-REx-ID: 15077 | OA
Alistarh D-A, Nadiradze G, Sabour A. Dynamic averaging load balancing on cycles. In: 47th International Colloquium on Automata, Languages, and Programming. Vol 168. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2020. doi:10.4230/LIPIcs.ICALP.2020.7
[Published Version] View | Files available | DOI | arXiv
 
[67]
2020 | Published | Journal Article | IST-REx-ID: 8268 | OA
Gurel NM, Kara K, Stojanov A, et al. Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications. IEEE Transactions on Signal Processing. 2020;68:4268-4282. doi:10.1109/TSP.2020.3010355
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[66]
2020 | Published | Conference Paper | IST-REx-ID: 8383
Alistarh D-A, Aspnes J, Ellen F, Gelashvili R, Zhu L. Brief Announcement: Why Extension-Based Proofs Fail. In: Proceedings of the 39th Symposium on Principles of Distributed Computing. Association for Computing Machinery; 2020:54-56. doi:10.1145/3382734.3405743
View | DOI | WoS
 
[65]
2019 | Published | Conference Paper | IST-REx-ID: 7437 | OA
Yu C, Tang H, Renggli C, et al. Distributed learning over unreliable networks. In: 36th International Conference on Machine Learning, ICML 2019. Vol 2019-June. IMLS; 2019:12481-12512.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
[64]
2019 | Published | Conference Paper | IST-REx-ID: 7122
Khirirat S, Johansson M, Alistarh D-A. Gradient compression for communication-limited convex optimization. In: 2018 IEEE Conference on Decision and Control. IEEE; 2019. doi:10.1109/cdc.2018.8619625
View | DOI | WoS
 
[63]
2019 | Published | Conference Paper | IST-REx-ID: 7228
Koval N, Alistarh D-A, Elizarov R. Scalable FIFO channels for programming via communicating sequential processes. In: 25th Anniversary of Euro-Par. Vol 11725. Springer Nature; 2019:317-333. doi:10.1007/978-3-030-29400-7_23
View | DOI | WoS
 
[62]
2019 | Published | Conference Poster | IST-REx-ID: 6485
Koval N, Alistarh D-A, Elizarov R. Lock-Free Channels for Programming via Communicating Sequential Processes. ACM; 2019:417-418. doi:10.1145/3293883.3297000
View | DOI | WoS
 
[61]
2019 | Published | Conference Paper | IST-REx-ID: 7542 | OA
Wendler C, Alistarh D-A, Püschel M. Powerset convolutional neural networks. In: Vol 32. Neural Information Processing Systems Foundation; 2019:927-938.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 
[60]
2019 | Published | Conference Paper | IST-REx-ID: 6673 | OA
Alistarh D-A, Nadiradze G, Koval N. Efficiency guarantees for parallel incremental algorithms under relaxed schedulers. In: 31st ACM Symposium on Parallelism in Algorithms and Architectures. ACM; 2019:145-154. doi:10.1145/3323165.3323201
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[59]
2019 | Published | Conference Paper | IST-REx-ID: 6676 | OA
Alistarh D-A, Aspnes J, Ellen F, Gelashvili R, Zhu L. Why extension-based proofs fail. In: Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing. ACM; 2019:986-996. doi:10.1145/3313276.3316407
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[58]
2019 | Published | Conference Paper | IST-REx-ID: 7201 | OA
Renggli C, Ashkboos S, Aghagolzadeh M, Alistarh D-A, Hoefler T. SparCML: High-performance sparse communication for machine learning. In: International Conference for High Performance Computing, Networking, Storage and Analysis, SC. ACM; 2019. doi:10.1145/3295500.3356222
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[57]
2018 | Published | Journal Article | IST-REx-ID: 6001
Alistarh D-A, Leiserson W, Matveev A, Shavit N. ThreadScan: Automatic and scalable memory reclamation. ACM Transactions on Parallel Computing. 2018;4(4). doi:10.1145/3201897
View | Files available | DOI
 
[56]
2018 | Published | Conference Paper | IST-REx-ID: 6031
Stojanov A, Smith TM, Alistarh D-A, Puschel M. Fast quantized arithmetic on x86: Trading compute for data movement. In: 2018 IEEE International Workshop on Signal Processing Systems. Vol 2018-October. IEEE; 2018. doi:10.1109/SiPS.2018.8598402
View | DOI | WoS
 
[55]
2018 | Published | Conference Paper | IST-REx-ID: 6558 | OA
Alistarh D-A, Allen-Zhu Z, Li J. Byzantine stochastic gradient descent. In: Advances in Neural Information Processing Systems. Vol 2018. Neural Information Processing Systems Foundation; 2018:4613-4623.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 
[54]
2018 | Published | Conference Paper | IST-REx-ID: 7116 | OA
Grubic D, Tam L, Alistarh D-A, Zhang C. 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. OpenProceedings; 2018:145-156. doi:10.5441/002/EDBT.2018.14
[Published Version] View | Files available | DOI
 
[53]
2018 | Published | Conference Paper | IST-REx-ID: 7123 | OA
Alistarh D-A, Aspnes J, Gelashvili R. Space-optimal majority in population protocols. In: Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms. ACM; 2018:2221-2239. doi:10.1137/1.9781611975031.144
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[52]
2018 | Published | Conference Paper | IST-REx-ID: 5963 | OA
Alistarh D-A, Brown TA, Kopinsky J, Nadiradze G. Relaxed schedulers can efficiently parallelize iterative algorithms. In: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. ACM; 2018:377-386. doi:10.1145/3212734.3212756
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[51]
2018 | Published | Conference Paper | IST-REx-ID: 5962 | OA
Alistarh D-A, De Sa C, Konstantinov NH. The convergence of stochastic gradient descent in asynchronous shared memory. In: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. ACM; 2018:169-178. doi:10.1145/3212734.3212763
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[50]
2018 | Published | Conference Paper | IST-REx-ID: 5961
Alistarh D-A. A brief tutorial on distributed and concurrent machine learning. In: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. ACM; 2018:487-488. doi:10.1145/3212734.3212798
View | DOI | WoS
 
[49]
2018 | Published | Conference Paper | IST-REx-ID: 5964 | OA
Aksenov V, Alistarh D-A, Kuznetsov P. Brief Announcement: Performance prediction for coarse-grained locking. In: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. ACM; 2018:411-413. doi:10.1145/3212734.3212785
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[48]
2018 | Published | Conference Paper | IST-REx-ID: 5965 | OA
Alistarh D-A, Brown TA, Kopinsky J, Li JZ, Nadiradze G. Distributionally linearizable data structures. In: Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18. ACM; 2018:133-142. doi:10.1145/3210377.3210411
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[47]
2018 | Published | Conference Paper | IST-REx-ID: 5966 | OA
Alistarh D-A, Haider SK, Kübler R, Nadiradze G. The transactional conflict problem. In: Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18. ACM; 2018:383-392. doi:10.1145/3210377.3210406
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[46]
2018 | Published | Conference Paper | IST-REx-ID: 6589 | OA
Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli C. The convergence of sparsified gradient methods. In: Advances in Neural Information Processing Systems 31. Vol Volume 2018. Neural Information Processing Systems Foundation; 2018:5973-5983.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
[45]
2018 | Published | Conference Paper | IST-REx-ID: 7812 | OA
Polino A, Pascanu R, Alistarh D-A. Model compression via distillation and quantization. In: 6th International Conference on Learning Representations. ; 2018.
[Published Version] View | Files available | arXiv
 
[44]
2018 | Published | Journal Article | IST-REx-ID: 536 | OA
Alistarh D-A, Aspnes J, King V, Saia J. Communication-efficient randomized consensus. Distributed Computing. 2018;31(6):489-501. doi:10.1007/s00446-017-0315-1
[Published Version] View | Files available | DOI | WoS
 
[43]
2017 | Published | Conference Paper | IST-REx-ID: 788 | OA
Alistarh D-A, Dudek B, Kosowski A, Soloveichik D, Uznański P. Robust detection in leak-prone population protocols. In: Vol 10467 LNCS. Springer; 2017:155-171. doi:10.1007/978-3-319-66799-7_11
View | DOI | Download None (ext.) | arXiv
 
[42]
2017 | Published | Conference Paper | IST-REx-ID: 789
Alistarh D-A, Leiserson W, Matveev A, Shavit N. Forkscan: Conservative memory reclamation for modern operating systems. In: ACM; 2017:483-498. doi:10.1145/3064176.3064214
View | DOI
 
