Dan-Adrian Alistarh

Alistarh Group

141 Publications

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[141]
2024 | Published | Conference Paper | IST-REx-ID: 17093 | OA
Zakerinia, H., Talaei, S., Nadiradze, G., & Alistarh, D.-A. (2024). Communication-efficient federated learning with data and client heterogeneity. In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 3448–3456). Valencia, Spain: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[140]
2024 | Published | Conference Paper | IST-REx-ID: 17332 | OA
Kokorin, I., Yudov, V., Aksenov, V., & Alistarh, D.-A. (2024). Wait-free trees with asymptotically-efficient range queries. In 2024 IEEE International Parallel and Distributed Processing Symposium (pp. 169–179). San Francisco, CA, United States: IEEE. https://doi.org/10.1109/IPDPS57955.2024.00023
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[139]
2024 | Published | Conference Paper | IST-REx-ID: 15011 | OA
Kurtic, E., Hoefler, T., & Alistarh, D.-A. (2024). How to prune your language model: Recovering accuracy on the “Sparsity May Cry” benchmark. In Proceedings of Machine Learning Research (Vol. 234, pp. 542–553). Hongkong, China: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[138]
2024 | Published | Conference Paper | IST-REx-ID: 18070
Chatterjee, B., Kungurtsev, V., & Alistarh, D.-A. (2024). Federated SGD with local asynchrony. In Proceedings of the 44th International Conference on Distributed Computing Systems (pp. 857–868). Jersey City, NJ, United States: IEEE. https://doi.org/10.1109/ICDCS60910.2024.00084
View | DOI
 
[137]
2024 | Published | Conference Paper | IST-REx-ID: 18113 | OA
Egiazarian, V., Panferov, A., Kuznedelev, D., Frantar, E., Babenko, A., & Alistarh, D.-A. (2024). Extreme compression of large language models via additive quantization. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 12284–12303). Vienna, Austria: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[136]
2024 | Published | Conference Paper | IST-REx-ID: 18117 | OA
Nikdan, M., Tabesh, S., Crncevic, E., & Alistarh, D.-A. (2024). RoSA: Accurate parameter-efficient fine-tuning via robust adaptation. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 38187–38206). Vienna, Austria: ML Research Press.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[135]
2024 | Published | Conference Paper | IST-REx-ID: 18975 | OA
Modoranu, I.-V., Kalinov, A., Kurtic, E., Frantar, E., & Alistarh, D.-A. (2024). Error feedback can accurately compress preconditioners. In 41st International Conference on Machine Learning (Vol. 235, pp. 35910–35933). Vienna, Austria: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[134]
2024 | Published | Conference Paper | IST-REx-ID: 18977 | OA
Dettmers, T., Svirschevski, R. A., Egiazarian, V., Kuznedelev, D., Frantar, E., Ashkboos, S., … Alistarh, D.-A. (2024). SpQR: A sparse-quantized representation for near-lossless LLM weight compression. In 12th International Conference on Learning Representations. Vienna, Austria: OpenReview.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[133]
2024 | Published | Conference Paper | IST-REx-ID: 19519 | OA
Malinovskii, Vladimir, PV-tuning: Beyond straight-through estimation for extreme LLM compression. 38th Conference on Neural Information Processing Systems 37. 2024
[Published Version] View | Files available | arXiv
 
[132]
2024 | Published | Conference Paper | IST-REx-ID: 19511 | OA
Ashkboos, Saleh, QuaRot: Outlier-free 4-bit inference in rotated LLMs. 38th Conference on Neural Information Processing Systems 37. 2024
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[131]
2024 | Published | Conference Paper | IST-REx-ID: 18061 | OA
Frantar, E., & Alistarh, D.-A. (2024). QMoE: Sub-1-bit compression of trillion parameter models. In P. Gibbons, G. Pekhimenko, & C. De Sa (Eds.), Proceedings of Machine Learning and Systems (Vol. 6). Santa Clara, CA, USA.
[Published Version] View | Files available | Download Published Version (ext.)
 
[130]
2024 | Published | Conference Paper | IST-REx-ID: 18062 | OA
Frantar, E., Ruiz, C. R., Houlsby, N., Alistarh, D.-A., & Evci, U. (2024). Scaling laws for sparsely-connected foundation models. In The Twelfth International Conference on Learning Representations. Vienna, Austria.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[129]
2024 | Published | Conference Paper | IST-REx-ID: 17456 | OA
Markov, I., Alimohammadi, K., Frantar, E., & Alistarh, D.-A. (2024). L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning. In P. Gibbons, G. Pekhimenko, & C. De Sa (Eds.), Proceedings of Machine Learning and Systems (Vol. 6). Athens, Greece: Association for Computing Machinery.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[128]
2024 | Published | Conference Paper | IST-REx-ID: 17329 | OA
Alistarh, D.-A., Chatterjee, K., Karrabi, M., & Lazarsfeld, J. M. (2024). Game dynamics and equilibrium computation in the population protocol model. In Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing (pp. 40–49). Nantes, France: Association for Computing Machinery. https://doi.org/10.1145/3662158.3662768
[Published Version] View | Files available | DOI
 
[127]
2024 | Published | Conference Paper | IST-REx-ID: 18976 | OA
Islamov, R., Safaryan, M., & Alistarh, D.-A. (2024). AsGrad: A sharp unified analysis of asynchronous-SGD algorithms. In Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 649–657). Valencia, Spain: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[126]
2024 | Published | Conference Paper | IST-REx-ID: 19518 | OA
Wu, Diyuan, The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information. 38th Conference on Neural Information Processing Systems 37. 2024
[Preprint] View | Download Preprint (ext.) | arXiv
 
[125]
2024 | Published | Conference Paper | IST-REx-ID: 19510 | OA
Modoranu, Ionut-Vlad, MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence. 38th Conference on Neural Information Processing Systems 37. 2024
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[124]
2024 | Published | Conference Paper | IST-REx-ID: 18121 | OA
Moakhar, A. S., Iofinova, E. B., Frantar, E., & Alistarh, D.-A. (2024). SPADE: Sparsity-guided debugging for deep neural networks. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 45955–45987). Vienna, Austria: ML Research Press.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[123]
2023 | Published | Journal Article | IST-REx-ID: 13179 | OA
Koval, N., Khalanskiy, D., & Alistarh, D.-A. (2023). CQS: A formally-verified framework for fair and abortable synchronization. Proceedings of the ACM on Programming Languages. Association for Computing Machinery . https://doi.org/10.1145/3591230
[Published Version] View | Files available | DOI
 
[122]
2023 | Published | Conference Paper | IST-REx-ID: 13262 | OA
Fedorov, A., Hashemi, D., Nadiradze, G., & Alistarh, D.-A. (2023). Provably-efficient and internally-deterministic parallel Union-Find. In Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures (pp. 261–271). Orlando, FL, United States: Association for Computing Machinery. https://doi.org/10.1145/3558481.3591082
[Published Version] View | Files available | DOI | arXiv
 
[121]
2023 | Published | Conference Paper | IST-REx-ID: 14260 | OA
Koval, N., Fedorov, A., Sokolova, M., Tsitelov, D., & Alistarh, D.-A. (2023). Lincheck: A practical framework for testing concurrent data structures on JVM. In 35th International Conference on Computer Aided Verification (Vol. 13964, pp. 156–169). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-37706-8_8
[Published Version] View | Files available | DOI
 
[120]
2023 | Published | Journal Article | IST-REx-ID: 12330 | OA
Aksenov, V., Alistarh, D.-A., Drozdova, A., & Mohtashami, A. (2023). The splay-list: A distribution-adaptive concurrent skip-list. Distributed Computing. Springer Nature. https://doi.org/10.1007/s00446-022-00441-x
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[119]
2023 | Published | Conference Paper | IST-REx-ID: 12735 | OA
Koval, N., Alistarh, D.-A., & Elizarov, R. (2023). Fast and scalable channels in Kotlin Coroutines. In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 107–118). Montreal, QC, Canada: Association for Computing Machinery. https://doi.org/10.1145/3572848.3577481
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[118]
2023 | Research Data Reference | IST-REx-ID: 14995 | OA
Koval, N., Fedorov, A., Sokolova, M., Tsitelov, D., & Alistarh, D.-A. (2023). Lincheck: A practical framework for testing concurrent data structures on JVM. Zenodo. https://doi.org/10.5281/ZENODO.7877757
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[117]
2023 | Published | Conference Paper | IST-REx-ID: 14460 | OA
Nikdan, M., Pegolotti, T., Iofinova, E. B., Kurtic, E., & Alistarh, D.-A. (2023). 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, pp. 26215–26227). Honolulu, Hawaii, HI, United States: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[116]
2023 | Published | Conference Paper | IST-REx-ID: 17378 | OA
Frantar, E., Ashkboos, S., Hoefler, T., & Alistarh, D.-A. (2023). OPTQ: Accurate post-training quantization for generative pre-trained transformers. In 11th International Conference on Learning Representations . Kigali, Rwanda: International Conference on Learning Representations.
[Published Version] View | Files available
 
[115]
2023 | Published | Conference Paper | IST-REx-ID: 14458 | OA
Frantar, E., & Alistarh, D.-A. (2023). SparseGPT: Massive language models can be accurately pruned in one-shot. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 10323–10337). Honolulu, Hawaii, HI, United States: ML Research Press.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[114]
2023 | Published | Conference Paper | IST-REx-ID: 14461 | OA
Markov, I., Vladu, A., Guo, Q., & Alistarh, D.-A. (2023). Quantized distributed training of large models with convergence guarantees. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 24020–24044). Honolulu, Hawaii, HI, United States: ML Research Press.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[113]
2023 | Published | Journal Article | IST-REx-ID: 14364 | OA
Alistarh, D.-A., Aspnes, J., Ellen, F., Gelashvili, R., & Zhu, L. (2023). Why extension-based proofs fail. SIAM Journal on Computing. Society for Industrial and Applied Mathematics. https://doi.org/10.1137/20M1375851
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[112]
2023 | Published | Journal Article | IST-REx-ID: 12566 | OA
Alistarh, D.-A., Ellen, F., & Rybicki, J. (2023). Wait-free approximate agreement on graphs. Theoretical Computer Science. Elsevier. https://doi.org/10.1016/j.tcs.2023.113733
[Published Version] View | Files available | DOI | WoS
 
[111]
2023 | Published | Conference Paper | IST-REx-ID: 13053 | OA
Krumes, A., Vladu, A., Kurtic, E., Lampert, C., & Alistarh, D.-A. (2023). CrAM: A Compression-Aware Minimizer. In 11th International Conference on Learning Representations . Kigali, Rwanda : OpenReview.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[110]
2023 | Published | Conference Paper | IST-REx-ID: 15363 | OA
Safaryan, M., Krumes, A., & Alistarh, D.-A. (2023). Knowledge distillation performs partial variance reduction. In 36th Conference on Neural Information Processing Systems (Vol. 36). New Orleans, LA, United States.
[Published Version] View | Files available | arXiv
 
