Dan-Adrian Alistarh

Alistarh Group

134 Publications

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[134]
2024 | Published | Conference Paper | IST-REx-ID: 17093 | OA
H. Zakerinia, S. Talaei, G. Nadiradze, and D.-A. Alistarh, “Communication-efficient federated learning with data and client heterogeneity,” in Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, Valencia, Spain, 2024, vol. 238, pp. 3448–3456.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[133]
2024 | Published | Conference Paper | IST-REx-ID: 17329 | OA
D.-A. Alistarh, K. Chatterjee, M. Karrabi, and J. M. Lazarsfeld, “Game dynamics and equilibrium computation in the population protocol model,” in Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing, Nantes, France, 2024, pp. 40–49.
[Published Version] View | Files available | DOI
 
[132]
2024 | Published | Conference Paper | IST-REx-ID: 17332 | OA
I. Kokorin, V. Yudov, V. Aksenov, and D.-A. Alistarh, “Wait-free trees with asymptotically-efficient range queries,” in 2024 IEEE International Parallel and Distributed Processing Symposium, San Francisco, CA, United States, 2024, pp. 169–179.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[131]
2024 | Published | Conference Paper | IST-REx-ID: 17456 | OA
I. Markov, K. Alimohammadi, E. Frantar, and D.-A. Alistarh, “L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning,” in Proceedings of Machine Learning and Systems , Athens, Greece, 2024, vol. 6.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[130]
2024 | Published | Conference Paper | IST-REx-ID: 15011 | OA
E. Kurtic, T. Hoefler, and D.-A. Alistarh, “How to prune your language model: Recovering accuracy on the ‘Sparsity May Cry’ benchmark,” in Proceedings of Machine Learning Research, Hongkong, China, 2024, vol. 234, pp. 542–553.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[129]
2024 | Published | Conference Paper | IST-REx-ID: 18061 | OA
E. Frantar and D.-A. Alistarh, “QMoE: Sub-1-bit compression of trillion parameter models,” in Proceedings of Machine Learning and Systems, Santa Clara, CA, USA, 2024, vol. 6.
[Published Version] View | Files available | Download Published Version (ext.)
 
[128]
2024 | Published | Conference Paper | IST-REx-ID: 18062 | OA
E. Frantar, C. R. Ruiz, N. Houlsby, D.-A. Alistarh, and U. Evci, “Scaling laws for sparsely-connected foundation models,” in The Twelfth International Conference on Learning Representations, Vienna, Austria, 2024.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[127]
2024 | Published | Conference Paper | IST-REx-ID: 18070
B. Chatterjee, V. Kungurtsev, and D.-A. Alistarh, “Federated SGD with local asynchrony,” in Proceedings of the 44th International Conference on Distributed Computing Systems, Jersey City, NJ, United States, 2024, pp. 857–868.
View | DOI
 
[126]
2024 | Published | Conference Paper | IST-REx-ID: 18113 | OA
V. Egiazarian, A. Panferov, D. Kuznedelev, E. Frantar, A. Babenko, and D.-A. Alistarh, “Extreme compression of large language models via additive quantization,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 12284–12303.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[125]
2024 | Published | Conference Paper | IST-REx-ID: 18117 | OA
M. Nikdan, S. Tabesh, E. Crncevic, and D.-A. Alistarh, “RoSA: Accurate parameter-efficient fine-tuning via robust adaptation,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 38187–38206.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[124]
2024 | Published | Conference Paper | IST-REx-ID: 18121 | OA
A. S. Moakhar, E. B. Iofinova, E. Frantar, and D.-A. Alistarh, “SPADE: Sparsity-guided debugging for deep neural networks,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 45955–45987.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[123]
2023 | Published | Journal Article | IST-REx-ID: 13179 | OA
N. Koval, D. Khalanskiy, and D.-A. Alistarh, “CQS: A formally-verified framework for fair and abortable synchronization,” Proceedings of the ACM on Programming Languages, vol. 7. Association for Computing Machinery , 2023.
[Published Version] View | Files available | DOI
 
[122]
2023 | Published | Conference Paper | IST-REx-ID: 13262 | OA
A. Fedorov, D. Hashemi, G. Nadiradze, and D.-A. Alistarh, “Provably-efficient and internally-deterministic parallel Union-Find,” in Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures, Orlando, FL, United States, 2023, pp. 261–271.
[Published Version] View | Files available | DOI | arXiv
 