[41]
2017 | Published | Conference Paper | IST-REx-ID: 790
Kara K, Alistarh D-A, Alonso G, Mutlu O, Zhang C. FPGA-accelerated dense linear machine learning: A precision-convergence trade-off. In: IEEE; 2017:160-167. doi:10.1109/FCCM.2017.39
View | DOI
 
[40]
2017 | Published | Conference Paper | IST-REx-ID: 791 | OA
Alistarh D-A, Kopinsky J, Li J, Nadiradze G. The power of choice in priority scheduling. In: Proceedings of the ACM Symposium on Principles of Distributed Computing. Vol Part F129314. ACM; 2017:283-292. doi:10.1145/3087801.3087810
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS | arXiv
 
[39]
2017 | Published | Conference Paper | IST-REx-ID: 787 | OA
Alistarh D-A, Aspnes J, Eisenstat D, Rivest R, Gelashvili R. Time-space trade-offs in population protocols. In: Proceedings of the 2017 Annual ACM-SIAM Symposium on Discrete Algorithms. SIAM; 2017:2560-2579. doi:10.1137/1.9781611974782.169
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[38]
2017 | Published | Conference Paper | IST-REx-ID: 487
Baig G, Radunovic B, Alistarh D-A, Balkwill M, Karagiannis T, Qiu L. Towards unlicensed cellular networks in TV white spaces. In: Proceedings of the 2017 13th International Conference on Emerging Networking EXperiments and Technologies. ACM; 2017:2-14. doi:10.1145/3143361.3143367
View | DOI | WoS
 
[37]
2017 | Published | Conference Paper | IST-REx-ID: 432 | OA
Zhang H, Li J, Kara K, Alistarh D-A, Liu J, Zhang C. ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning. In: Proceedings of Machine Learning Research. Vol 70. ML Research Press; 2017:4035-4043.
[Submitted Version] View | Files available | WoS
 
[36]
2017 | Published | Conference Paper | IST-REx-ID: 431 | OA
Alistarh D-A, Grubic D, Li J, Tomioka R, Vojnović M. QSGD: Communication-efficient SGD via gradient quantization and encoding. In: Vol 2017. Neural Information Processing Systems Foundation; 2017:1710-1721.
[Submitted Version] View | Download Submitted Version (ext.) | WoS | arXiv
 
[35]
2016 | Published | Conference Paper | IST-REx-ID: 785
Haider S, Hasenplaugh W, Alistarh D-A. Lease/Release: Architectural support for scaling contended data structures. In: Vol 12-16-March-2016. ACM; 2016. doi:10.1145/2851141.2851155
View | DOI
 
[34]
2016 | Published | Journal Article | IST-REx-ID: 786 | OA
Alistarh D-A, Censor Hillel K, Shavit N. Are lock free concurrent algorithms practically wait free . Journal of the ACM. 2016;63(4). doi:10.1145/2903136
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[33]
2015 | Published | Conference Paper | IST-REx-ID: 776
Alistarh D-A, Kopinsky J, Li J, Shavit N. The SprayList: A scalable relaxed priority queue. In: Vol 2015-January. ACM; 2015:11-20. doi:10.1145/2688500.2688523
View | DOI
 
[32]
2015 | Published | Conference Paper | IST-REx-ID: 777
Alistarh D-A, Iglesias J, Vojnović M. Streaming min-max hypergraph partitioning. In: Vol 2015-January. Neural Information Processing Systems; 2015:1900-1908.
View | Download None (ext.)
 
[31]
2015 | Published | Conference Paper | IST-REx-ID: 778 | OA
Alistarh D-A, Kopinsky J, Kuznetsov P, Ravi S, Shavit N. Inherent limitations of hybrid transactional memory. In: Vol 9363. Springer; 2015:185-199. doi:10.1007/978-3-662-48653-5_13
View | DOI | Download None (ext.) | arXiv
 
[30]
2015 | Published | Conference Paper | IST-REx-ID: 779
Alistarh D-A, Matveev A, Leiserson W, Shavit N. ThreadScan: Automatic and scalable memory reclamation. In: Vol 2015-June. ACM; 2015:123-132. doi:10.1145/2755573.2755600
View | Files available | DOI
 
[29]
2015 | Published | Conference Paper | IST-REx-ID: 780 | OA
Alistarh D-A, Gelashvili R. Polylogarithmic-time leader election in population protocols. In: Vol 9135. Springer; 2015:479-491. doi: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 D-A, Gelashvili R, Vojnović M. Fast and exact majority in population protocols. In: Vol 2015-July. ACM; 2015:47-56. doi:10.1145/2767386.2767429
View | DOI
 
[27]
2015 | Published | Conference Paper | IST-REx-ID: 782
Alistarh D-A, Sauerwald T, Vojnović M. Lock-Free algorithms under stochastic schedulers. In: Vol 2015-July. ACM; 2015:251-260. doi:10.1145/2767386.2767430
View | DOI
 
[26]
2015 | Published | Conference Paper | IST-REx-ID: 783 | OA
Alistarh D-A, Gelashvili R, Vladu A. How to elect a leader faster than a tournament. In: Vol 2015-July. ACM; 2015:365-374. doi:10.1145/2767386.2767420
View | DOI | Download None (ext.)
 
[25]
2015 | Published | Conference Paper | IST-REx-ID: 784
Alistarh D-A, Ballani H, Costa P, et al. A high-radix, low-latency optical switch for data centers. In: ACM; 2015:367-368. doi:10.1145/2785956.2790035
View | DOI
 
[24]
2014 | Published | Conference Paper | IST-REx-ID: 768
Alistarh D-A, Aspnes J, Bender M, Gelashvili R, Gilbert S. Dynamic task allocation in asynchronous shared memory. In: SIAM; 2014:416-435. doi:10.1137/1.9781611973402.31
View | DOI
 
[23]
2014 | Published | Journal Article | IST-REx-ID: 769
Alistarh D-A, Aspnes J, Censor Hillel K, Gilbert S, Guerraoui R. Tight bounds for asynchronous renaming. Journal of the ACM. 2014;61(3). doi:10.1145/2597630
View | DOI
 
[22]
2014 | Published | Conference Paper | IST-REx-ID: 770
Alistarh D-A, Eugster P, Herlihy M, Matveev A, Shavit N. StackTrack: An automated transactional approach to concurrent memory reclamation. In: ACM; 2014. doi:10.1145/2592798.2592808
View | DOI
 
[21]
2014 | Published | Conference Paper | IST-REx-ID: 771
Alistarh D-A, Denysyuk O, Rodrígues L, Shavit N. Balls-into-Leaves: Sub-logarithmic renaming in synchronous message-passing systems. In: ACM; 2014:232-241. doi:10.1145/2611462.2611499
View | DOI
 
[20]
2014 | Published | Conference Paper | IST-REx-ID: 772 | OA
Alistarh D-A, Censor Hillel K, Shavit N. Are lock-free concurrent algorithms practically wait-free? In: ACM; 2014:714-723. doi:10.1145/2591796.2591836
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[19]
2014 | Published | Conference Paper | IST-REx-ID: 773
Alistarh D-A, Aspnes J, King V, Saia J. Communication-efficient randomized consensus. In: Kuhn F, ed. Vol 8784. Springer; 2014:61-75. doi:10.1007/978-3-662-45174-8_5
View | DOI
 
[18]
2014 | Published | Conference Paper | IST-REx-ID: 774
Alistarh D-A, Censor Hille K, Shavit N. Brief announcement: Are lock-free concurrent algorithms practically wait-free? In: ACM; 2014:50-52. doi:10.1145/2611462.2611502
View | DOI
 
[17]
2014 | Published | Conference Paper | IST-REx-ID: 775 | OA
Alistarh D-A, Kopinsky J, Matveev A, Shavit N. The levelarray: A fast, practical long-lived renaming algorithm. In: IEEE; 2014:348-357. doi:10.1109/ICDCS.2014.43
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[16]
2013 | Published | Conference Paper | IST-REx-ID: 765
Alistarh D-A, Aspnes J, Giakkoupis G, Woelfel P. Randomized loose renaming in O(loglogn) time. In: ACM; 2013:200-209. doi:10.1145/2484239.2484240
View | DOI
 
[15]
2012 | Published | Conference Paper | IST-REx-ID: 762
Alistarh D-A, Guerraoui R, Kuznetsov P, Losa G. On the cost of composing shared-memory algorithms. In: ACM; 2012:298-307. doi:10.1145/2312005.2312057
View | DOI
 