[109]
2023 | Published | Conference Paper | IST-REx-ID: 14771 | OA
Iofinova, E. B., Krumes, A., & Alistarh, D.-A. (2023). Bias in pruned vision models: In-depth analysis and countermeasures. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 24364–24373). Vancouver, BC, Canada: IEEE. https://doi.org/10.1109/cvpr52729.2023.02334
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[108]
2022 | Published | Conference Paper | IST-REx-ID: 11181 | OA
Brown, T. A., Sigouin, W., & Alistarh, D.-A. (2022). 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 (pp. 385–399). Seoul, Republic of Korea: Association for Computing Machinery. https://doi.org/10.1145/3503221.3508410
[Published Version] View | Files available | DOI | WoS
 
[107]
2022 | Published | Conference Paper | IST-REx-ID: 17088 | OA
Kurtic, E., Campos, D., Nguyen, T., Frantar, E., Kurtz, M., Fineran, B., … Alistarh, D.-A. (2022). 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 (pp. 4163–4181). Abu Dhabi, United Arab Emirates: Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.emnlp-main.279
[Published Version] View | Files available | DOI | arXiv
 
[106]
2022 | Published | Conference Paper | IST-REx-ID: 11180 | OA
Postnikova, A., Koval, N., Nadiradze, G., & Alistarh, D.-A. (2022). 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 (pp. 353–367). Seoul, Republic of Korea: Association for Computing Machinery. https://doi.org/10.1145/3503221.3508432
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[105]
2022 | Research Data Reference | IST-REx-ID: 13076 | OA
Postnikova, A., Koval, N., Nadiradze, G., & Alistarh, D.-A. (2022). Multi-queues can be state-of-the-art priority schedulers. Zenodo. https://doi.org/10.5281/ZENODO.5733408
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[104]
2022 | Published | Conference Paper | IST-REx-ID: 11844 | OA
Alistarh, D.-A., Rybicki, J., & Voitovych, S. (2022). Near-optimal leader election in population protocols on graphs. In Proceedings of the Annual ACM Symposium on Principles of Distributed Computing (pp. 246–256). Salerno, Italy: Association for Computing Machinery. https://doi.org/10.1145/3519270.3538435
[Published Version] View | Files available | DOI | arXiv
 
[103]
2022 | Published | Conference Paper | IST-REx-ID: 17087 | OA
Frantar, E., Singh, S. P., & Alistarh, D.-A. (2022). Optimal brain compression: A framework for accurate post-training quantization and pruning. In 36th Conference on Neural Information Processing Systems (Vol. 35). New Orleans, LA, United States: ML Research Press.
[Submitted Version] View | Files available | arXiv
 
[102]
2022 | Published | Conference Paper | IST-REx-ID: 17059 | OA
Frantar, E., & Alistarh, D.-A. (2022). SPDY: Accurate pruning with speedup guarantees. In 39th International Conference on Machine Learning (Vol. 162, pp. 6726–6743). Baltimore, MD, United States: ML Research Press.
[Published Version] View | Files available | WoS
 
[101]
2022 | Published | Conference Paper | IST-REx-ID: 12780 | OA
Markov, I., Ramezanikebrya, H., & Alistarh, D.-A. (2022). CGX: Adaptive system support for communication-efficient deep learning. In Proceedings of the 23rd ACM/IFIP International Middleware Conference (pp. 241–254). Quebec, QC, Canada: Association for Computing Machinery. https://doi.org/10.1145/3528535.3565248
[Published Version] View | Files available | DOI | arXiv
 
[100]
2022 | Published | Conference Paper | IST-REx-ID: 11184 | OA
Alistarh, D.-A., Gelashvili, R., & Rybicki, J. (2022). Fast graphical population protocols. In Q. Bramas, V. Gramoli, & A. Milani (Eds.), 25th International Conference on Principles of Distributed Systems (Vol. 217). Strasbourg, France: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.OPODIS.2021.14
[Published Version] View | Files available | DOI | arXiv
 
[99]
2022 | Published | Journal Article | IST-REx-ID: 8286 | OA
Alistarh, Dan-Adrian, Dynamic averaging load balancing on cycles. Algorithmica 84 (4). 2022
[Published Version] View | Files available | DOI | WoS | arXiv
 
[98]
2022 | Published | Conference Paper | IST-REx-ID: 12299 | OA
Iofinova, E. B., Krumes, A., Kurtz, M., & Alistarh, D.-A. (2022). How well do sparse ImageNet models transfer? In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12256–12266). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01195
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[97]
2021 | Published | Conference Paper | IST-REx-ID: 10853 | OA
Fedorov, A., Koval, N., & Alistarh, D.-A. (2021). A scalable concurrent algorithm for dynamic connectivity. In Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures (pp. 208–220). Virtual, Online: Association for Computing Machinery. https://doi.org/10.1145/3409964.3461810
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[96]
2021 | Published | Journal Article | IST-REx-ID: 10180 | OA
Hoefler, T., Alistarh, D.-A., Ben-Nun, T., Dryden, N., & Peste, E.-A. (2021). Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks. Journal of Machine Learning Research. Journal of Machine Learning Research.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[95]
2021 | Published | Conference Paper | IST-REx-ID: 9823 | OA
Alistarh, D.-A., Ellen, F., & Rybicki, J. (2021). Wait-free approximate agreement on graphs. In Structural Information and Communication Complexity (Vol. 12810, pp. 87–105). Wrocław, Poland: Springer Nature. https://doi.org/10.1007/978-3-030-79527-6_6
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[94]
2021 | Published | Conference Paper | IST-REx-ID: 9951
Alistarh, D.-A., Töpfer, M., & Uznański, P. (2021). Comparison dynamics in population protocols. In Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing (pp. 55–65). Virtual, Italy: Association for Computing Machinery. https://doi.org/10.1145/3465084.3467915
View | DOI | WoS
 
[93]
2021 | Published | Journal Article | IST-REx-ID: 9571 | OA
Ramezani-Kebrya, Ali, NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization. Journal of Machine Learning Research 22 (114). 2021
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[92]
2021 | Published | Conference Paper | IST-REx-ID: 10218 | OA
Alistarh, D.-A., Gelashvili, R., & Rybicki, J. (2021). Brief announcement: Fast graphical population protocols. In 35th International Symposium on Distributed Computing (Vol. 209). Freiburg, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.DISC.2021.43
[Published Version] View | Files available | DOI | arXiv
 
[91]
2021 | Published | Conference Paper | IST-REx-ID: 11463 | OA
Frantar, Elias, M-FAC: Efficient matrix-free approximations of second-order information. 35th Conference on Neural Information Processing Systems 34. 2021
[Published Version] View | Download Published Version (ext.) | arXiv
 
[90]
2021 | Published | Conference Paper | IST-REx-ID: 10217 | OA
Alistarh, Dan-Adrian, Lower bounds for shared-memory leader election under bounded write contention. 35th International Symposium on Distributed Computing 209. 2021
[Published Version] View | Files available | DOI
 
[89]
2021 | Published | Conference Paper | IST-REx-ID: 11464 | OA
Alistarh, Dan-Adrian, Towards tight communication lower bounds for distributed optimisation. 35th Conference on Neural Information Processing Systems 34. 2021
[Published Version] View | Download Published Version (ext.) | arXiv
 
[88]
2021 | Published | Journal Article | IST-REx-ID: 8723 | OA
Li, Shigang, Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging. IEEE Transactions on Parallel and Distributed Systems 32 (7). 2021
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[87]
2021 | Published | Conference Paper | IST-REx-ID: 9620 | OA
Alistarh, Dan-Adrian, Collecting coupons is faster with friends. Structural Information and Communication Complexity 12810. 2021
[Preprint] View | Files available | DOI
 
[86]
2021 | Published | Conference Paper | IST-REx-ID: 9543 | OA
Davies, Peter, New bounds for distributed mean estimation and variance reduction. 9th International Conference on Learning Representations. 2021
[Published Version] View | Download Published Version (ext.) | arXiv
 
[85]
2021 | Published | Conference Paper | IST-REx-ID: 13147 | OA
Alimisis, Foivos, Communication-efficient distributed optimization with quantized preconditioners. Proceedings of the 38th International Conference on Machine Learning 139. 2021
[Published Version] View | Files available | arXiv
 
[84]
2021 | Published | Conference Paper | IST-REx-ID: 11436 | OA
Kungurtsev, V., Egan, M., Chatterjee, B., & Alistarh, D.-A. (2021). Asynchronous optimization methods for efficient training of deep neural networks with guarantees. In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (Vol. 35, pp. 8209–8216). Virtual, Online: AAAI Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[83]
2021 | Published | Conference Paper | IST-REx-ID: 10435 | OA
Nadiradze, G., Sabour, A., Davies, P., Li, S., & Alistarh, D.-A. (2021). Asynchronous decentralized SGD with quantized and local updates. In 35th Conference on Neural Information Processing Systems. Sydney, Australia: Neural Information Processing Systems Foundation.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[82]
2021 | Published | Conference Paper | IST-REx-ID: 11452 | OA
Alimisis, F., Davies, P., Vandereycken, B., & Alistarh, D.-A. (2021). Distributed principal component analysis with limited communication. In Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems (Vol. 4, pp. 2823–2834). Virtual, Online: Neural Information Processing Systems Foundation.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[81]
2021 | Published | Conference Paper | IST-REx-ID: 10432 | OA
Nadiradze, G., Markov, I., Chatterjee, B., Kungurtsev, V., & Alistarh, D.-A. (2021). Elastic consistency: A practical consistency model for distributed stochastic gradient descent. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, pp. 9037–9045). Virtual.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[80]
2021 | Published | Conference Paper | IST-REx-ID: 11458 | OA
Krumes, A., Iofinova, E. B., Vladu, A., & Alistarh, D.-A. (2021). AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 8557–8570). Virtual, Online: Neural Information Processing Systems Foundation.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[79]
2020 | Published | Conference Paper | IST-REx-ID: 7635
Koval, N., Sokolova, M., Fedorov, A., Alistarh, D.-A., & Tsitelov, D. (2020). Testing concurrency on the JVM with Lincheck. In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP (pp. 423–424). San Diego, CA, United States: Association for Computing Machinery. https://doi.org/10.1145/3332466.3374503
View | DOI
 
[78]
2020 | Published | Conference Paper | IST-REx-ID: 8191
Alistarh, D.-A., Brown, T. A., & Singhal, N. (2020). Memory tagging: Minimalist synchronization for scalable concurrent data structures. In Annual ACM Symposium on Parallelism in Algorithms and Architectures (pp. 37–49). Virtual Event, United States: Association for Computing Machinery. https://doi.org/10.1145/3350755.3400213
View | DOI | WoS
 
[77]
2020 | Published | Conference Paper | IST-REx-ID: 8383
Alistarh, D.-A., Aspnes, J., Ellen, F., Gelashvili, R., & Zhu, L. (2020). Brief Announcement: Why Extension-Based Proofs Fail. In Proceedings of the 39th Symposium on Principles of Distributed Computing (pp. 54–56). Virtual, Italy: Association for Computing Machinery. https://doi.org/10.1145/3382734.3405743
View | DOI
 
[76]
2020 | Conference Paper | IST-REx-ID: 9415 | OA
Kurtz, M., Kopinsky, J., Gelashvili, R., Matveev, A., Carr, J., Goin, M., … Alistarh, D.-A. (2020). Inducing and exploiting activation sparsity for fast neural network inference. In 37th International Conference on Machine Learning, ICML 2020 (Vol. 119, pp. 5533–5543). Online.
[Published Version] View | Files available
 