[121]
2023 | Published | Conference Paper | IST-REx-ID: 14260 | OA
N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, and D.-A. Alistarh, “Lincheck: A practical framework for testing concurrent data structures on JVM,” in 35th International Conference on Computer Aided Verification , Paris, France, 2023, vol. 13964, pp. 156–169.
[Published Version] View | Files available | DOI
 
[120]
2023 | Published | Journal Article | IST-REx-ID: 14364 | OA
D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, and L. Zhu, “Why extension-based proofs fail,” SIAM Journal on Computing, vol. 52, no. 4. Society for Industrial and Applied Mathematics, pp. 913–944, 2023.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[119]
2023 | Published | Conference Paper | IST-REx-ID: 14458 | OA
E. Frantar and D.-A. Alistarh, “SparseGPT: Massive language models can be accurately pruned in one-shot,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 10323–10337.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[118]
2023 | Published | Conference Paper | IST-REx-ID: 14460 | OA
M. Nikdan, T. Pegolotti, E. B. Iofinova, E. Kurtic, and D.-A. Alistarh, “SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 26215–26227.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[117]
2023 | Published | Conference Paper | IST-REx-ID: 14461 | OA
I. Markov, A. Vladu, Q. Guo, and D.-A. Alistarh, “Quantized distributed training of large models with convergence guarantees,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 24020–24044.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[116]
2023 | Published | Conference Paper | IST-REx-ID: 17378 | OA
E. Frantar, S. Ashkboos, T. Hoefler, and D.-A. Alistarh, “OPTQ: Accurate post-training quantization for generative pre-trained transformers,” in 11th International Conference on Learning Representations , Kigali, Rwanda, 2023.
[Published Version] View | Files available
 
[115]
2023 | Published | Journal Article | IST-REx-ID: 12330 | OA
V. Aksenov, D.-A. Alistarh, A. Drozdova, and A. Mohtashami, “The splay-list: A distribution-adaptive concurrent skip-list,” Distributed Computing, vol. 36. Springer Nature, pp. 395–418, 2023.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[114]
2023 | Published | Journal Article | IST-REx-ID: 12566 | OA
D.-A. Alistarh, F. Ellen, and J. Rybicki, “Wait-free approximate agreement on graphs,” Theoretical Computer Science, vol. 948, no. 2. Elsevier, 2023.
[Published Version] View | Files available | DOI | WoS
 
[113]
2023 | Published | Conference Paper | IST-REx-ID: 12735 | OA
N. Koval, D.-A. Alistarh, and R. Elizarov, “Fast and scalable channels in Kotlin Coroutines,” in Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Montreal, QC, Canada, 2023, pp. 107–118.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[112]
2023 | Published | Conference Paper | IST-REx-ID: 13053 | OA
A. Krumes, A. Vladu, E. Kurtic, C. Lampert, and D.-A. Alistarh, “CrAM: A Compression-Aware Minimizer,” in 11th International Conference on Learning Representations , Kigali, Rwanda , 2023.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[111]
2023 | Published | Conference Paper | IST-REx-ID: 14771 | OA
E. B. Iofinova, A. Krumes, and D.-A. Alistarh, “Bias in pruned vision models: In-depth analysis and countermeasures,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 2023, pp. 24364–24373.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[110]
2023 | Research Data Reference | IST-REx-ID: 14995 | OA
N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, and D.-A. Alistarh, “Lincheck: A practical framework for testing concurrent data structures on JVM.” Zenodo, 2023.
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[109]
2023 | Published | Conference Paper | IST-REx-ID: 15363 | OA
M. Safaryan, A. Krumes, and D.-A. Alistarh, “Knowledge distillation performs partial variance reduction,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2023, vol. 36.
[Published Version] View | Files available | arXiv
 
[108]
2022 | Published | Conference Paper | IST-REx-ID: 11180 | OA
A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can be state-of-the-art priority schedulers,” in Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Seoul, Republic of Korea, 2022, pp. 353–367.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[107]
2022 | Published | Conference Paper | IST-REx-ID: 11181 | OA
T. A. Brown, W. Sigouin, and D.-A. Alistarh, “PathCAS: An efficient middle ground for concurrent search data structures,” in Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Seoul, Republic of Korea, 2022, pp. 385–399.
[Published Version] View | Files available | DOI | WoS
 