[14]
2012 | Published | Conference Paper | IST-REx-ID: 763
Alistarh D-A, Attiya H, Guerraoui R, Travers C. Early deciding synchronous renaming in O(log f) rounds or less. In: Vol 7355 LNCS. Springer; 2012:195-206. doi:10.1007/978-3-642-31104-8_17
View | DOI
 
[13]
2012 | Published | Journal Article | IST-REx-ID: 764
Alistarh D-A, Gilbert S, Guerraoui R, Travers C. Of choices, failures and asynchrony: the many faces of set agreement. Algorithmica (New York). 2012;62(1-2):595-629. doi:10.1007/s00453-011-9581-7
View | DOI
 
[12]
2012 | Published | Conference Paper | IST-REx-ID: 766
Alistarh D-A, Bender M, Gilbert S, Guerraoui R. How to allocate tasks asynchronously. In: IEEE; 2012:331-340. doi:10.1109/FOCS.2012.41
View | DOI
 
[11]
2012 | Published | Journal Article | IST-REx-ID: 767
Alistarh D-A, Gilbert S, Guerraoui R, Travers C. Generating Fast Indulgent Algorithms. Theory of Computing Systems. 2012;51(4):404-424. doi:10.1007/s00224-012-9407-2
View | DOI
 
[10]
2011 | Published | Conference Paper | IST-REx-ID: 757
Alistarh D-A, Gilbert S, Guerraoui R, Travers C. Generating fast indulgent algorithms. In: Vol 6522 LNCS. Springer; 2011:41-52. doi:10.1007/978-3-642-17679-1_4
View | DOI
 
[9]
2011 | Published | Conference Paper | IST-REx-ID: 759
Alistarh D-A, Aspnes J, Gilbert S, Guerraoui R. The complexity of renaming. In: IEEE; 2011:718-727. doi:10.1109/FOCS.2011.66
View | DOI
 
[8]
2011 | Published | Conference Paper | IST-REx-ID: 760
Alistarh D-A, Aspnes J. Sub-logarithmic test-and-set against a weak adversary. In: Vol 6950 LNCS. Springer; 2011:97-109. doi:10.1007/978-3-642-24100-0_7
View | DOI
 
[7]
2011 | Published | Conference Paper | IST-REx-ID: 761
Alistarh D-A, Aspnes J, Censor Hillel K, Gilbert S, Zadimoghaddam M. Optimal-time adaptive strong renaming, with applications to counting. In: ACM; 2011:239-248. doi:10.1145/1993806.1993850
View | DOI
 
[6]
2010 | Published | Conference Paper | IST-REx-ID: 754
Alistarh D-A, Attiya H, Gilbert S, Giurgiu A, Guerraoui R. Fast randomized test-and-set and renaming. In: Vol 6343 LNCS. Springer; 2010:94-108. doi:10.1007/978-3-642-15763-9_9
View | DOI
 
[5]
2010 | Published | Conference Paper | IST-REx-ID: 755
Alistarh D-A, Gilbert S, Guerraoui R, Zadimoghaddam M. How efficient can gossip be? (On the cost of resilient information exchange). In: Vol 6199 LNCS. Springer; 2010:115-126. doi:10.1007/978-3-642-14162-1_10
View | DOI
 
[4]
2010 | Published | Conference Paper | IST-REx-ID: 756
Alistarh D-A, Gilbert S, Guerraoui R, Milošević Ž, Newport C. Securing every bit: Authenticated broadcast in radio networks. In: ACM; 2010:50-59. doi:10.1145/1810479.1810489
View | DOI
 
[3]
2010 | Published | Conference Paper | IST-REx-ID: 758
Alistarh D-A, Gilbert S, Guerraoui R, Travers C. Brief announcement: New bounds for partially synchronous set agreement. In: Vol 6343 LNCS. Springer; 2010:404-405. doi:10.1007/978-3-642-15763-9_40
View | DOI
 
[2]
2009 | Published | Conference Paper | IST-REx-ID: 752
Alistarh D-A, Gilbert S, Guerraoui R, Travers C. Of choices, failures and asynchrony: the many faces of set agreement. In: Vol 5878 LNCS. Springer; 2009:943-953. doi:10.1007/978-3-642-10631-6_95
View | DOI
 
[1]
2008 | Published | Conference Paper | IST-REx-ID: 753
Alistarh D-A, Gilbert S, Guerraoui R, Travers C. How to solve consensus in the smallest window of synchrony. In: Vol 5218 LNCS. Springer; 2008:32-46. doi:10.1007/978-3-540-87779-0_3
View | DOI
 
Marked Publication(s)

Search

Filter Publications

Display / Sort

Citation Style: AMA

Export / Embed

Grants


149 Publications

Mark all

[149]
2025 | Published | Journal Article | IST-REx-ID: 19713 | OA
Talaei S, Ansaripour M, Nadiradze G, Alistarh D-A. Hybrid decentralized optimization: Leveraging both first- and zeroth-order optimizers for faster convergence. Proceedings of the39th AAAI Conference on Artificial Intelligence. 2025;39(19):20778-20786. doi:10.1609/aaai.v39i19.34290
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[148]
2025 | Published | Conference Paper | IST-REx-ID: 20038 | OA
Jin T, Humayun AI, Evci U, et al. The journey matters: Average parameter count over pre-training unifies sparse and dense scaling laws. In: 13th International Conference on Learning Representations. ICLR; 2025:85165-85181.
[Published Version] View | Files available | arXiv
 
[147]
2025 | Published | Conference Paper | IST-REx-ID: 20037 | OA
Sawmya S, Kong L, Markov I, Alistarh D-A, Shavit N. Wasserstein distances, neuronal entanglement, and sparsity. In: 13th International Conference on Learning Representations. ICLR; 2025:26244-26274.
[Published Version] View | Files available | arXiv
 
[146]
2025 | Published | Conference Paper | IST-REx-ID: 20032 | OA
Chen J, Yao D, Pervez AA, Alistarh D-A, Locatello F. Scalable mechanistic neural networks. In: 13th International Conference on Learning Representations. ICLR; 2025:63716-63737.
[Published Version] View | Files available | arXiv
 
[145]
2025 | Published | Conference Paper | IST-REx-ID: 20034 | OA
Robert T, Safaryan M, Modoranu I-V, Alistarh D-A. LDAdam: Adaptive optimization from low-dimensional gradient statistics. In: 13th International Conference on Learning Representations. ICLR; 2025:101877-101913.
[Published Version] View | Files available | arXiv
 
[144]
2025 | Published | Conference Paper | IST-REx-ID: 19877 | OA
Frantar E, Castro RL, Chen J, Hoefler T, Alistarh D-A. MARLIN: Mixed-precision auto-regressive parallel inference on Large Language Models. In: Proceedings of the 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming. Association for Computing Machinery; 2025:239-251. doi:10.1145/3710848.3710871
[Published Version] View | Files available | DOI | WoS | arXiv
 
[143]
2025 | Epub ahead of print | Journal Article | IST-REx-ID: 19969 | OA
Alistarh D-A, Rybicki J, Voitovych S. Near-optimal leader election in population protocols on graphs. Distributed Computing. 2025. doi:10.1007/s00446-025-00487-7
[Published Version] View | Files available | DOI | Download Published Version (ext.) | WoS | arXiv
 
[142]
2024 | Published | Conference Paper | IST-REx-ID: 17093 | OA
Zakerinia H, Talaei S, Nadiradze G, Alistarh D-A. Communication-efficient federated learning with data and client heterogeneity. In: Proceedings of the 27th International Conference on Artificial Intelligence and Statistics. Vol 238. ML Research Press; 2024:3448-3456.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[141]
2024 | Published | Conference Paper | IST-REx-ID: 15011 | OA
Kurtic E, Hoefler T, Alistarh D-A. How to prune your language model: Recovering accuracy on the “Sparsity May Cry” benchmark. In: Proceedings of Machine Learning Research. Vol 234. ML Research Press; 2024:542-553.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[140]
2024 | Published | Conference Paper | IST-REx-ID: 18113 | OA
Egiazarian V, Panferov A, Kuznedelev D, Frantar E, Babenko A, Alistarh D-A. Extreme compression of large language models via additive quantization. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:12284-12303.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[139]
2024 | Published | Conference Paper | IST-REx-ID: 18117 | OA
Nikdan M, Tabesh S, Crncevic E, Alistarh D-A. RoSA: Accurate parameter-efficient fine-tuning via robust adaptation. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:38187-38206.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[138]
2024 | Published | Conference Paper | IST-REx-ID: 18975 | OA
Modoranu I-V, Kalinov A, Kurtic E, Frantar E, Alistarh D-A. Error feedback can accurately compress preconditioners. In: 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:35910-35933.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[137]
2024 | Published | Conference Paper | IST-REx-ID: 18977 | OA
Dettmers T, Svirschevski RA, Egiazarian V, et al. 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
 
[136]
2024 | Published | Conference Paper | IST-REx-ID: 18061 | OA
Frantar E, Alistarh D-A. QMoE: Sub-1-bit compression of trillion parameter models. In: Gibbons P, Pekhimenko G, De Sa C, eds. Proceedings of Machine Learning and Systems. Vol 6. ; 2024.
[Published Version] View | Files available | Download Published Version (ext.)
 