[75]
2020 | Published | Conference Paper | IST-REx-ID: 7605 | OA
Alistarh, Dan-Adrian, In search of the fastest concurrent union-find algorithm. 23rd International Conference on Principles of Distributed Systems 153. 2020
[Published Version] View | Files available | DOI | arXiv
 
[74]
2020 | Published | Journal Article | IST-REx-ID: 8268 | OA
Gurel, Nezihe Merve, Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications. IEEE Transactions on Signal Processing 68. 2020
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[73]
2020 | Published | Conference Paper | IST-REx-ID: 8725 | OA
Aksenov, V., Alistarh, D.-A., Drozdova, A., & Mohtashami, A. (2020). The splay-list: A distribution-adaptive concurrent skip-list. In 34th International Symposium on Distributed Computing (Vol. 179, p. 3:1-3:18). Freiburg, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.DISC.2020.3
[Published Version] View | Files available | DOI | arXiv
 
[72]
2020 | Published | Conference Paper | IST-REx-ID: 8722 | OA
Li, S., Tal Ben-Nun, T. B.-N., Girolamo, S. D., Alistarh, D.-A., & Hoefler, T. (2020). 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 (pp. 45–61). San Diego, CA, United States: Association for Computing Machinery. https://doi.org/10.1145/3332466.3374528
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[71]
2020 | Published | Conference Paper | IST-REx-ID: 7636 | OA
Brown, T. A., Prokopec, A., & Alistarh, D.-A. (2020). Non-blocking interpolation search trees with doubly-logarithmic running time. In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 276–291). San Diego, CA, United States: Association for Computing Machinery. https://doi.org/10.1145/3332466.3374542
[Published Version] View | DOI | Download Published Version (ext.) | WoS
 
[70]
2020 | Published | Conference Paper | IST-REx-ID: 15086 | OA
Faghri, F., Tabrizian, I., Markov, I., Alistarh, D.-A., Roy, D., & Ramezani-Kebrya, A. (2020). Adaptive gradient quantization for data-parallel SGD. In Advances in Neural Information Processing Systems (Vol. 33). Vancouver, Canada: Neural Information Processing Systems Foundation.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[69]
2020 | Published | Conference Paper | IST-REx-ID: 9632 | OA
Singh, Sidak Pal, WoodFisher: Efficient second-order approximation for neural network compression. 33. 2020
[Published Version] View | Download Published Version (ext.) | arXiv
 
[68]
2020 | Published | Conference Paper | IST-REx-ID: 9631 | OA
Aksenov, Vitaly, Scalable belief propagation via relaxed scheduling. 33. 2020
[Published Version] View | Download Published Version (ext.) | arXiv
 
[67]
2020 | Published | Conference Paper | IST-REx-ID: 8724 | OA
Konstantinov, N. H., Frantar, E., Alistarh, D.-A., & Lampert, C. (2020). On the sample complexity of adversarial multi-source PAC learning. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 5416–5425). Online: ML Research Press.
[Published Version] View | Files available | arXiv
 
[66]
2020 | Published | Conference Paper | IST-REx-ID: 15077 | OA
Alistarh, Dan-Adrian, Dynamic averaging load balancing on cycles. 47th International Colloquium on Automata, Languages, and Programming 168. 2020
[Published Version] View | Files available | DOI | arXiv
 
[65]
2019 | Published | Conference Paper | IST-REx-ID: 7437 | OA
Yu, C., Tang, H., Renggli, C., Kassing, S., Singla, A., Alistarh, D.-A., … Liu, J. (2019). Distributed learning over unreliable networks. In 36th International Conference on Machine Learning, ICML 2019 (Vol. 2019–June, pp. 12481–12512). Long Beach, CA, United States: IMLS.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
[64]
2019 | Published | Conference Paper | IST-REx-ID: 7122
Khirirat, S., Johansson, M., & Alistarh, D.-A. (2019). Gradient compression for communication-limited convex optimization. In 2018 IEEE Conference on Decision and Control. Miami Beach, FL, United States: IEEE. https://doi.org/10.1109/cdc.2018.8619625
View | DOI | WoS
 
[63]
2019 | Published | Conference Paper | IST-REx-ID: 7228
Koval, N., Alistarh, D.-A., & Elizarov, R. (2019). Scalable FIFO channels for programming via communicating sequential processes. In 25th Anniversary of Euro-Par (Vol. 11725, pp. 317–333). Göttingen, Germany: Springer Nature. https://doi.org/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. (2019). Lock-free channels for programming via communicating sequential processes. Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming (pp. 417–418). Washington, NY, United States: ACM. https://doi.org/10.1145/3293883.3297000
View | DOI | WoS
 
[61]
2019 | Published | Conference Paper | IST-REx-ID: 7542 | OA
Wendler, C., Alistarh, D.-A., & Püschel, M. (2019). Powerset convolutional neural networks (Vol. 32, pp. 927–938). Presented at the NIPS: Conference on Neural Information Processing Systems, Vancouver, Canada: Neural Information Processing Systems Foundation.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 
[60]
2019 | Published | Conference Paper | IST-REx-ID: 6676 | OA
Alistarh, Dan-Adrian, Why extension-based proofs fail. Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing. 2019
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[59]
2019 | Published | Conference Paper | IST-REx-ID: 6673 | OA
Alistarh, Dan-Adrian, Efficiency guarantees for parallel incremental algorithms under relaxed schedulers. 31st ACM Symposium on Parallelism in Algorithms and Architectures. 2019
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[58]
2019 | Published | Conference Paper | IST-REx-ID: 7201 | OA
Renggli, Cedric, SparCML: High-performance sparse communication for machine learning. International Conference for High Performance Computing, Networking, Storage and Analysis, SC. 2019
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[57]
2018 | Published | Journal Article | IST-REx-ID: 6001
Alistarh, D.-A., Leiserson, W., Matveev, A., & Shavit, N. (2018). ThreadScan: Automatic and scalable memory reclamation. ACM Transactions on Parallel Computing. Association for Computing Machinery. https://doi.org/10.1145/3201897
View | Files available | DOI
 
[56]
2018 | Published | Conference Paper | IST-REx-ID: 6031
Stojanov, A., Smith, T. M., Alistarh, D.-A., & Puschel, M. (2018). Fast quantized arithmetic on x86: Trading compute for data movement. In 2018 IEEE International Workshop on Signal Processing Systems (Vol. 2018–October). Cape Town, South Africa: IEEE. https://doi.org/10.1109/SiPS.2018.8598402
View | DOI | WoS
 
[55]
2018 | Published | Conference Paper | IST-REx-ID: 7812 | OA
Polino, A., Pascanu, R., & Alistarh, D.-A. (2018). Model compression via distillation and quantization. In 6th International Conference on Learning Representations. Vancouver, Canada.
[Published Version] View | Files available | arXiv
 
[54]
2018 | Published | Conference Paper | IST-REx-ID: 6558 | OA
Alistarh, D.-A., Allen-Zhu, Z., & Li, J. (2018). Byzantine stochastic gradient descent. In Advances in Neural Information Processing Systems (Vol. 2018, pp. 4613–4623). Montreal, Canada: Neural Information Processing Systems Foundation.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 
[53]
2018 | Published | Conference Paper | IST-REx-ID: 7116 | OA
Grubic, D., Tam, L., Alistarh, D.-A., & Zhang, C. (2018). 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 (pp. 145–156). Vienna, Austria: OpenProceedings. https://doi.org/10.5441/002/EDBT.2018.14
[Published Version] View | Files available | DOI
 
[52]
2018 | Published | Conference Paper | IST-REx-ID: 7123 | OA
Alistarh, D.-A., Aspnes, J., & Gelashvili, R. (2018). Space-optimal majority in population protocols. In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 2221–2239). New Orleans, LA, United States: ACM. https://doi.org/10.1137/1.9781611975031.144
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[51]
2018 | Published | Conference Paper | IST-REx-ID: 5963 | OA
Alistarh, Dan-Adrian, Relaxed schedulers can efficiently parallelize iterative algorithms. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC '18. 2018
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[50]
2018 | Published | Conference Paper | IST-REx-ID: 5962 | OA
Alistarh, Dan-Adrian, The convergence of stochastic gradient descent in asynchronous shared memory. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC '18. 2018
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[49]
2018 | Published | Conference Paper | IST-REx-ID: 5961
Alistarh, Dan-Adrian, A brief tutorial on distributed and concurrent machine learning. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC '18. 2018
View | DOI | WoS
 
[48]
2018 | Published | Conference Paper | IST-REx-ID: 5964 | OA
Aksenov, Vitaly, Brief Announcement: Performance prediction for coarse-grained locking. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC '18. 2018
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[47]
2018 | Published | Conference Paper | IST-REx-ID: 5966 | OA
Alistarh, Dan-Adrian, The transactional conflict problem. Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA '18. 2018
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[46]
2018 | Published | Conference Paper | IST-REx-ID: 5965 | OA
Alistarh, Dan-Adrian, Distributionally linearizable data structures. Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA '18. 2018
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[45]
2018 | Published | Conference Paper | IST-REx-ID: 6589 | OA
Alistarh, D.-A., Hoefler, T., Johansson, M., Konstantinov, N. H., Khirirat, S., & Renggli, C. (2018). The convergence of sparsified gradient methods. In Advances in Neural Information Processing Systems 31 (Vol. Volume 2018, pp. 5973–5983). Montreal, Canada: Neural Information Processing Systems Foundation.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
[44]
2018 | Published | Journal Article | IST-REx-ID: 536 | OA
Alistarh, D.-A., Aspnes, J., King, V., & Saia, J. (2018). Communication-efficient randomized consensus. Distributed Computing. Springer. https://doi.org/10.1007/s00446-017-0315-1
[Published Version] View | Files available | DOI
 
[43]
2017 | Published | Conference Paper | IST-REx-ID: 487
Baig, G., Radunovic, B., Alistarh, D.-A., Balkwill, M., Karagiannis, T., & Qiu, L. (2017). Towards unlicensed cellular networks in TV white spaces. In Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies (pp. 2–14). Incheon, South Korea: ACM. https://doi.org/10.1145/3143361.3143367
View | DOI
 
[42]
2017 | Published | Conference Paper | IST-REx-ID: 787 | OA
Alistarh, D.-A., Aspnes, J., Eisenstat, D., Rivest, R., & Gelashvili, R. (2017). Time-space trade-offs in population protocols (pp. 2560–2579). Presented at the SODA: Symposium on Discrete Algorithms, SIAM. https://doi.org/doi.org/10.1137/1.9781611974782.169
View | DOI | Download None (ext.)
 