[106]
2022 | Published | Conference Paper | IST-REx-ID: 11184 | OA
D.-A. Alistarh, R. Gelashvili, and J. Rybicki, “Fast graphical population protocols,” in 25th International Conference on Principles of Distributed Systems, Strasbourg, France, 2022, vol. 217.
[Published Version] View | Files available | DOI | arXiv
 
[105]
2022 | Published | Conference Paper | IST-REx-ID: 11844 | OA
D.-A. Alistarh, J. Rybicki, and S. Voitovych, “Near-optimal leader election in population protocols on graphs,” in Proceedings of the Annual ACM Symposium on Principles of Distributed Computing, Salerno, Italy, 2022, pp. 246–256.
[Published Version] View | Files available | DOI | arXiv
 
[104]
2022 | Published | Conference Paper | IST-REx-ID: 17059 | OA
E. Frantar and D.-A. Alistarh, “SPDY: Accurate pruning with speedup guarantees,” in 39th International Conference on Machine Learning, Baltimore, MD, United States, 2022, vol. 162, pp. 6726–6743.
[Published Version] View | Files available | WoS
 
[103]
2022 | Published | Conference Paper | IST-REx-ID: 17087 | OA
E. Frantar, S. P. Singh, and D.-A. Alistarh, “Optimal brain compression: A framework for accurate post-training quantization and pruning,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2022, vol. 35.
[Submitted Version] View | Files available | arXiv
 
[102]
2022 | Published | Conference Paper | IST-REx-ID: 17088 | OA
E. Kurtic et al., “The optimal BERT surgeon: Scalable and accurate second-order pruning for large language models,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, pp. 4163–4181.
[Published Version] View | Files available | DOI | arXiv
 
[101]
2022 | Published | Conference Paper | IST-REx-ID: 12299 | OA
E. B. Iofinova, A. Krumes, M. Kurtz, and D.-A. Alistarh, “How well do sparse ImageNet models transfer?,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, United States, 2022, pp. 12256–12266.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[100]
2022 | Published | Conference Paper | IST-REx-ID: 12780 | OA
I. Markov, H. Ramezanikebrya, and D.-A. Alistarh, “CGX: Adaptive system support for communication-efficient deep learning,” in Proceedings of the 23rd ACM/IFIP International Middleware Conference, Quebec, QC, Canada, 2022, pp. 241–254.
[Published Version] View | Files available | DOI | arXiv
 
[99]
2022 | Research Data Reference | IST-REx-ID: 13076 | OA
A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can be state-of-the-art priority schedulers.” Zenodo, 2022.
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[98]
2022 | Published | Journal Article | IST-REx-ID: 8286 | OA
D.-A. Alistarh, G. Nadiradze, and A. Sabour, “Dynamic averaging load balancing on cycles,” Algorithmica, vol. 84, no. 4. Springer Nature, pp. 1007–1029, 2022.
[Published Version] View | Files available | DOI | WoS | arXiv
 
[97]
2021 | Published | Journal Article | IST-REx-ID: 10180 | OA
T. Hoefler, D.-A. Alistarh, T. Ben-Nun, N. Dryden, and E.-A. Peste, “Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks,” Journal of Machine Learning Research, vol. 22, no. 241. Journal of Machine Learning Research, pp. 1–124, 2021.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[96]
2021 | Published | Conference Paper | IST-REx-ID: 10217 | OA
D.-A. Alistarh, R. Gelashvili, and G. Nadiradze, “Lower bounds for shared-memory leader election under bounded write contention,” in 35th International Symposium on Distributed Computing, Freiburg, Germany, 2021, vol. 209.
[Published Version] View | Files available | DOI
 
[95]
2021 | Published | Conference Paper | IST-REx-ID: 10218 | OA
D.-A. Alistarh, R. Gelashvili, and J. Rybicki, “Brief announcement: Fast graphical population protocols,” in 35th International Symposium on Distributed Computing, Freiburg, Germany, 2021, vol. 209.
[Published Version] View | Files available | DOI | arXiv
 