[135]
2024 | Published | Conference Paper | IST-REx-ID: 18062 | OA
Frantar E, Ruiz CR, Houlsby N, Alistarh D-A, Evci U. 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
 
[134]
2024 | Published | Conference Paper | IST-REx-ID: 17329 | OA
Alistarh D-A, Chatterjee K, Karrabi M, Lazarsfeld JM. Game dynamics and equilibrium computation in the population protocol model. In: Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing. Association for Computing Machinery; 2024:40-49. doi:10.1145/3662158.3662768
[Published Version] View | Files available | DOI
 
[133]
2024 | Published | Conference Paper | IST-REx-ID: 18976 | OA
Islamov R, Safaryan M, Alistarh D-A. AsGrad: A sharp unified analysis of asynchronous-SGD algorithms. In: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. Vol 238. ML Research Press; 2024:649-657.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[132]
2024 | Published | Conference Paper | IST-REx-ID: 18121 | OA
Moakhar AS, Iofinova EB, Frantar E, Alistarh D-A. SPADE: Sparsity-guided debugging for deep neural networks. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:45955-45987.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[131]
2024 | Published | Conference Paper | IST-REx-ID: 17456 | OA
Markov I, Alimohammadi K, Frantar E, Alistarh D-A. L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning. In: Gibbons P, Pekhimenko G, De Sa C, eds. Proceedings of Machine Learning and Systems . Vol 6. Association for Computing Machinery; 2024.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[130]
2024 | Published | Conference Paper | IST-REx-ID: 19518 | OA
Wu D, Modoranu I-V, Safaryan M, Kuznedelev D, Alistarh D-A. The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[129]
2024 | Published | Conference Paper | IST-REx-ID: 19510 | OA
Modoranu I-V, Safaryan M, Malinovsky G, et al. MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[128]
2024 | Published | Conference Paper | IST-REx-ID: 19511 | OA
Ashkboos S, Mohtashami A, Croci ML, et al. QuaRot: Outlier-free 4-bit inference in rotated LLMs. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[127]
2024 | Published | Conference Paper | IST-REx-ID: 19519 | OA
Malinovskii V, Mazur D, Ilin I, et al. PV-tuning: Beyond straight-through estimation for extreme LLM compression. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.
[Published Version] View | Files available | arXiv
 
[126]
2024 | Published | Conference Paper | IST-REx-ID: 17332 | OA
Kokorin I, Yudov V, Aksenov V, Alistarh D-A. Wait-free trees with asymptotically-efficient range queries. In: 2024 IEEE International Parallel and Distributed Processing Symposium. IEEE; 2024:169-179. doi:10.1109/IPDPS57955.2024.00023
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[125]
2024 | Published | Conference Paper | IST-REx-ID: 18070
Chatterjee B, Kungurtsev V, Alistarh D-A. Federated SGD with local asynchrony. In: Proceedings of the 44th International Conference on Distributed Computing Systems. IEEE; 2024:857-868. doi:10.1109/ICDCS60910.2024.00084
View | DOI | WoS
 
[124]
2024 | Research Data Reference | IST-REx-ID: 19884 | OA
Frantar E, Castro R, Chen J, Hoefler T, Alistarh D-A. MARLIN: Mixed-precision auto-regressive parallel inference on Large Language Models. 2024. doi:10.5281/ZENODO.14213091
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[123]
2023 | Published | Journal Article | IST-REx-ID: 13179 | OA
Koval N, Khalanskiy D, Alistarh D-A. CQS: A formally-verified framework for fair and abortable synchronization. Proceedings of the ACM on Programming Languages. 2023;7. doi:10.1145/3591230
[Published Version] View | Files available | DOI
 
[122]
2023 | Published | Journal Article | IST-REx-ID: 12330 | OA
Aksenov V, Alistarh D-A, Drozdova A, Mohtashami A. The splay-list: A distribution-adaptive concurrent skip-list. Distributed Computing. 2023;36:395-418. doi:10.1007/s00446-022-00441-x
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[121]
2023 | Published | Conference Paper | IST-REx-ID: 12735 | OA
Koval N, Alistarh D-A, Elizarov R. Fast and scalable channels in Kotlin Coroutines. In: Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Association for Computing Machinery; 2023:107-118. doi:10.1145/3572848.3577481
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[120]
2023 | Published | Conference Paper | IST-REx-ID: 14460 | OA
Nikdan M, Pegolotti T, Iofinova EB, Kurtic E, Alistarh D-A. SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:26215-26227.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[119]
2023 | Published | Conference Paper | IST-REx-ID: 17378 | OA
Frantar E, Ashkboos S, Hoefler T, Alistarh D-A. 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
 
[118]
2023 | Published | Conference Paper | IST-REx-ID: 14458 | OA
Frantar E, Alistarh D-A. SparseGPT: Massive language models can be accurately pruned in one-shot. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:10323-10337.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[117]
2023 | Published | Journal Article | IST-REx-ID: 12566 | OA
Alistarh D-A, Ellen F, Rybicki J. Wait-free approximate agreement on graphs. Theoretical Computer Science. 2023;948(2). doi:10.1016/j.tcs.2023.113733
[Published Version] View | Files available | DOI | WoS
 
[116]
2023 | Published | Conference Paper | IST-REx-ID: 13053 | OA
Krumes A, Vladu A, Kurtic E, Lampert C, Alistarh D-A. CrAM: A Compression-Aware Minimizer. In: 11th International Conference on Learning Representations . OpenReview; 2023.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[115]
2023 | Published | Conference Paper | IST-REx-ID: 15363 | OA
Safaryan M, Krumes A, Alistarh D-A. Knowledge distillation performs partial variance reduction. In: 36th Conference on Neural Information Processing Systems. Vol 36. ; 2023.
[Published Version] View | Files available | arXiv
 
[114]
2023 | Published | Conference Paper | IST-REx-ID: 14771 | OA
Iofinova EB, Krumes A, Alistarh D-A. Bias in pruned vision models: In-depth analysis and countermeasures. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE; 2023:24364-24373. doi:10.1109/cvpr52729.2023.02334
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[113]
2023 | Published | Conference Paper | IST-REx-ID: 14461 | OA
Markov I, Vladu A, Guo Q, Alistarh D-A. Quantized distributed training of large models with convergence guarantees. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:24020-24044.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[112]
2023 | Published | Journal Article | IST-REx-ID: 14364 | OA
Alistarh D-A, Aspnes J, Ellen F, Gelashvili R, Zhu L. Why extension-based proofs fail. SIAM Journal on Computing. 2023;52(4):913-944. doi:10.1137/20M1375851
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[111]
2023 | Published | Conference Paper | IST-REx-ID: 13262 | OA
Fedorov A, Hashemi D, Nadiradze G, Alistarh D-A. Provably-efficient and internally-deterministic parallel Union-Find. In: Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures. Association for Computing Machinery; 2023:261-271. doi:10.1145/3558481.3591082
[Published Version] View | Files available | DOI | WoS | arXiv
 
[110]
2023 | Published | Conference Paper | IST-REx-ID: 14260 | OA
Koval N, Fedorov A, Sokolova M, Tsitelov D, Alistarh D-A. Lincheck: A practical framework for testing concurrent data structures on JVM. In: 35th International Conference on Computer Aided Verification . Vol 13964. Springer Nature; 2023:156-169. doi:10.1007/978-3-031-37706-8_8
[Published Version] View | Files available | DOI | WoS
 
[109]
2023 | Research Data Reference | IST-REx-ID: 14995 | OA
Koval N, Fedorov A, Sokolova M, Tsitelov D, Alistarh D-A. Lincheck: A practical framework for testing concurrent data structures on JVM. 2023. doi:10.5281/ZENODO.7877757
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[108]
2022 | Published | Conference Paper | IST-REx-ID: 11181 | OA
Brown TA, Sigouin W, Alistarh D-A. 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. Association for Computing Machinery; 2022:385-399. doi:10.1145/3503221.3508410
[Published Version] View | Files available | DOI | WoS
 