[41]
2017 | Published | Conference Paper | IST-REx-ID: 788 | OA
Alistarh, D.-A., Dudek, B., Kosowski, A., Soloveichik, D., & Uznański, P. (2017). Robust detection in leak-prone population protocols (Vol. 10467 LNCS, pp. 155–171). Presented at the DNA Computing and Molecular Programming, Springer. https://doi.org/10.1007/978-3-319-66799-7_11
View | DOI | Download None (ext.) | arXiv
 
[40]
2017 | Published | Conference Paper | IST-REx-ID: 789
Alistarh, D.-A., Leiserson, W., Matveev, A., & Shavit, N. (2017). Forkscan: Conservative memory reclamation for modern operating systems (pp. 483–498). Presented at the EuroSys: European Conference on Computer Systems, ACM. https://doi.org/10.1145/3064176.3064214
View | DOI
 
[39]
2017 | Published | Conference Paper | IST-REx-ID: 790
Kara, K., Alistarh, D.-A., Alonso, G., Mutlu, O., & Zhang, C. (2017). FPGA-accelerated dense linear machine learning: A precision-convergence trade-off (pp. 160–167). Presented at the FCCM: Field-Programmable Custom Computing Machines, IEEE. https://doi.org/10.1109/FCCM.2017.39
View | DOI
 
[38]
2017 | Published | Conference Paper | IST-REx-ID: 791 | OA
Alistarh, D.-A., Kopinsky, J., Li, J., & Nadiradze, G. (2017). The power of choice in priority scheduling. In Proceedings of the ACM Symposium on Principles of Distributed Computing (Vol. Part F129314, pp. 283–292). Washington, WA, USA: ACM. https://doi.org/10.1145/3087801.3087810
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[37]
2017 | Published | Conference Paper | IST-REx-ID: 432 | OA
Zhang, Hantian, ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning. Proceedings of Machine Learning Research 70. 2017
[Submitted Version] View | Files available
 
[36]
2017 | Published | Conference Paper | IST-REx-ID: 431 | OA
Alistarh, Dan-Adrian, QSGD: Communication-efficient SGD via gradient quantization and encoding. 2017. 2017
[Submitted Version] View | Download Submitted Version (ext.) | arXiv
 
[35]
2016 | Published | Conference Paper | IST-REx-ID: 785
Haider, S., Hasenplaugh, W., & Alistarh, D.-A. (2016). Lease/Release: Architectural support for scaling contended data structures (Vol. 12-16-March-2016). Presented at the PPoPP: Principles and Practice of Parallel Pogramming, ACM. https://doi.org/10.1145/2851141.2851155
View | DOI
 
[34]
2016 | Published | Journal Article | IST-REx-ID: 786 | OA
Alistarh, D.-A., Censor Hillel, K., & Shavit, N. (2016). Are lock free concurrent algorithms practically wait free . Journal of the ACM. ACM. https://doi.org/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. (2015). The SprayList: A scalable relaxed priority queue (Vol. 2015–January, pp. 11–20). Presented at the PPoPP: Principles and Practice of Parallel Pogramming, ACM. https://doi.org/10.1145/2688500.2688523
View | DOI
 
[32]
2015 | Published | Conference Paper | IST-REx-ID: 777
Alistarh, D.-A., Iglesias, J., & Vojnović, M. (2015). Streaming min-max hypergraph partitioning (Vol. 2015–January, pp. 1900–1908). Presented at the NIPS: Neural Information Processing Systems, Neural Information Processing Systems.
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. (2015). Inherent limitations of hybrid transactional memory (Vol. 9363, pp. 185–199). Presented at the DISC: Distributed Computing, Springer. https://doi.org/10.1007/978-3-662-48653-5_13
View | DOI | Download None (ext.) | arXiv
 
[30]
2015 | Published | Conference Paper | IST-REx-ID: 779
Alistarh, D.-A., Matveev, A., Leiserson, W., & Shavit, N. (2015). ThreadScan: Automatic and scalable memory reclamation (Vol. 2015–June, pp. 123–132). Presented at the SPAA: Symposium on Parallelism in Algorithms and Architectures, ACM. https://doi.org/10.1145/2755573.2755600
View | Files available | DOI
 
[29]
2015 | Published | Conference Paper | IST-REx-ID: 780 | OA
Alistarh, D.-A., & Gelashvili, R. (2015). Polylogarithmic-time leader election in population protocols (Vol. 9135, pp. 479–491). Presented at the ICALP: International Colloquium on Automota, Languages and Programming, Springer. https://doi.org/10.1007/978-3-662-47666-6_38
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[28]
2015 | Published | Conference Paper | IST-REx-ID: 781
Alistarh, D.-A., Gelashvili, R., & Vojnović, M. (2015). Fast and exact majority in population protocols (Vol. 2015–July, pp. 47–56). Presented at the PODC: Principles of Distributed Computing, ACM. https://doi.org/10.1145/2767386.2767429
View | DOI
 
[27]
2015 | Published | Conference Paper | IST-REx-ID: 782
Alistarh, D.-A., Sauerwald, T., & Vojnović, M. (2015). Lock-Free algorithms under stochastic schedulers (Vol. 2015–July, pp. 251–260). Presented at the PODC: Principles of Distributed Computing, ACM. https://doi.org/10.1145/2767386.2767430
View | DOI
 
[26]
2015 | Published | Conference Paper | IST-REx-ID: 783 | OA
Alistarh, D.-A., Gelashvili, R., & Vladu, A. (2015). How to elect a leader faster than a tournament (Vol. 2015–July, pp. 365–374). Presented at the PODC: Principles of Distributed Computing, ACM. https://doi.org/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., Funnell, A., Benjamin, J., Watts, P., & Thomsen, B. (2015). A high-radix, low-latency optical switch for data centers (pp. 367–368). Presented at the SIGCOMM: Special Interest Group on Data Communication, London, United Kindgdom: ACM. https://doi.org/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. (2014). Dynamic task allocation in asynchronous shared memory (pp. 416–435). Presented at the SODA: Symposium on Discrete Algorithms, SIAM. https://doi.org/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. (2014). Tight bounds for asynchronous renaming. Journal of the ACM. ACM. https://doi.org/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. (2014). StackTrack: An automated transactional approach to concurrent memory reclamation. Presented at the EuroSys: European Conference on Computer Systems, ACM. https://doi.org/10.1145/2592798.2592808
View | DOI
 
[21]
2014 | Published | Conference Paper | IST-REx-ID: 771
Alistarh, D.-A., Denysyuk, O., Rodrígues, L., & Shavit, N. (2014). Balls-into-Leaves: Sub-logarithmic renaming in synchronous message-passing systems (pp. 232–241). Presented at the PODC: Principles of Distributed Computing, ACM. https://doi.org/10.1145/2611462.2611499
View | DOI
 
[20]
2014 | Published | Conference Paper | IST-REx-ID: 772 | OA
Alistarh, D.-A., Censor Hillel, K., & Shavit, N. (2014). Are lock-free concurrent algorithms practically wait-free? (pp. 714–723). Presented at the STOC: Symposium on Theory of Computing, ACM. https://doi.org/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. (2014). Communication-efficient randomized consensus. In F. Kuhn (Ed.) (Vol. 8784, pp. 61–75). Presented at the DISC: Distributed Computing, Austin, USA: Springer. https://doi.org/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. (2014). Brief announcement: Are lock-free concurrent algorithms practically wait-free? (pp. 50–52). Presented at the PODC: Principles of Distributed Computing, ACM. https://doi.org/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. (2014). The levelarray: A fast, practical long-lived renaming algorithm (pp. 348–357). Presented at the ICDCS: International Conference on Distributed Computing Systems, IEEE. https://doi.org/10.1109/ICDCS.2014.43
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[16]
2013 | Published | Conference Paper | IST-REx-ID: 765
Alistarh, D.-A., Aspnes, J., Giakkoupis, G., & Woelfel, P. (2013). Randomized loose renaming in O(loglogn) time (pp. 200–209). Presented at the PODC: Principles of Distributed Computing, ACM. https://doi.org/10.1145/2484239.2484240
View | DOI
 
[15]
2012 | Published | Conference Paper | IST-REx-ID: 762
Alistarh, D.-A., Guerraoui, R., Kuznetsov, P., & Losa, G. (2012). On the cost of composing shared-memory algorithms (pp. 298–307). Presented at the SPAA: Symposium on Parallelism in Algorithms and Architectures, ACM. https://doi.org/10.1145/2312005.2312057
View | DOI
 
[14]
2012 | Published | Conference Paper | IST-REx-ID: 763
Alistarh, D.-A., Attiya, H., Guerraoui, R., & Travers, C. (2012). Early deciding synchronous renaming in O(log f) rounds or less (Vol. 7355 LNCS, pp. 195–206). Presented at the SIROCCO: Structural Information and Communication Complexity, Springer. https://doi.org/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. (2012). Of choices, failures and asynchrony: the many faces of set agreement. Algorithmica (New York). Springer. https://doi.org/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. (2012). How to allocate tasks asynchronously (pp. 331–340). Presented at the FOCS: Foundations of Computer Science, IEEE. https://doi.org/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. (2012). Generating Fast Indulgent Algorithms. Theory of Computing Systems. Elsevier. https://doi.org/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. (2011). Generating fast indulgent algorithms (Vol. 6522 LNCS, pp. 41–52). Presented at the ICDCN: International Conference on Distributed Computing and Networking, Springer. https://doi.org/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. (2011). The complexity of renaming (pp. 718–727). Presented at the FOCS: Foundations of Computer Science, IEEE. https://doi.org/10.1109/FOCS.2011.66
View | DOI
 
[8]
2011 | Published | Conference Paper | IST-REx-ID: 760
Alistarh, D.-A., & Aspnes, J. (2011). Sub-logarithmic test-and-set against a weak adversary (Vol. 6950 LNCS, pp. 97–109). Presented at the DISC: Distributed Computing, Springer. https://doi.org/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. (2011). Optimal-time adaptive strong renaming, with applications to counting (pp. 239–248). Presented at the PODC: Principles of Distributed Computing, ACM. https://doi.org/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. (2010). Fast randomized test-and-set and renaming (Vol. 6343 LNCS, pp. 94–108). Presented at the DISC: Distributed Computing, Springer. https://doi.org/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. (2010). How efficient can gossip be? (On the cost of resilient information exchange) (Vol. 6199 LNCS, pp. 115–126). Presented at the ICALP: International Colloquium on Automota, Languages and Programming, Springer. https://doi.org/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. (2010). Securing every bit: Authenticated broadcast in radio networks (pp. 50–59). Presented at the SPAA: Symposium on Parallelism in Algorithms and Architectures, ACM. https://doi.org/10.1145/1810479.1810489
View | DOI
 