[107]
2022 | Published | Conference Paper | IST-REx-ID: 17088 | OA
Kurtic E, Campos D, Nguyen T, et al. 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. Association for Computational Linguistics; 2022:4163-4181. doi: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 A, Koval N, Nadiradze G, Alistarh D-A. 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. Association for Computing Machinery; 2022:353-367. doi: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 A, Koval N, Nadiradze G, Alistarh D-A. Multi-queues can be state-of-the-art priority schedulers. 2022. doi:10.5281/ZENODO.5733408
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[104]
2022 | Published | Conference Paper | IST-REx-ID: 17087 | OA
Frantar E, Singh SP, Alistarh D-A. 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
 
[103]
2022 | Published | Conference Paper | IST-REx-ID: 17059 | OA
Frantar E, Alistarh D-A. SPDY: Accurate pruning with speedup guarantees. In: 39th International Conference on Machine Learning. Vol 162. ML Research Press; 2022:6726-6743.
[Published Version] View | Files available | WoS
 
[102]
2022 | Published | Conference Paper | IST-REx-ID: 11184 | OA
Alistarh D-A, Gelashvili R, Rybicki J. Fast graphical population protocols. In: Bramas Q, Gramoli V, Milani A, eds. 25th International Conference on Principles of Distributed Systems. Vol 217. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2022. doi:10.4230/LIPIcs.OPODIS.2021.14
[Published Version] View | Files available | DOI | arXiv
 
[101]
2022 | Published | Conference Paper | IST-REx-ID: 12299 | OA
Iofinova EB, Krumes A, Kurtz M, Alistarh D-A. How well do sparse ImageNet models transfer? In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Institute of Electrical and Electronics Engineers; 2022:12256-12266. doi:10.1109/cvpr52688.2022.01195
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[100]
2022 | Published | Journal Article | IST-REx-ID: 8286 | OA
Alistarh D-A, Nadiradze G, Sabour A. Dynamic averaging load balancing on cycles. Algorithmica. 2022;84(4):1007-1029. doi:10.1007/s00453-021-00905-9
[Published Version] View | Files available | DOI | WoS | arXiv
 
[99]
2022 | Published | Conference Paper | IST-REx-ID: 12780 | OA
Markov I, Ramezanikebrya H, Alistarh D-A. CGX: Adaptive system support for communication-efficient deep learning. In: Proceedings of the 23rd ACM/IFIP International Middleware Conference. Association for Computing Machinery; 2022:241-254. doi:10.1145/3528535.3565248
[Published Version] View | Files available | DOI | WoS | arXiv
 
[98]
2022 | Published | Conference Paper | IST-REx-ID: 11844 | OA
Alistarh D-A, Rybicki J, Voitovych S. Near-optimal leader election in population protocols on graphs. In: Proceedings of the Annual ACM Symposium on Principles of Distributed Computing. Association for Computing Machinery; 2022:246-256. doi:10.1145/3519270.3538435
[Published Version] View | Files available | DOI | WoS | arXiv
 
[97]
2021 | Published | Conference Paper | IST-REx-ID: 10853 | OA
Fedorov A, Koval N, Alistarh D-A. A scalable concurrent algorithm for dynamic connectivity. In: Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures. Association for Computing Machinery; 2021:208-220. doi:10.1145/3409964.3461810
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[96]
2021 | Published | Conference Paper | IST-REx-ID: 9951
Alistarh D-A, Töpfer M, Uznański P. Comparison dynamics in population protocols. In: Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing. Association for Computing Machinery; 2021:55-65. doi:10.1145/3465084.3467915
View | DOI | WoS
 
[95]
2021 | Published | Conference Paper | IST-REx-ID: 10218 | OA
Alistarh D-A, Gelashvili R, Rybicki J. Brief announcement: Fast graphical population protocols. In: 35th International Symposium on Distributed Computing. Vol 209. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2021. doi:10.4230/LIPIcs.DISC.2021.43
[Published Version] View | Files available | DOI | arXiv
 
[94]
2021 | Published | Conference Paper | IST-REx-ID: 11436 | OA
Kungurtsev V, Egan M, Chatterjee B, Alistarh D-A. Asynchronous optimization methods for efficient training of deep neural networks with guarantees. In: 35th AAAI Conference on Artificial Intelligence, AAAI 2021. Vol 35. AAAI Press; 2021:8209-8216.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[93]
2021 | Published | Conference Paper | IST-REx-ID: 10435 | OA
Nadiradze G, Sabour A, Davies P, Li S, Alistarh D-A. 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
 
[92]
2021 | Published | Conference Paper | IST-REx-ID: 11452 | OA
Alimisis F, Davies P, Vandereycken B, Alistarh D-A. Distributed principal component analysis with limited communication. In: Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems. Vol 4. Neural Information Processing Systems Foundation; 2021:2823-2834.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[91]
2021 | Published | Conference Paper | IST-REx-ID: 10432 | OA
Nadiradze G, Markov I, Chatterjee B, Kungurtsev V, Alistarh D-A. Elastic consistency: A practical consistency model for distributed stochastic gradient descent. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol 35. ; 2021:9037-9045.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[90]
2021 | Published | Conference Paper | IST-REx-ID: 11458 | OA
Krumes A, Iofinova EB, Vladu A, Alistarh D-A. AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. In: 35th Conference on Neural Information Processing Systems. Vol 34. Neural Information Processing Systems Foundation; 2021:8557-8570.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[89]
2021 | Published | Conference Paper | IST-REx-ID: 11463 | OA
Frantar E, Kurtic E, Alistarh D-A. M-FAC: Efficient matrix-free approximations of second-order information. In: 35th Conference on Neural Information Processing Systems. Vol 34. Neural Information Processing Systems Foundation; 2021:14873-14886.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[88]
2021 | Published | Conference Paper | IST-REx-ID: 11464 | OA
Alistarh D-A, Korhonen J. Towards tight communication lower bounds for distributed optimisation. In: 35th Conference on Neural Information Processing Systems. Vol 34. Neural Information Processing Systems Foundation; 2021:7254-7266.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[87]
2021 | Published | Conference Paper | IST-REx-ID: 10217 | OA
Alistarh D-A, Gelashvili R, Nadiradze G. Lower bounds for shared-memory leader election under bounded write contention. In: 35th International Symposium on Distributed Computing. Vol 209. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2021. doi:10.4230/LIPIcs.DISC.2021.4
[Published Version] View | Files available | DOI
 
[86]
2021 | Published | Journal Article | IST-REx-ID: 10180 | OA
Hoefler T, Alistarh D-A, Ben-Nun T, Dryden N, Krumes A. Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks. Journal of Machine Learning Research. 2021;22(241):1-124.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[85]
2021 | Published | Conference Paper | IST-REx-ID: 13147 | OA
Alimisis F, Davies P, Alistarh D-A. Communication-efficient distributed optimization with quantized preconditioners. In: Proceedings of the 38th International Conference on Machine Learning. Vol 139. ML Research Press; 2021:196-206.
[Published Version] View | Files available | arXiv
 
[84]
2021 | Published | Journal Article | IST-REx-ID: 8723 | OA
Li S, Tal Ben-Nun TB-N, Nadiradze G, et al. Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging. IEEE Transactions on Parallel and Distributed Systems. 2021;32(7). doi:10.1109/TPDS.2020.3040606
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[83]
2021 | Published | Conference Paper | IST-REx-ID: 9543 | OA
Davies P, Gurunanthan V, Moshrefi N, Ashkboos S, Alistarh D-A. New bounds for distributed mean estimation and variance reduction. In: 9th International Conference on Learning Representations. ; 2021.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[82]
2021 | Published | Journal Article | IST-REx-ID: 9571 | OA
Ramezani-Kebrya A, Faghri F, Markov I, Aksenov V, Alistarh D-A, Roy DM. NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization. Journal of Machine Learning Research. 2021;22(114):1−43.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[81]
2021 | Published | Conference Paper | IST-REx-ID: 9620 | OA
Alistarh D-A, Davies P. Collecting coupons is faster with friends. In: Structural Information and Communication Complexity. Vol 12810. Springer Nature; 2021:3-12. doi:10.1007/978-3-030-79527-6_1
[Preprint] View | Files available | DOI | WoS
 