[3]
2010 | Published | Conference Paper | IST-REx-ID: 758
Alistarh, D.-A., Gilbert, S., Guerraoui, R., & Travers, C. (2010). Brief announcement: New bounds for partially synchronous set agreement (Vol. 6343 LNCS, pp. 404–405). Presented at the DISC: Distributed Computing, Springer. https://doi.org/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. (2009). Of choices, failures and asynchrony: the many faces of set agreement (Vol. 5878 LNCS, pp. 943–953). Presented at the ISAAC: International Symposium on Algorithms and Computation, Springer. https://doi.org/10.1007/978-3-642-10631-6_95
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[1]
2008 | Published | Conference Paper | IST-REx-ID: 753
Alistarh, D.-A., Gilbert, S., Guerraoui, R., & Travers, C. (2008). How to solve consensus in the smallest window of synchrony (Vol. 5218 LNCS, pp. 32–46). Presented at the DISC: Distributed Computing, Springer. https://doi.org/10.1007/978-3-540-87779-0_3
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[141]
2024 | Published | Conference Paper | IST-REx-ID: 17093 | OA
Zakerinia, H., Talaei, S., Nadiradze, G., & Alistarh, D.-A. (2024). Communication-efficient federated learning with data and client heterogeneity. In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 3448–3456). Valencia, Spain: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[140]
2024 | Published | Conference Paper | IST-REx-ID: 17332 | OA
Kokorin, I., Yudov, V., Aksenov, V., & Alistarh, D.-A. (2024). Wait-free trees with asymptotically-efficient range queries. In 2024 IEEE International Parallel and Distributed Processing Symposium (pp. 169–179). San Francisco, CA, United States: IEEE. https://doi.org/10.1109/IPDPS57955.2024.00023
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[139]
2024 | Published | Conference Paper | IST-REx-ID: 15011 | OA
Kurtic, E., Hoefler, T., & Alistarh, D.-A. (2024). How to prune your language model: Recovering accuracy on the “Sparsity May Cry” benchmark. In Proceedings of Machine Learning Research (Vol. 234, pp. 542–553). Hongkong, China: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[138]
2024 | Published | Conference Paper | IST-REx-ID: 18070
Chatterjee, B., Kungurtsev, V., & Alistarh, D.-A. (2024). Federated SGD with local asynchrony. In Proceedings of the 44th International Conference on Distributed Computing Systems (pp. 857–868). Jersey City, NJ, United States: IEEE. https://doi.org/10.1109/ICDCS60910.2024.00084
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[137]
2024 | Published | Conference Paper | IST-REx-ID: 18113 | OA
Egiazarian, V., Panferov, A., Kuznedelev, D., Frantar, E., Babenko, A., & Alistarh, D.-A. (2024). Extreme compression of large language models via additive quantization. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 12284–12303). Vienna, Austria: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[136]
2024 | Published | Conference Paper | IST-REx-ID: 18117 | OA
Nikdan, M., Tabesh, S., Crncevic, E., & Alistarh, D.-A. (2024). RoSA: Accurate parameter-efficient fine-tuning via robust adaptation. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 38187–38206). Vienna, Austria: ML Research Press.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[135]
2024 | Published | Conference Paper | IST-REx-ID: 18975 | OA
Modoranu, I.-V., Kalinov, A., Kurtic, E., Frantar, E., & Alistarh, D.-A. (2024). Error feedback can accurately compress preconditioners. In 41st International Conference on Machine Learning (Vol. 235, pp. 35910–35933). Vienna, Austria: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[134]
2024 | Published | Conference Paper | IST-REx-ID: 18977 | OA
Dettmers, T., Svirschevski, R. A., Egiazarian, V., Kuznedelev, D., Frantar, E., Ashkboos, S., … Alistarh, D.-A. (2024). SpQR: A sparse-quantized representation for near-lossless LLM weight compression. In 12th International Conference on Learning Representations. Vienna, Austria: OpenReview.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[133]
2024 | Published | Conference Paper | IST-REx-ID: 19519 | OA
Malinovskii, Vladimir, PV-tuning: Beyond straight-through estimation for extreme LLM compression. 38th Conference on Neural Information Processing Systems 37. 2024
[Published Version] View | Files available | arXiv
 
[132]
2024 | Published | Conference Paper | IST-REx-ID: 19511 | OA
Ashkboos, Saleh, QuaRot: Outlier-free 4-bit inference in rotated LLMs. 38th Conference on Neural Information Processing Systems 37. 2024
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[131]
2024 | Published | Conference Paper | IST-REx-ID: 18061 | OA
Frantar, E., & Alistarh, D.-A. (2024). QMoE: Sub-1-bit compression of trillion parameter models. In P. Gibbons, G. Pekhimenko, & C. De Sa (Eds.), Proceedings of Machine Learning and Systems (Vol. 6). Santa Clara, CA, USA.
[Published Version] View | Files available | Download Published Version (ext.)
 
[130]
2024 | Published | Conference Paper | IST-REx-ID: 18062 | OA
Frantar, E., Ruiz, C. R., Houlsby, N., Alistarh, D.-A., & Evci, U. (2024). Scaling laws for sparsely-connected foundation models. In The Twelfth International Conference on Learning Representations. Vienna, Austria.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[129]
2024 | Published | Conference Paper | IST-REx-ID: 17456 | OA
Markov, I., Alimohammadi, K., Frantar, E., & Alistarh, D.-A. (2024). L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning. In P. Gibbons, G. Pekhimenko, & C. De Sa (Eds.), Proceedings of Machine Learning and Systems (Vol. 6). Athens, Greece: Association for Computing Machinery.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[128]
2024 | Published | Conference Paper | IST-REx-ID: 17329 | OA
Alistarh, D.-A., Chatterjee, K., Karrabi, M., & Lazarsfeld, J. M. (2024). Game dynamics and equilibrium computation in the population protocol model. In Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing (pp. 40–49). Nantes, France: Association for Computing Machinery. https://doi.org/10.1145/3662158.3662768
[Published Version] View | Files available | DOI
 
[127]
2024 | Published | Conference Paper | IST-REx-ID: 18976 | OA
Islamov, R., Safaryan, M., & Alistarh, D.-A. (2024). AsGrad: A sharp unified analysis of asynchronous-SGD algorithms. In Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 649–657). Valencia, Spain: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[126]
2024 | Published | Conference Paper | IST-REx-ID: 19518 | OA
Wu, Diyuan, The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information. 38th Conference on Neural Information Processing Systems 37. 2024
[Preprint] View | Download Preprint (ext.) | arXiv
 
[125]
2024 | Published | Conference Paper | IST-REx-ID: 19510 | OA
Modoranu, Ionut-Vlad, MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence. 38th Conference on Neural Information Processing Systems 37. 2024
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[124]
2024 | Published | Conference Paper | IST-REx-ID: 18121 | OA
Moakhar, A. S., Iofinova, E. B., Frantar, E., & Alistarh, D.-A. (2024). SPADE: Sparsity-guided debugging for deep neural networks. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 45955–45987). Vienna, Austria: ML Research Press.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[123]
2023 | Published | Journal Article | IST-REx-ID: 13179 | OA
Koval, N., Khalanskiy, D., & Alistarh, D.-A. (2023). CQS: A formally-verified framework for fair and abortable synchronization. Proceedings of the ACM on Programming Languages. Association for Computing Machinery . https://doi.org/10.1145/3591230
[Published Version] View | Files available | DOI
 
[122]
2023 | Published | Conference Paper | IST-REx-ID: 13262 | OA
Fedorov, A., Hashemi, D., Nadiradze, G., & Alistarh, D.-A. (2023). Provably-efficient and internally-deterministic parallel Union-Find. In Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures (pp. 261–271). Orlando, FL, United States: Association for Computing Machinery. https://doi.org/10.1145/3558481.3591082
[Published Version] View | Files available | DOI | arXiv
 
[121]
2023 | Published | Conference Paper | IST-REx-ID: 14260 | OA
Koval, N., Fedorov, A., Sokolova, M., Tsitelov, D., & Alistarh, D.-A. (2023). Lincheck: A practical framework for testing concurrent data structures on JVM. In 35th International Conference on Computer Aided Verification (Vol. 13964, pp. 156–169). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-37706-8_8
[Published Version] View | Files available | DOI
 
[120]
2023 | Published | Journal Article | IST-REx-ID: 12330 | OA
Aksenov, V., Alistarh, D.-A., Drozdova, A., & Mohtashami, A. (2023). The splay-list: A distribution-adaptive concurrent skip-list. Distributed Computing. Springer Nature. https://doi.org/10.1007/s00446-022-00441-x
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[119]
2023 | Published | Conference Paper | IST-REx-ID: 12735 | OA
Koval, N., Alistarh, D.-A., & Elizarov, R. (2023). Fast and scalable channels in Kotlin Coroutines. In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 107–118). Montreal, QC, Canada: Association for Computing Machinery. https://doi.org/10.1145/3572848.3577481
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[118]
2023 | Research Data Reference | IST-REx-ID: 14995 | OA
Koval, N., Fedorov, A., Sokolova, M., Tsitelov, D., & Alistarh, D.-A. (2023). Lincheck: A practical framework for testing concurrent data structures on JVM. Zenodo. https://doi.org/10.5281/ZENODO.7877757
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[117]
2023 | Published | Conference Paper | IST-REx-ID: 14460 | OA
Nikdan, M., Pegolotti, T., Iofinova, E. B., Kurtic, E., & Alistarh, D.-A. (2023). 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, pp. 26215–26227). Honolulu, Hawaii, HI, United States: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[116]
2023 | Published | Conference Paper | IST-REx-ID: 17378 | OA
Frantar, E., Ashkboos, S., Hoefler, T., & Alistarh, D.-A. (2023). OPTQ: Accurate post-training quantization for generative pre-trained transformers. In 11th International Conference on Learning Representations . Kigali, Rwanda: International Conference on Learning Representations.
[Published Version] View | Files available
 
[115]
2023 | Published | Conference Paper | IST-REx-ID: 14458 | OA
Frantar, E., & Alistarh, D.-A. (2023). SparseGPT: Massive language models can be accurately pruned in one-shot. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 10323–10337). Honolulu, Hawaii, HI, United States: ML Research Press.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[114]
2023 | Published | Conference Paper | IST-REx-ID: 14461 | OA
Markov, I., Vladu, A., Guo, Q., & Alistarh, D.-A. (2023). Quantized distributed training of large models with convergence guarantees. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 24020–24044). Honolulu, Hawaii, HI, United States: ML Research Press.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[113]
2023 | Published | Journal Article | IST-REx-ID: 14364 | OA
Alistarh, D.-A., Aspnes, J., Ellen, F., Gelashvili, R., & Zhu, L. (2023). Why extension-based proofs fail. SIAM Journal on Computing. Society for Industrial and Applied Mathematics. https://doi.org/10.1137/20M1375851
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[112]
2023 | Published | Journal Article | IST-REx-ID: 12566 | OA
Alistarh, D.-A., Ellen, F., & Rybicki, J. (2023). Wait-free approximate agreement on graphs. Theoretical Computer Science. Elsevier. https://doi.org/10.1016/j.tcs.2023.113733
[Published Version] View | Files available | DOI | WoS
 
[111]
2023 | Published | Conference Paper | IST-REx-ID: 13053 | OA
Krumes, A., Vladu, A., Kurtic, E., Lampert, C., & Alistarh, D.-A. (2023). CrAM: A Compression-Aware Minimizer. In 11th International Conference on Learning Representations . Kigali, Rwanda : OpenReview.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[110]
2023 | Published | Conference Paper | IST-REx-ID: 15363 | OA
Safaryan, M., Krumes, A., & Alistarh, D.-A. (2023). Knowledge distillation performs partial variance reduction. In 36th Conference on Neural Information Processing Systems (Vol. 36). New Orleans, LA, United States.
[Published Version] View | Files available | arXiv
 
[109]
2023 | Published | Conference Paper | IST-REx-ID: 14771 | OA
Iofinova, E. B., Krumes, A., & Alistarh, D.-A. (2023). Bias in pruned vision models: In-depth analysis and countermeasures. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 24364–24373). Vancouver, BC, Canada: IEEE. https://doi.org/10.1109/cvpr52729.2023.02334
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[108]
2022 | Published | Conference Paper | IST-REx-ID: 11181 | OA
Brown, T. A., Sigouin, W., & Alistarh, D.-A. (2022). 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 (pp. 385–399). Seoul, Republic of Korea: Association for Computing Machinery. https://doi.org/10.1145/3503221.3508410
[Published Version] View | Files available | DOI | WoS
 
[107]
2022 | Published | Conference Paper | IST-REx-ID: 17088 | OA
Kurtic, E., Campos, D., Nguyen, T., Frantar, E., Kurtz, M., Fineran, B., … Alistarh, D.-A. (2022). 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 (pp. 4163–4181). Abu Dhabi, United Arab Emirates: Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.emnlp-main.279
[Published Version] View | Files available | DOI | arXiv
 
[106]
2022 | Published | Conference Paper | IST-REx-ID: 11180 | OA
Postnikova, A., Koval, N., Nadiradze, G., & Alistarh, D.-A. (2022). 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 (pp. 353–367). Seoul, Republic of Korea: Association for Computing Machinery. https://doi.org/10.1145/3503221.3508432
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[105]
2022 | Research Data Reference | IST-REx-ID: 13076 | OA
Postnikova, A., Koval, N., Nadiradze, G., & Alistarh, D.-A. (2022). Multi-queues can be state-of-the-art priority schedulers. Zenodo. https://doi.org/10.5281/ZENODO.5733408
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[104]
2022 | Published | Conference Paper | IST-REx-ID: 11844 | OA
Alistarh, D.-A., Rybicki, J., & Voitovych, S. (2022). Near-optimal leader election in population protocols on graphs. In Proceedings of the Annual ACM Symposium on Principles of Distributed Computing (pp. 246–256). Salerno, Italy: Association for Computing Machinery. https://doi.org/10.1145/3519270.3538435
[Published Version] View | Files available | DOI | arXiv
 
[103]
2022 | Published | Conference Paper | IST-REx-ID: 17087 | OA
Frantar, E., Singh, S. P., & Alistarh, D.-A. (2022). Optimal brain compression: A framework for accurate post-training quantization and pruning. In 36th Conference on Neural Information Processing Systems (Vol. 35). New Orleans, LA, United States: ML Research Press.
[Submitted Version] View | Files available | arXiv
 
[102]
2022 | Published | Conference Paper | IST-REx-ID: 17059 | OA
Frantar, E., & Alistarh, D.-A. (2022). SPDY: Accurate pruning with speedup guarantees. In 39th International Conference on Machine Learning (Vol. 162, pp. 6726–6743). Baltimore, MD, United States: ML Research Press.
[Published Version] View | Files available | WoS
 
[101]
2022 | Published | Conference Paper | IST-REx-ID: 12780 | OA
Markov, I., Ramezanikebrya, H., & Alistarh, D.-A. (2022). CGX: Adaptive system support for communication-efficient deep learning. In Proceedings of the 23rd ACM/IFIP International Middleware Conference (pp. 241–254). Quebec, QC, Canada: Association for Computing Machinery. https://doi.org/10.1145/3528535.3565248
[Published Version] View | Files available | DOI | arXiv
 
[100]
2022 | Published | Conference Paper | IST-REx-ID: 11184 | OA
Alistarh, D.-A., Gelashvili, R., & Rybicki, J. (2022). Fast graphical population protocols. In Q. Bramas, V. Gramoli, & A. Milani (Eds.), 25th International Conference on Principles of Distributed Systems (Vol. 217). Strasbourg, France: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.OPODIS.2021.14
[Published Version] View | Files available | DOI | arXiv
 
[99]
2022 | Published | Journal Article | IST-REx-ID: 8286 | OA
Alistarh, Dan-Adrian, Dynamic averaging load balancing on cycles. Algorithmica 84 (4). 2022
[Published Version] View | Files available | DOI | WoS | arXiv
 
[98]
2022 | Published | Conference Paper | IST-REx-ID: 12299 | OA
Iofinova, E. B., Krumes, A., Kurtz, M., & Alistarh, D.-A. (2022). How well do sparse ImageNet models transfer? In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12256–12266). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01195
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[97]
2021 | Published | Conference Paper | IST-REx-ID: 10853 | OA
Fedorov, A., Koval, N., & Alistarh, D.-A. (2021). A scalable concurrent algorithm for dynamic connectivity. In Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures (pp. 208–220). Virtual, Online: Association for Computing Machinery. https://doi.org/10.1145/3409964.3461810
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[96]
2021 | Published | Journal Article | IST-REx-ID: 10180 | OA
Hoefler, T., Alistarh, D.-A., Ben-Nun, T., Dryden, N., & Peste, E.-A. (2021). Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks. Journal of Machine Learning Research. Journal of Machine Learning Research.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[95]
2021 | Published | Conference Paper | IST-REx-ID: 9823 | OA
Alistarh, D.-A., Ellen, F., & Rybicki, J. (2021). Wait-free approximate agreement on graphs. In Structural Information and Communication Complexity (Vol. 12810, pp. 87–105). Wrocław, Poland: Springer Nature. https://doi.org/10.1007/978-3-030-79527-6_6
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[94]
2021 | Published | Conference Paper | IST-REx-ID: 9951
Alistarh, D.-A., Töpfer, M., & Uznański, P. (2021). Comparison dynamics in population protocols. In Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing (pp. 55–65). Virtual, Italy: Association for Computing Machinery. https://doi.org/10.1145/3465084.3467915
View | DOI | WoS
 
[93]
2021 | Published | Journal Article | IST-REx-ID: 9571 | OA
Ramezani-Kebrya, Ali, NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization. Journal of Machine Learning Research 22 (114). 2021
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[92]
2021 | Published | Conference Paper | IST-REx-ID: 10218 | OA
Alistarh, D.-A., Gelashvili, R., & Rybicki, J. (2021). Brief announcement: Fast graphical population protocols. In 35th International Symposium on Distributed Computing (Vol. 209). Freiburg, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.DISC.2021.43
[Published Version] View | Files available | DOI | arXiv
 
[91]
2021 | Published | Conference Paper | IST-REx-ID: 11463 | OA
Frantar, Elias, M-FAC: Efficient matrix-free approximations of second-order information. 35th Conference on Neural Information Processing Systems 34. 2021
[Published Version] View | Download Published Version (ext.) | arXiv
 
[90]
2021 | Published | Conference Paper | IST-REx-ID: 10217 | OA
Alistarh, Dan-Adrian, Lower bounds for shared-memory leader election under bounded write contention. 35th International Symposium on Distributed Computing 209. 2021
[Published Version] View | Files available | DOI
 
[89]
2021 | Published | Conference Paper | IST-REx-ID: 11464 | OA
Alistarh, Dan-Adrian, Towards tight communication lower bounds for distributed optimisation. 35th Conference on Neural Information Processing Systems 34. 2021
[Published Version] View | Download Published Version (ext.) | arXiv
 
[88]
2021 | Published | Journal Article | IST-REx-ID: 8723 | OA
Li, Shigang, Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging. IEEE Transactions on Parallel and Distributed Systems 32 (7). 2021
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[87]
2021 | Published | Conference Paper | IST-REx-ID: 9620 | OA
Alistarh, Dan-Adrian, Collecting coupons is faster with friends. Structural Information and Communication Complexity 12810. 2021
[Preprint] View | Files available | DOI
 
[86]
2021 | Published | Conference Paper | IST-REx-ID: 9543 | OA
Davies, Peter, New bounds for distributed mean estimation and variance reduction. 9th International Conference on Learning Representations. 2021
[Published Version] View | Download Published Version (ext.) | arXiv
 
[85]
2021 | Published | Conference Paper | IST-REx-ID: 13147 | OA
Alimisis, Foivos, Communication-efficient distributed optimization with quantized preconditioners. Proceedings of the 38th International Conference on Machine Learning 139. 2021
[Published Version] View | Files available | arXiv
 
[84]
2021 | Published | Conference Paper | IST-REx-ID: 11436 | OA
Kungurtsev, V., Egan, M., Chatterjee, B., & Alistarh, D.-A. (2021). Asynchronous optimization methods for efficient training of deep neural networks with guarantees. In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (Vol. 35, pp. 8209–8216). Virtual, Online: AAAI Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[83]
2021 | Published | Conference Paper | IST-REx-ID: 10435 | OA
Nadiradze, G., Sabour, A., Davies, P., Li, S., & Alistarh, D.-A. (2021). Asynchronous decentralized SGD with quantized and local updates. In 35th Conference on Neural Information Processing Systems. Sydney, Australia: Neural Information Processing Systems Foundation.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[82]
2021 | Published | Conference Paper | IST-REx-ID: 11452 | OA
Alimisis, F., Davies, P., Vandereycken, B., & Alistarh, D.-A. (2021). Distributed principal component analysis with limited communication. In Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems (Vol. 4, pp. 2823–2834). Virtual, Online: Neural Information Processing Systems Foundation.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[81]
2021 | Published | Conference Paper | IST-REx-ID: 10432 | OA
Nadiradze, G., Markov, I., Chatterjee, B., Kungurtsev, V., & Alistarh, D.-A. (2021). Elastic consistency: A practical consistency model for distributed stochastic gradient descent. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, pp. 9037–9045). Virtual.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[80]
2021 | Published | Conference Paper | IST-REx-ID: 11458 | OA
Krumes, A., Iofinova, E. B., Vladu, A., & Alistarh, D.-A. (2021). AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 8557–8570). Virtual, Online: Neural Information Processing Systems Foundation.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[79]
2020 | Published | Conference Paper | IST-REx-ID: 7635
Koval, N., Sokolova, M., Fedorov, A., Alistarh, D.-A., & Tsitelov, D. (2020). Testing concurrency on the JVM with Lincheck. In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP (pp. 423–424). San Diego, CA, United States: Association for Computing Machinery. https://doi.org/10.1145/3332466.3374503
View | DOI
 
[78]
2020 | Published | Conference Paper | IST-REx-ID: 8191
Alistarh, D.-A., Brown, T. A., & Singhal, N. (2020). Memory tagging: Minimalist synchronization for scalable concurrent data structures. In Annual ACM Symposium on Parallelism in Algorithms and Architectures (pp. 37–49). Virtual Event, United States: Association for Computing Machinery. https://doi.org/10.1145/3350755.3400213
View | DOI | WoS
 
[77]
2020 | Published | Conference Paper | IST-REx-ID: 8383
Alistarh, D.-A., Aspnes, J., Ellen, F., Gelashvili, R., & Zhu, L. (2020). Brief Announcement: Why Extension-Based Proofs Fail. In Proceedings of the 39th Symposium on Principles of Distributed Computing (pp. 54–56). Virtual, Italy: Association for Computing Machinery. https://doi.org/10.1145/3382734.3405743
View | DOI
 