[80]
2021 | Published | Conference Paper | IST-REx-ID: 9823 | OA
Alistarh D-A, Ellen F, Rybicki J. Wait-free approximate agreement on graphs. In: Structural Information and Communication Complexity. Vol 12810. Springer Nature; 2021:87-105. doi:10.1007/978-3-030-79527-6_6
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[79]
2020 | Published | Conference Paper | IST-REx-ID: 8191
Alistarh D-A, Brown TA, Singhal N. Memory tagging: Minimalist synchronization for scalable concurrent data structures. In: Annual ACM Symposium on Parallelism in Algorithms and Architectures. Association for Computing Machinery; 2020:37-49. doi:10.1145/3350755.3400213
View | DOI | WoS
 
[78]
2020 | Conference Paper | IST-REx-ID: 9415 | OA
Kurtz M, Kopinsky J, Gelashvili R, et al. Inducing and exploiting activation sparsity for fast neural network inference. In: 37th International Conference on Machine Learning, ICML 2020. Vol 119. ; 2020:5533-5543.
[Published Version] View | Files available
 
[77]
2020 | Published | Conference Paper | IST-REx-ID: 8725 | OA
Aksenov V, Alistarh D-A, Drozdova A, Mohtashami A. The splay-list: A distribution-adaptive concurrent skip-list. In: 34th International Symposium on Distributed Computing. Vol 179. LIPIcs. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2020:3:1-3:18. doi:10.4230/LIPIcs.DISC.2020.3
[Published Version] View | Files available | DOI | arXiv
 
[76]
2020 | Published | Conference Paper | IST-REx-ID: 8722 | OA
Li S, Tal Ben-Nun TB-N, Girolamo SD, Alistarh D-A, Hoefler T. 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. Association for Computing Machinery; 2020:45-61. doi:10.1145/3332466.3374528
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[75]
2020 | Published | Conference Paper | IST-REx-ID: 7636 | OA
Brown TA, Prokopec A, Alistarh D-A. Non-blocking interpolation search trees with doubly-logarithmic running time. In: Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Association for Computing Machinery; 2020:276-291. doi:10.1145/3332466.3374542
[Published Version] View | DOI | Download Published Version (ext.) | WoS
 
[74]
2020 | Published | Conference Paper | IST-REx-ID: 15086 | OA
Faghri F, Tabrizian I, Markov I, Alistarh D-A, Roy D, Ramezani-Kebrya A. 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
 
[73]
2020 | Published | Conference Paper | IST-REx-ID: 9632 | OA
Singh SP, Alistarh D-A. WoodFisher: Efficient second-order approximation for neural network compression. In: Vol 33. Neural Information Processing Systems Foundation; 2020:18098-18109.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[72]
2020 | Published | Conference Paper | IST-REx-ID: 9631 | OA
Aksenov V, Alistarh D-A, Korhonen J. Scalable belief propagation via relaxed scheduling. In: Vol 33. Neural Information Processing Systems Foundation; 2020:22361-22372.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[71]
2020 | Published | Conference Paper | IST-REx-ID: 7635 | OA
Koval N, Sokolova M, Fedorov A, Alistarh D-A, Tsitelov D. Testing concurrency on the JVM with Lincheck. In: Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Association for Computing Machinery; 2020:423-424. doi:10.1145/3332466.3374503
[Published Version] View | DOI | Download Published Version (ext.)
 
[70]
2020 | Published | Conference Paper | IST-REx-ID: 8724 | OA
Konstantinov NH, Frantar E, Alistarh D-A, Lampert C. On the sample complexity of adversarial multi-source PAC learning. In: Proceedings of the 37th International Conference on Machine Learning. Vol 119. ML Research Press; 2020:5416-5425.
[Published Version] View | Files available | arXiv
 
[69]
2020 | Published | Conference Paper | IST-REx-ID: 7605 | OA
Alistarh D-A, Fedorov A, Koval N. In search of the fastest concurrent union-find algorithm. In: 23rd International Conference on Principles of Distributed Systems. Vol 153. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2020:15:1-15:16. doi:10.4230/LIPIcs.OPODIS.2019.15
[Published Version] View | Files available | DOI | arXiv
 
[68]
2020 | Published | Conference Paper | IST-REx-ID: 15077 | OA
Alistarh D-A, Nadiradze G, Sabour A. Dynamic averaging load balancing on cycles. In: 47th International Colloquium on Automata, Languages, and Programming. Vol 168. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2020. doi:10.4230/LIPIcs.ICALP.2020.7
[Published Version] View | Files available | DOI | arXiv
 
[67]
2020 | Published | Journal Article | IST-REx-ID: 8268 | OA
Gurel NM, Kara K, Stojanov A, et al. Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications. IEEE Transactions on Signal Processing. 2020;68:4268-4282. doi:10.1109/TSP.2020.3010355
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[66]
2020 | Published | Conference Paper | IST-REx-ID: 8383
Alistarh D-A, Aspnes J, Ellen F, Gelashvili R, Zhu L. Brief Announcement: Why Extension-Based Proofs Fail. In: Proceedings of the 39th Symposium on Principles of Distributed Computing. Association for Computing Machinery; 2020:54-56. doi:10.1145/3382734.3405743
View | DOI | WoS
 
[65]
2019 | Published | Conference Paper | IST-REx-ID: 7437 | OA
Yu C, Tang H, Renggli C, et al. Distributed learning over unreliable networks. In: 36th International Conference on Machine Learning, ICML 2019. Vol 2019-June. IMLS; 2019:12481-12512.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
[64]
2019 | Published | Conference Paper | IST-REx-ID: 7122
Khirirat S, Johansson M, Alistarh D-A. Gradient compression for communication-limited convex optimization. In: 2018 IEEE Conference on Decision and Control. IEEE; 2019. doi:10.1109/cdc.2018.8619625
View | DOI | WoS
 
[63]
2019 | Published | Conference Paper | IST-REx-ID: 7228
Koval N, Alistarh D-A, Elizarov R. Scalable FIFO channels for programming via communicating sequential processes. In: 25th Anniversary of Euro-Par. Vol 11725. Springer Nature; 2019:317-333. doi:10.1007/978-3-030-29400-7_23
View | DOI | WoS
 
[62]
2019 | Published | Conference Poster | IST-REx-ID: 6485
Koval N, Alistarh D-A, Elizarov R. Lock-Free Channels for Programming via Communicating Sequential Processes. ACM; 2019:417-418. doi:10.1145/3293883.3297000
View | DOI | WoS
 
[61]
2019 | Published | Conference Paper | IST-REx-ID: 7542 | OA
Wendler C, Alistarh D-A, Püschel M. Powerset convolutional neural networks. In: Vol 32. Neural Information Processing Systems Foundation; 2019:927-938.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 
[60]
2019 | Published | Conference Paper | IST-REx-ID: 6673 | OA
Alistarh D-A, Nadiradze G, Koval N. Efficiency guarantees for parallel incremental algorithms under relaxed schedulers. In: 31st ACM Symposium on Parallelism in Algorithms and Architectures. ACM; 2019:145-154. doi:10.1145/3323165.3323201
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[59]
2019 | Published | Conference Paper | IST-REx-ID: 6676 | OA
Alistarh D-A, Aspnes J, Ellen F, Gelashvili R, Zhu L. Why extension-based proofs fail. In: Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing. ACM; 2019:986-996. doi:10.1145/3313276.3316407
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[58]
2019 | Published | Conference Paper | IST-REx-ID: 7201 | OA
Renggli C, Ashkboos S, Aghagolzadeh M, Alistarh D-A, Hoefler T. SparCML: High-performance sparse communication for machine learning. In: International Conference for High Performance Computing, Networking, Storage and Analysis, SC. ACM; 2019. doi:10.1145/3295500.3356222
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[57]
2018 | Published | Journal Article | IST-REx-ID: 6001
Alistarh D-A, Leiserson W, Matveev A, Shavit N. ThreadScan: Automatic and scalable memory reclamation. ACM Transactions on Parallel Computing. 2018;4(4). doi:10.1145/3201897
View | Files available | DOI
 
[56]
2018 | Published | Conference Paper | IST-REx-ID: 6031
Stojanov A, Smith TM, Alistarh D-A, Puschel M. Fast quantized arithmetic on x86: Trading compute for data movement. In: 2018 IEEE International Workshop on Signal Processing Systems. Vol 2018-October. IEEE; 2018. doi:10.1109/SiPS.2018.8598402
View | DOI | WoS
 