[76]
2020 | Conference Paper | IST-REx-ID: 9415 | OA
Kurtz, M., Kopinsky, J., Gelashvili, R., Matveev, A., Carr, J., Goin, M., … Alistarh, D.-A. (2020). Inducing and exploiting activation sparsity for fast neural network inference. In 37th International Conference on Machine Learning, ICML 2020 (Vol. 119, pp. 5533–5543). Online.
[Published Version] View | Files available
 
[75]
2020 | Published | Conference Paper | IST-REx-ID: 7605 | OA
Alistarh, Dan-Adrian, In search of the fastest concurrent union-find algorithm. 23rd International Conference on Principles of Distributed Systems 153. 2020
[Published Version] View | Files available | DOI | arXiv
 
[74]
2020 | Published | Journal Article | IST-REx-ID: 8268 | OA
Gurel, Nezihe Merve, Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications. IEEE Transactions on Signal Processing 68. 2020
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[73]
2020 | Published | Conference Paper | IST-REx-ID: 8725 | OA
Aksenov, V., Alistarh, D.-A., Drozdova, A., & Mohtashami, A. (2020). The splay-list: A distribution-adaptive concurrent skip-list. In 34th International Symposium on Distributed Computing (Vol. 179, p. 3:1-3:18). Freiburg, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.DISC.2020.3
[Published Version] View | Files available | DOI | arXiv
 
[72]
2020 | Published | Conference Paper | IST-REx-ID: 8722 | OA
Li, S., Tal Ben-Nun, T. B.-N., Girolamo, S. D., Alistarh, D.-A., & Hoefler, T. (2020). 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 (pp. 45–61). San Diego, CA, United States: Association for Computing Machinery. https://doi.org/10.1145/3332466.3374528
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[71]
2020 | Published | Conference Paper | IST-REx-ID: 7636 | OA
Brown, T. A., Prokopec, A., & Alistarh, D.-A. (2020). Non-blocking interpolation search trees with doubly-logarithmic running time. In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 276–291). San Diego, CA, United States: Association for Computing Machinery. https://doi.org/10.1145/3332466.3374542
[Published Version] View | DOI | Download Published Version (ext.) | WoS
 
[70]
2020 | Published | Conference Paper | IST-REx-ID: 15086 | OA
Faghri, F., Tabrizian, I., Markov, I., Alistarh, D.-A., Roy, D., & Ramezani-Kebrya, A. (2020). Adaptive gradient quantization for data-parallel SGD. In Advances in Neural Information Processing Systems (Vol. 33). Vancouver, Canada: Neural Information Processing Systems Foundation.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[69]
2020 | Published | Conference Paper | IST-REx-ID: 9632 | OA
Singh, Sidak Pal, WoodFisher: Efficient second-order approximation for neural network compression. 33. 2020
[Published Version] View | Download Published Version (ext.) | arXiv
 
[68]
2020 | Published | Conference Paper | IST-REx-ID: 9631 | OA
Aksenov, Vitaly, Scalable belief propagation via relaxed scheduling. 33. 2020
[Published Version] View | Download Published Version (ext.) | arXiv
 
[67]
2020 | Published | Conference Paper | IST-REx-ID: 8724 | OA
Konstantinov, N. H., Frantar, E., Alistarh, D.-A., & Lampert, C. (2020). On the sample complexity of adversarial multi-source PAC learning. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 5416–5425). Online: ML Research Press.
[Published Version] View | Files available | arXiv
 
[66]
2020 | Published | Conference Paper | IST-REx-ID: 15077 | OA
Alistarh, Dan-Adrian, Dynamic averaging load balancing on cycles. 47th International Colloquium on Automata, Languages, and Programming 168. 2020
[Published Version] View | Files available | DOI | arXiv
 
[65]
2019 | Published | Conference Paper | IST-REx-ID: 7437 | OA
Yu, C., Tang, H., Renggli, C., Kassing, S., Singla, A., Alistarh, D.-A., … Liu, J. (2019). Distributed learning over unreliable networks. In 36th International Conference on Machine Learning, ICML 2019 (Vol. 2019–June, pp. 12481–12512). Long Beach, CA, United States: IMLS.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
[64]
2019 | Published | Conference Paper | IST-REx-ID: 7122
Khirirat, S., Johansson, M., & Alistarh, D.-A. (2019). Gradient compression for communication-limited convex optimization. In 2018 IEEE Conference on Decision and Control. Miami Beach, FL, United States: IEEE. https://doi.org/10.1109/cdc.2018.8619625
View | DOI | WoS
 
[63]
2019 | Published | Conference Paper | IST-REx-ID: 7228
Koval, N., Alistarh, D.-A., & Elizarov, R. (2019). Scalable FIFO channels for programming via communicating sequential processes. In 25th Anniversary of Euro-Par (Vol. 11725, pp. 317–333). Göttingen, Germany: Springer Nature. https://doi.org/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. (2019). Lock-free channels for programming via communicating sequential processes. Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming (pp. 417–418). Washington, NY, United States: ACM. https://doi.org/10.1145/3293883.3297000
View | DOI | WoS
 
[61]
2019 | Published | Conference Paper | IST-REx-ID: 7542 | OA
Wendler, C., Alistarh, D.-A., & Püschel, M. (2019). Powerset convolutional neural networks (Vol. 32, pp. 927–938). Presented at the NIPS: Conference on Neural Information Processing Systems, Vancouver, Canada: Neural Information Processing Systems Foundation.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 
[60]
2019 | Published | Conference Paper | IST-REx-ID: 6676 | OA
Alistarh, Dan-Adrian, Why extension-based proofs fail. Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing. 2019
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[59]
2019 | Published | Conference Paper | IST-REx-ID: 6673 | OA
Alistarh, Dan-Adrian, Efficiency guarantees for parallel incremental algorithms under relaxed schedulers. 31st ACM Symposium on Parallelism in Algorithms and Architectures. 2019
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[58]
2019 | Published | Conference Paper | IST-REx-ID: 7201 | OA
Renggli, Cedric, SparCML: High-performance sparse communication for machine learning. International Conference for High Performance Computing, Networking, Storage and Analysis, SC. 2019
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[57]
2018 | Published | Journal Article | IST-REx-ID: 6001
Alistarh, D.-A., Leiserson, W., Matveev, A., & Shavit, N. (2018). ThreadScan: Automatic and scalable memory reclamation. ACM Transactions on Parallel Computing. Association for Computing Machinery. https://doi.org/10.1145/3201897
View | Files available | DOI
 
[56]
2018 | Published | Conference Paper | IST-REx-ID: 6031
Stojanov, A., Smith, T. M., Alistarh, D.-A., & Puschel, M. (2018). Fast quantized arithmetic on x86: Trading compute for data movement. In 2018 IEEE International Workshop on Signal Processing Systems (Vol. 2018–October). Cape Town, South Africa: IEEE. https://doi.org/10.1109/SiPS.2018.8598402
View | DOI | WoS
 
[55]
2018 | Published | Conference Paper | IST-REx-ID: 7812 | OA
Polino, A., Pascanu, R., & Alistarh, D.-A. (2018). Model compression via distillation and quantization. In 6th International Conference on Learning Representations. Vancouver, Canada.
[Published Version] View | Files available | arXiv
 
[54]
2018 | Published | Conference Paper | IST-REx-ID: 6558 | OA
Alistarh, D.-A., Allen-Zhu, Z., & Li, J. (2018). Byzantine stochastic gradient descent. In Advances in Neural Information Processing Systems (Vol. 2018, pp. 4613–4623). Montreal, Canada: Neural Information Processing Systems Foundation.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 
[53]
2018 | Published | Conference Paper | IST-REx-ID: 7116 | OA
Grubic, D., Tam, L., Alistarh, D.-A., & Zhang, C. (2018). 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 (pp. 145–156). Vienna, Austria: OpenProceedings. https://doi.org/10.5441/002/EDBT.2018.14
[Published Version] View | Files available | DOI
 
[52]
2018 | Published | Conference Paper | IST-REx-ID: 7123 | OA
Alistarh, D.-A., Aspnes, J., & Gelashvili, R. (2018). Space-optimal majority in population protocols. In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 2221–2239). New Orleans, LA, United States: ACM. https://doi.org/10.1137/1.9781611975031.144
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[51]
2018 | Published | Conference Paper | IST-REx-ID: 5963 | OA
Alistarh, Dan-Adrian, Relaxed schedulers can efficiently parallelize iterative algorithms. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC '18. 2018
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[50]
2018 | Published | Conference Paper | IST-REx-ID: 5962 | OA
Alistarh, Dan-Adrian, The convergence of stochastic gradient descent in asynchronous shared memory. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC '18. 2018
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[49]
2018 | Published | Conference Paper | IST-REx-ID: 5961
Alistarh, Dan-Adrian, A brief tutorial on distributed and concurrent machine learning. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC '18. 2018
View | DOI | WoS
 
[48]
2018 | Published | Conference Paper | IST-REx-ID: 5964 | OA
Aksenov, Vitaly, Brief Announcement: Performance prediction for coarse-grained locking. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC '18. 2018
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[47]
2018 | Published | Conference Paper | IST-REx-ID: 5966 | OA
Alistarh, Dan-Adrian, The transactional conflict problem. Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA '18. 2018
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[46]
2018 | Published | Conference Paper | IST-REx-ID: 5965 | OA
Alistarh, Dan-Adrian, Distributionally linearizable data structures. Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA '18. 2018
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[45]
2018 | Published | Conference Paper | IST-REx-ID: 6589 | OA
Alistarh, D.-A., Hoefler, T., Johansson, M., Konstantinov, N. H., Khirirat, S., & Renggli, C. (2018). The convergence of sparsified gradient methods. In Advances in Neural Information Processing Systems 31 (Vol. Volume 2018, pp. 5973–5983). Montreal, Canada: Neural Information Processing Systems Foundation.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
[44]
2018 | Published | Journal Article | IST-REx-ID: 536 | OA
Alistarh, D.-A., Aspnes, J., King, V., & Saia, J. (2018). Communication-efficient randomized consensus. Distributed Computing. Springer. https://doi.org/10.1007/s00446-017-0315-1
[Published Version] View | Files available | DOI
 
[43]
2017 | Published | Conference Paper | IST-REx-ID: 487
Baig, G., Radunovic, B., Alistarh, D.-A., Balkwill, M., Karagiannis, T., & Qiu, L. (2017). Towards unlicensed cellular networks in TV white spaces. In Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies (pp. 2–14). Incheon, South Korea: ACM. https://doi.org/10.1145/3143361.3143367
View | DOI
 
[42]
2017 | Published | Conference Paper | IST-REx-ID: 787 | OA
Alistarh, D.-A., Aspnes, J., Eisenstat, D., Rivest, R., & Gelashvili, R. (2017). Time-space trade-offs in population protocols (pp. 2560–2579). Presented at the SODA: Symposium on Discrete Algorithms, SIAM. https://doi.org/doi.org/10.1137/1.9781611974782.169
View | DOI | Download None (ext.)
 