[55]
2018 | Published | Conference Paper | IST-REx-ID: 6558 | OA
Alistarh D-A, Allen-Zhu Z, Li J. Byzantine stochastic gradient descent. In: Advances in Neural Information Processing Systems. Vol 2018. Neural Information Processing Systems Foundation; 2018:4613-4623.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 
[54]
2018 | Published | Conference Paper | IST-REx-ID: 7116 | OA
Grubic D, Tam L, Alistarh D-A, Zhang C. 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. OpenProceedings; 2018:145-156. doi:10.5441/002/EDBT.2018.14
[Published Version] View | Files available | DOI
 
[53]
2018 | Published | Conference Paper | IST-REx-ID: 7123 | OA
Alistarh D-A, Aspnes J, Gelashvili R. Space-optimal majority in population protocols. In: Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms. ACM; 2018:2221-2239. doi:10.1137/1.9781611975031.144
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[52]
2018 | Published | Conference Paper | IST-REx-ID: 5963 | OA
Alistarh D-A, Brown TA, Kopinsky J, Nadiradze G. Relaxed schedulers can efficiently parallelize iterative algorithms. In: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. ACM; 2018:377-386. doi:10.1145/3212734.3212756
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[51]
2018 | Published | Conference Paper | IST-REx-ID: 5962 | OA
Alistarh D-A, De Sa C, Konstantinov NH. The convergence of stochastic gradient descent in asynchronous shared memory. In: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. ACM; 2018:169-178. doi:10.1145/3212734.3212763
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[50]
2018 | Published | Conference Paper | IST-REx-ID: 5961
Alistarh D-A. A brief tutorial on distributed and concurrent machine learning. In: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. ACM; 2018:487-488. doi:10.1145/3212734.3212798
View | DOI | WoS
 
[49]
2018 | Published | Conference Paper | IST-REx-ID: 5964 | OA
Aksenov V, Alistarh D-A, Kuznetsov P. Brief Announcement: Performance prediction for coarse-grained locking. In: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. ACM; 2018:411-413. doi:10.1145/3212734.3212785
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[48]
2018 | Published | Conference Paper | IST-REx-ID: 5965 | OA
Alistarh D-A, Brown TA, Kopinsky J, Li JZ, Nadiradze G. Distributionally linearizable data structures. In: Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18. ACM; 2018:133-142. doi:10.1145/3210377.3210411
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[47]
2018 | Published | Conference Paper | IST-REx-ID: 5966 | OA
Alistarh D-A, Haider SK, Kübler R, Nadiradze G. The transactional conflict problem. In: Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18. ACM; 2018:383-392. doi:10.1145/3210377.3210406
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[46]
2018 | Published | Conference Paper | IST-REx-ID: 6589 | OA
Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli C. The convergence of sparsified gradient methods. In: Advances in Neural Information Processing Systems 31. Vol Volume 2018. Neural Information Processing Systems Foundation; 2018:5973-5983.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
[45]
2018 | Published | Conference Paper | IST-REx-ID: 7812 | OA
Polino A, Pascanu R, Alistarh D-A. Model compression via distillation and quantization. In: 6th International Conference on Learning Representations. ; 2018.
[Published Version] View | Files available | arXiv
 
[44]
2018 | Published | Journal Article | IST-REx-ID: 536 | OA
Alistarh D-A, Aspnes J, King V, Saia J. Communication-efficient randomized consensus. Distributed Computing. 2018;31(6):489-501. doi:10.1007/s00446-017-0315-1
[Published Version] View | Files available | DOI | WoS
 
[43]
2017 | Published | Conference Paper | IST-REx-ID: 788 | OA
Alistarh D-A, Dudek B, Kosowski A, Soloveichik D, Uznański P. Robust detection in leak-prone population protocols. In: Vol 10467 LNCS. Springer; 2017:155-171. doi:10.1007/978-3-319-66799-7_11
View | DOI | Download None (ext.) | arXiv
 
[42]
2017 | Published | Conference Paper | IST-REx-ID: 789
Alistarh D-A, Leiserson W, Matveev A, Shavit N. Forkscan: Conservative memory reclamation for modern operating systems. In: ACM; 2017:483-498. doi:10.1145/3064176.3064214
View | DOI
 
[41]
2017 | Published | Conference Paper | IST-REx-ID: 790
Kara K, Alistarh D-A, Alonso G, Mutlu O, Zhang C. FPGA-accelerated dense linear machine learning: A precision-convergence trade-off. In: IEEE; 2017:160-167. doi:10.1109/FCCM.2017.39
View | DOI
 
[40]
2017 | Published | Conference Paper | IST-REx-ID: 791 | OA
Alistarh D-A, Kopinsky J, Li J, Nadiradze G. The power of choice in priority scheduling. In: Proceedings of the ACM Symposium on Principles of Distributed Computing. Vol Part F129314. ACM; 2017:283-292. doi:10.1145/3087801.3087810
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS | arXiv
 
[39]
2017 | Published | Conference Paper | IST-REx-ID: 787 | OA
Alistarh D-A, Aspnes J, Eisenstat D, Rivest R, Gelashvili R. Time-space trade-offs in population protocols. In: Proceedings of the 2017 Annual ACM-SIAM Symposium on Discrete Algorithms. SIAM; 2017:2560-2579. doi:10.1137/1.9781611974782.169
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[38]
2017 | Published | Conference Paper | IST-REx-ID: 487
Baig G, Radunovic B, Alistarh D-A, Balkwill M, Karagiannis T, Qiu L. Towards unlicensed cellular networks in TV white spaces. In: Proceedings of the 2017 13th International Conference on Emerging Networking EXperiments and Technologies. ACM; 2017:2-14. doi:10.1145/3143361.3143367
View | DOI | WoS
 
[37]
2017 | Published | Conference Paper | IST-REx-ID: 432 | OA
Zhang H, Li J, Kara K, Alistarh D-A, Liu J, Zhang C. ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning. In: Proceedings of Machine Learning Research. Vol 70. ML Research Press; 2017:4035-4043.
[Submitted Version] View | Files available | WoS
 
[36]
2017 | Published | Conference Paper | IST-REx-ID: 431 | OA
Alistarh D-A, Grubic D, Li J, Tomioka R, Vojnović M. QSGD: Communication-efficient SGD via gradient quantization and encoding. In: Vol 2017. Neural Information Processing Systems Foundation; 2017:1710-1721.
[Submitted Version] View | Download Submitted Version (ext.) | WoS | arXiv
 
[35]
2016 | Published | Conference Paper | IST-REx-ID: 785
Haider S, Hasenplaugh W, Alistarh D-A. Lease/Release: Architectural support for scaling contended data structures. In: Vol 12-16-March-2016. ACM; 2016. doi:10.1145/2851141.2851155
View | DOI
 
[34]
2016 | Published | Journal Article | IST-REx-ID: 786 | OA
Alistarh D-A, Censor Hillel K, Shavit N. Are lock free concurrent algorithms practically wait free . Journal of the ACM. 2016;63(4). doi:10.1145/2903136
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[33]
2015 | Published | Conference Paper | IST-REx-ID: 776
Alistarh D-A, Kopinsky J, Li J, Shavit N. The SprayList: A scalable relaxed priority queue. In: Vol 2015-January. ACM; 2015:11-20. doi:10.1145/2688500.2688523
View | DOI
 
[32]
2015 | Published | Conference Paper | IST-REx-ID: 777
Alistarh D-A, Iglesias J, Vojnović M. Streaming min-max hypergraph partitioning. In: Vol 2015-January. Neural Information Processing Systems; 2015:1900-1908.
View | Download None (ext.)
 
[31]
2015 | Published | Conference Paper | IST-REx-ID: 778 | OA
Alistarh D-A, Kopinsky J, Kuznetsov P, Ravi S, Shavit N. Inherent limitations of hybrid transactional memory. In: Vol 9363. Springer; 2015:185-199. doi:10.1007/978-3-662-48653-5_13
View | DOI | Download None (ext.) | arXiv
 
[30]
2015 | Published | Conference Paper | IST-REx-ID: 779
Alistarh D-A, Matveev A, Leiserson W, Shavit N. ThreadScan: Automatic and scalable memory reclamation. In: Vol 2015-June. ACM; 2015:123-132. doi:10.1145/2755573.2755600
View | Files available | DOI
 
[29]
2015 | Published | Conference Paper | IST-REx-ID: 780 | OA
Alistarh D-A, Gelashvili R. Polylogarithmic-time leader election in population protocols. In: Vol 9135. Springer; 2015:479-491. doi: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 D-A, Gelashvili R, Vojnović M. Fast and exact majority in population protocols. In: Vol 2015-July. ACM; 2015:47-56. doi:10.1145/2767386.2767429
View | DOI
 
[27]
2015 | Published | Conference Paper | IST-REx-ID: 782
Alistarh D-A, Sauerwald T, Vojnović M. Lock-Free algorithms under stochastic schedulers. In: Vol 2015-July. ACM; 2015:251-260. doi:10.1145/2767386.2767430
View | DOI
 
[26]
2015 | Published | Conference Paper | IST-REx-ID: 783 | OA
Alistarh D-A, Gelashvili R, Vladu A. How to elect a leader faster than a tournament. In: Vol 2015-July. ACM; 2015:365-374. doi:10.1145/2767386.2767420
View | DOI | Download None (ext.)
 