[41]
2017 | Published | Conference Paper | IST-REx-ID: 788 | OA
Alistarh, D.-A., Dudek, B., Kosowski, A., Soloveichik, D., & Uznański, P. (2017). Robust detection in leak-prone population protocols (Vol. 10467 LNCS, pp. 155–171). Presented at the DNA Computing and Molecular Programming, Springer. https://doi.org/10.1007/978-3-319-66799-7_11
View | DOI | Download None (ext.) | arXiv
 
[40]
2017 | Published | Conference Paper | IST-REx-ID: 789
Alistarh, D.-A., Leiserson, W., Matveev, A., & Shavit, N. (2017). Forkscan: Conservative memory reclamation for modern operating systems (pp. 483–498). Presented at the EuroSys: European Conference on Computer Systems, ACM. https://doi.org/10.1145/3064176.3064214
View | DOI
 
[39]
2017 | Published | Conference Paper | IST-REx-ID: 790
Kara, K., Alistarh, D.-A., Alonso, G., Mutlu, O., & Zhang, C. (2017). FPGA-accelerated dense linear machine learning: A precision-convergence trade-off (pp. 160–167). Presented at the FCCM: Field-Programmable Custom Computing Machines, IEEE. https://doi.org/10.1109/FCCM.2017.39
View | DOI
 
[38]
2017 | Published | Conference Paper | IST-REx-ID: 791 | OA
Alistarh, D.-A., Kopinsky, J., Li, J., & Nadiradze, G. (2017). The power of choice in priority scheduling. In Proceedings of the ACM Symposium on Principles of Distributed Computing (Vol. Part F129314, pp. 283–292). Washington, WA, USA: ACM. https://doi.org/10.1145/3087801.3087810
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[37]
2017 | Published | Conference Paper | IST-REx-ID: 432 | OA
Zhang, Hantian, ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning. Proceedings of Machine Learning Research 70. 2017
[Submitted Version] View | Files available
 
[36]
2017 | Published | Conference Paper | IST-REx-ID: 431 | OA
Alistarh, Dan-Adrian, QSGD: Communication-efficient SGD via gradient quantization and encoding. 2017. 2017
[Submitted Version] View | Download Submitted Version (ext.) | arXiv
 
[35]
2016 | Published | Conference Paper | IST-REx-ID: 785
Haider, S., Hasenplaugh, W., & Alistarh, D.-A. (2016). Lease/Release: Architectural support for scaling contended data structures (Vol. 12-16-March-2016). Presented at the PPoPP: Principles and Practice of Parallel Pogramming, ACM. https://doi.org/10.1145/2851141.2851155
View | DOI
 
[34]
2016 | Published | Journal Article | IST-REx-ID: 786 | OA
Alistarh, D.-A., Censor Hillel, K., & Shavit, N. (2016). Are lock free concurrent algorithms practically wait free . Journal of the ACM. ACM. https://doi.org/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. (2015). The SprayList: A scalable relaxed priority queue (Vol. 2015–January, pp. 11–20). Presented at the PPoPP: Principles and Practice of Parallel Pogramming, ACM. https://doi.org/10.1145/2688500.2688523
View | DOI
 
[32]
2015 | Published | Conference Paper | IST-REx-ID: 777
Alistarh, D.-A., Iglesias, J., & Vojnović, M. (2015). Streaming min-max hypergraph partitioning (Vol. 2015–January, pp. 1900–1908). Presented at the NIPS: Neural Information Processing Systems, Neural Information Processing Systems.
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. (2015). Inherent limitations of hybrid transactional memory (Vol. 9363, pp. 185–199). Presented at the DISC: Distributed Computing, Springer. https://doi.org/10.1007/978-3-662-48653-5_13
View | DOI | Download None (ext.) | arXiv
 
[30]
2015 | Published | Conference Paper | IST-REx-ID: 779
Alistarh, D.-A., Matveev, A., Leiserson, W., & Shavit, N. (2015). ThreadScan: Automatic and scalable memory reclamation (Vol. 2015–June, pp. 123–132). Presented at the SPAA: Symposium on Parallelism in Algorithms and Architectures, ACM. https://doi.org/10.1145/2755573.2755600
View | Files available | DOI
 
[29]
2015 | Published | Conference Paper | IST-REx-ID: 780 | OA
Alistarh, D.-A., & Gelashvili, R. (2015). Polylogarithmic-time leader election in population protocols (Vol. 9135, pp. 479–491). Presented at the ICALP: International Colloquium on Automota, Languages and Programming, Springer. https://doi.org/10.1007/978-3-662-47666-6_38
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[28]
2015 | Published | Conference Paper | IST-REx-ID: 781
Alistarh, D.-A., Gelashvili, R., & Vojnović, M. (2015). Fast and exact majority in population protocols (Vol. 2015–July, pp. 47–56). Presented at the PODC: Principles of Distributed Computing, ACM. https://doi.org/10.1145/2767386.2767429
View | DOI
 
[27]
2015 | Published | Conference Paper | IST-REx-ID: 782
Alistarh, D.-A., Sauerwald, T., & Vojnović, M. (2015). Lock-Free algorithms under stochastic schedulers (Vol. 2015–July, pp. 251–260). Presented at the PODC: Principles of Distributed Computing, ACM. https://doi.org/10.1145/2767386.2767430
View | DOI
 
[26]
2015 | Published | Conference Paper | IST-REx-ID: 783 | OA
Alistarh, D.-A., Gelashvili, R., & Vladu, A. (2015). How to elect a leader faster than a tournament (Vol. 2015–July, pp. 365–374). Presented at the PODC: Principles of Distributed Computing, ACM. https://doi.org/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., Funnell, A., Benjamin, J., Watts, P., & Thomsen, B. (2015). A high-radix, low-latency optical switch for data centers (pp. 367–368). Presented at the SIGCOMM: Special Interest Group on Data Communication, London, United Kindgdom: ACM. https://doi.org/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. (2014). Dynamic task allocation in asynchronous shared memory (pp. 416–435). Presented at the SODA: Symposium on Discrete Algorithms, SIAM. https://doi.org/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. (2014). Tight bounds for asynchronous renaming. Journal of the ACM. ACM. https://doi.org/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. (2014). StackTrack: An automated transactional approach to concurrent memory reclamation. Presented at the EuroSys: European Conference on Computer Systems, ACM. https://doi.org/10.1145/2592798.2592808
View | DOI
 
[21]
2014 | Published | Conference Paper | IST-REx-ID: 771
Alistarh, D.-A., Denysyuk, O., Rodrígues, L., & Shavit, N. (2014). Balls-into-Leaves: Sub-logarithmic renaming in synchronous message-passing systems (pp. 232–241). Presented at the PODC: Principles of Distributed Computing, ACM. https://doi.org/10.1145/2611462.2611499
View | DOI
 
[20]
2014 | Published | Conference Paper | IST-REx-ID: 772 | OA
Alistarh, D.-A., Censor Hillel, K., & Shavit, N. (2014). Are lock-free concurrent algorithms practically wait-free? (pp. 714–723). Presented at the STOC: Symposium on Theory of Computing, ACM. https://doi.org/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. (2014). Communication-efficient randomized consensus. In F. Kuhn (Ed.) (Vol. 8784, pp. 61–75). Presented at the DISC: Distributed Computing, Austin, USA: Springer. https://doi.org/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. (2014). Brief announcement: Are lock-free concurrent algorithms practically wait-free? (pp. 50–52). Presented at the PODC: Principles of Distributed Computing, ACM. https://doi.org/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. (2014). The levelarray: A fast, practical long-lived renaming algorithm (pp. 348–357). Presented at the ICDCS: International Conference on Distributed Computing Systems, IEEE. https://doi.org/10.1109/ICDCS.2014.43
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[16]
2013 | Published | Conference Paper | IST-REx-ID: 765
Alistarh, D.-A., Aspnes, J., Giakkoupis, G., & Woelfel, P. (2013). Randomized loose renaming in O(loglogn) time (pp. 200–209). Presented at the PODC: Principles of Distributed Computing, ACM. https://doi.org/10.1145/2484239.2484240
View | DOI
 
[15]
2012 | Published | Conference Paper | IST-REx-ID: 762
Alistarh, D.-A., Guerraoui, R., Kuznetsov, P., & Losa, G. (2012). On the cost of composing shared-memory algorithms (pp. 298–307). Presented at the SPAA: Symposium on Parallelism in Algorithms and Architectures, ACM. https://doi.org/10.1145/2312005.2312057
View | DOI
 
[14]
2012 | Published | Conference Paper | IST-REx-ID: 763
Alistarh, D.-A., Attiya, H., Guerraoui, R., & Travers, C. (2012). Early deciding synchronous renaming in O(log f) rounds or less (Vol. 7355 LNCS, pp. 195–206). Presented at the SIROCCO: Structural Information and Communication Complexity, Springer. https://doi.org/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. (2012). Of choices, failures and asynchrony: the many faces of set agreement. Algorithmica (New York). Springer. https://doi.org/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. (2012). How to allocate tasks asynchronously (pp. 331–340). Presented at the FOCS: Foundations of Computer Science, IEEE. https://doi.org/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. (2012). Generating Fast Indulgent Algorithms. Theory of Computing Systems. Elsevier. https://doi.org/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. (2011). Generating fast indulgent algorithms (Vol. 6522 LNCS, pp. 41–52). Presented at the ICDCN: International Conference on Distributed Computing and Networking, Springer. https://doi.org/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. (2011). The complexity of renaming (pp. 718–727). Presented at the FOCS: Foundations of Computer Science, IEEE. https://doi.org/10.1109/FOCS.2011.66
View | DOI
 
[8]
2011 | Published | Conference Paper | IST-REx-ID: 760
Alistarh, D.-A., & Aspnes, J. (2011). Sub-logarithmic test-and-set against a weak adversary (Vol. 6950 LNCS, pp. 97–109). Presented at the DISC: Distributed Computing, Springer. https://doi.org/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. (2011). Optimal-time adaptive strong renaming, with applications to counting (pp. 239–248). Presented at the PODC: Principles of Distributed Computing, ACM. https://doi.org/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. (2010). Fast randomized test-and-set and renaming (Vol. 6343 LNCS, pp. 94–108). Presented at the DISC: Distributed Computing, Springer. https://doi.org/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. (2010). How efficient can gossip be? (On the cost of resilient information exchange) (Vol. 6199 LNCS, pp. 115–126). Presented at the ICALP: International Colloquium on Automota, Languages and Programming, Springer. https://doi.org/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. (2010). Securing every bit: Authenticated broadcast in radio networks (pp. 50–59). Presented at the SPAA: Symposium on Parallelism in Algorithms and Architectures, ACM. https://doi.org/10.1145/1810479.1810489
View | DOI
 
[3]
2010 | Published | Conference Paper | IST-REx-ID: 758
Alistarh, D.-A., Gilbert, S., Guerraoui, R., & Travers, C. (2010). Brief announcement: New bounds for partially synchronous set agreement (Vol. 6343 LNCS, pp. 404–405). Presented at the DISC: Distributed Computing, Springer. https://doi.org/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. (2009). Of choices, failures and asynchrony: the many faces of set agreement (Vol. 5878 LNCS, pp. 943–953). Presented at the ISAAC: International Symposium on Algorithms and Computation, Springer. https://doi.org/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. (2008). How to solve consensus in the smallest window of synchrony (Vol. 5218 LNCS, pp. 32–46). Presented at the DISC: Distributed Computing, Springer. https://doi.org/10.1007/978-3-540-87779-0_3
View | DOI
 
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