[25]
2015 | Published | Conference Paper | IST-REx-ID: 784
Alistarh D-A, Ballani H, Costa P, et al. A high-radix, low-latency optical switch for data centers. In: ACM; 2015:367-368. doi:10.1145/2785956.2790035
View | DOI
 
[24]
2014 | Published | Conference Paper | IST-REx-ID: 768
Alistarh D-A, Aspnes J, Bender M, Gelashvili R, Gilbert S. Dynamic task allocation in asynchronous shared memory. In: SIAM; 2014:416-435. doi:10.1137/1.9781611973402.31
View | DOI
 
[23]
2014 | Published | Journal Article | IST-REx-ID: 769
Alistarh D-A, Aspnes J, Censor Hillel K, Gilbert S, Guerraoui R. Tight bounds for asynchronous renaming. Journal of the ACM. 2014;61(3). doi:10.1145/2597630
View | DOI
 
[22]
2014 | Published | Conference Paper | IST-REx-ID: 770
Alistarh D-A, Eugster P, Herlihy M, Matveev A, Shavit N. StackTrack: An automated transactional approach to concurrent memory reclamation. In: ACM; 2014. doi:10.1145/2592798.2592808
View | DOI
 
[21]
2014 | Published | Conference Paper | IST-REx-ID: 771
Alistarh D-A, Denysyuk O, Rodrígues L, Shavit N. Balls-into-Leaves: Sub-logarithmic renaming in synchronous message-passing systems. In: ACM; 2014:232-241. doi:10.1145/2611462.2611499
View | DOI
 
[20]
2014 | Published | Conference Paper | IST-REx-ID: 772 | OA
Alistarh D-A, Censor Hillel K, Shavit N. Are lock-free concurrent algorithms practically wait-free? In: ACM; 2014:714-723. doi:10.1145/2591796.2591836
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[19]
2014 | Published | Conference Paper | IST-REx-ID: 773
Alistarh D-A, Aspnes J, King V, Saia J. Communication-efficient randomized consensus. In: Kuhn F, ed. Vol 8784. Springer; 2014:61-75. doi:10.1007/978-3-662-45174-8_5
View | DOI
 
[18]
2014 | Published | Conference Paper | IST-REx-ID: 774
Alistarh D-A, Censor Hille K, Shavit N. Brief announcement: Are lock-free concurrent algorithms practically wait-free? In: ACM; 2014:50-52. doi:10.1145/2611462.2611502
View | DOI
 
[17]
2014 | Published | Conference Paper | IST-REx-ID: 775 | OA
Alistarh D-A, Kopinsky J, Matveev A, Shavit N. The levelarray: A fast, practical long-lived renaming algorithm. In: IEEE; 2014:348-357. doi:10.1109/ICDCS.2014.43
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[16]
2013 | Published | Conference Paper | IST-REx-ID: 765
Alistarh D-A, Aspnes J, Giakkoupis G, Woelfel P. Randomized loose renaming in O(loglogn) time. In: ACM; 2013:200-209. doi:10.1145/2484239.2484240
View | DOI
 
[15]
2012 | Published | Conference Paper | IST-REx-ID: 762
Alistarh D-A, Guerraoui R, Kuznetsov P, Losa G. On the cost of composing shared-memory algorithms. In: ACM; 2012:298-307. doi:10.1145/2312005.2312057
View | DOI
 
[14]
2012 | Published | Conference Paper | IST-REx-ID: 763
Alistarh D-A, Attiya H, Guerraoui R, Travers C. Early deciding synchronous renaming in O(log f) rounds or less. In: Vol 7355 LNCS. Springer; 2012:195-206. doi:10.1007/978-3-642-31104-8_17
View | DOI
 
[13]
2012 | Published | Journal Article | IST-REx-ID: 764
Alistarh D-A, Gilbert S, Guerraoui R, Travers C. Of choices, failures and asynchrony: the many faces of set agreement. Algorithmica (New York). 2012;62(1-2):595-629. doi:10.1007/s00453-011-9581-7
View | DOI
 
[12]
2012 | Published | Conference Paper | IST-REx-ID: 766
Alistarh D-A, Bender M, Gilbert S, Guerraoui R. How to allocate tasks asynchronously. In: IEEE; 2012:331-340. doi:10.1109/FOCS.2012.41
View | DOI
 
[11]
2012 | Published | Journal Article | IST-REx-ID: 767
Alistarh D-A, Gilbert S, Guerraoui R, Travers C. Generating Fast Indulgent Algorithms. Theory of Computing Systems. 2012;51(4):404-424. doi:10.1007/s00224-012-9407-2
View | DOI
 
[10]
2011 | Published | Conference Paper | IST-REx-ID: 757
Alistarh D-A, Gilbert S, Guerraoui R, Travers C. Generating fast indulgent algorithms. In: Vol 6522 LNCS. Springer; 2011:41-52. doi:10.1007/978-3-642-17679-1_4
View | DOI
 
[9]
2011 | Published | Conference Paper | IST-REx-ID: 759
Alistarh D-A, Aspnes J, Gilbert S, Guerraoui R. The complexity of renaming. In: IEEE; 2011:718-727. doi:10.1109/FOCS.2011.66
View | DOI
 
[8]
2011 | Published | Conference Paper | IST-REx-ID: 760
Alistarh D-A, Aspnes J. Sub-logarithmic test-and-set against a weak adversary. In: Vol 6950 LNCS. Springer; 2011:97-109. doi:10.1007/978-3-642-24100-0_7
View | DOI
 
[7]
2011 | Published | Conference Paper | IST-REx-ID: 761
Alistarh D-A, Aspnes J, Censor Hillel K, Gilbert S, Zadimoghaddam M. Optimal-time adaptive strong renaming, with applications to counting. In: ACM; 2011:239-248. doi:10.1145/1993806.1993850
View | DOI
 
[6]
2010 | Published | Conference Paper | IST-REx-ID: 754
Alistarh D-A, Attiya H, Gilbert S, Giurgiu A, Guerraoui R. Fast randomized test-and-set and renaming. In: Vol 6343 LNCS. Springer; 2010:94-108. doi:10.1007/978-3-642-15763-9_9
View | DOI
 
[5]
2010 | Published | Conference Paper | IST-REx-ID: 755
Alistarh D-A, Gilbert S, Guerraoui R, Zadimoghaddam M. How efficient can gossip be? (On the cost of resilient information exchange). In: Vol 6199 LNCS. Springer; 2010:115-126. doi:10.1007/978-3-642-14162-1_10
View | DOI
 
[4]
2010 | Published | Conference Paper | IST-REx-ID: 756
Alistarh D-A, Gilbert S, Guerraoui R, Milošević Ž, Newport C. Securing every bit: Authenticated broadcast in radio networks. In: ACM; 2010:50-59. doi:10.1145/1810479.1810489
View | DOI
 
[3]
2010 | Published | Conference Paper | IST-REx-ID: 758
Alistarh D-A, Gilbert S, Guerraoui R, Travers C. Brief announcement: New bounds for partially synchronous set agreement. In: Vol 6343 LNCS. Springer; 2010:404-405. doi:10.1007/978-3-642-15763-9_40
View | DOI
 
[2]
2009 | Published | Conference Paper | IST-REx-ID: 752
Alistarh D-A, Gilbert S, Guerraoui R, Travers C. Of choices, failures and asynchrony: the many faces of set agreement. In: Vol 5878 LNCS. Springer; 2009:943-953. doi:10.1007/978-3-642-10631-6_95
View | DOI
 
[1]
2008 | Published | Conference Paper | IST-REx-ID: 753
Alistarh D-A, Gilbert S, Guerraoui R, Travers C. How to solve consensus in the smallest window of synchrony. In: Vol 5218 LNCS. Springer; 2008:32-46. doi:10.1007/978-3-540-87779-0_3
View | DOI
 
Marked Publication(s)

Search

Filter Publications

Display / Sort

Citation Style: AMA

Export / Embed