[94]
2021 | Published | Conference Paper | IST-REx-ID: 9543 | OA
P. Davies, V. Gurunanthan, N. Moshrefi, S. Ashkboos, and D.-A. Alistarh, “New bounds for distributed mean estimation and variance reduction,” in 9th International Conference on Learning Representations, Virtual, 2021.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[93]
2021 | Published | Journal Article | IST-REx-ID: 9571 | OA
A. Ramezani-Kebrya, F. Faghri, I. Markov, V. Aksenov, D.-A. Alistarh, and D. M. Roy, “NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization,” Journal of Machine Learning Research, vol. 22, no. 114. Journal of Machine Learning Research, p. 1−43, 2021.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[92]
2021 | Published | Conference Paper | IST-REx-ID: 9620 | OA
D.-A. Alistarh and P. Davies, “Collecting coupons is faster with friends,” in Structural Information and Communication Complexity, Wrocław, Poland, 2021, vol. 12810, pp. 3–12.
[Preprint] View | Files available | DOI
 
[91]
2021 | Published | Conference Paper | IST-REx-ID: 9823 | OA
D.-A. Alistarh, F. Ellen, and J. Rybicki, “Wait-free approximate agreement on graphs,” in Structural Information and Communication Complexity, Wrocław, Poland, 2021, vol. 12810, pp. 87–105.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[90]
2021 | Published | Conference Paper | IST-REx-ID: 9951
D.-A. Alistarh, M. Töpfer, and P. Uznański, “Comparison dynamics in population protocols,” in Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, Virtual, Italy, 2021, pp. 55–65.
View | DOI | WoS
 
[89]
2021 | Published | Journal Article | IST-REx-ID: 8723 | OA
S. Li et al., “Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging,” IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 7. IEEE, 2021.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[88]
2021 | Published | Conference Paper | IST-REx-ID: 13147 | OA
F. Alimisis, P. Davies, and D.-A. Alistarh, “Communication-efficient distributed optimization with quantized preconditioners,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 196–206.
[Published Version] View | Files available | arXiv
 
[87]
2021 | Published | Conference Paper | IST-REx-ID: 10853 | OA
A. Fedorov, N. Koval, and D.-A. Alistarh, “A scalable concurrent algorithm for dynamic connectivity,” in Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures, Virtual, Online, 2021, pp. 208–220.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[86]
2021 | Published | Conference Paper | IST-REx-ID: 11436 | OA
V. Kungurtsev, M. Egan, B. Chatterjee, and D.-A. Alistarh, “Asynchronous optimization methods for efficient training of deep neural networks with guarantees,” in 35th AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual, Online, 2021, vol. 35, no. 9B, pp. 8209–8216.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[85]
2021 | Published | Conference Paper | IST-REx-ID: 11452 | OA
F. Alimisis, P. Davies, B. Vandereycken, and D.-A. Alistarh, “Distributed principal component analysis with limited communication,” in Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 4, pp. 2823–2834.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[84]
2021 | Published | Conference Paper | IST-REx-ID: 11458 | OA
A. Krumes, E. B. Iofinova, A. Vladu, and D.-A. Alistarh, “AC/DC: Alternating Compressed/DeCompressed training of deep neural networks,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 8557–8570.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[83]
2021 | Published | Conference Paper | IST-REx-ID: 11463 | OA
E. Frantar, E. Kurtic, and D.-A. Alistarh, “M-FAC: Efficient matrix-free approximations of second-order information,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 14873–14886.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[82]
2021 | Published | Conference Paper | IST-REx-ID: 11464 | OA
D.-A. Alistarh and J. Korhonen, “Towards tight communication lower bounds for distributed optimisation,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 7254–7266.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[81]
2021 | Published | Conference Paper | IST-REx-ID: 10435 | OA
G. Nadiradze, A. Sabour, P. Davies, S. Li, and D.-A. Alistarh, “Asynchronous decentralized SGD with quantized and local updates,” in 35th Conference on Neural Information Processing Systems, Sydney, Australia, 2021.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[80]
2021 | Published | Conference Paper | IST-REx-ID: 10432 | OA
G. Nadiradze, I. Markov, B. Chatterjee, V. Kungurtsev, and D.-A. Alistarh, “Elastic consistency: A practical consistency model for distributed stochastic gradient descent,” in Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35, no. 10, pp. 9037–9045.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[79]
2020 | Published | Conference Paper | IST-REx-ID: 9631 | OA
V. Aksenov, D.-A. Alistarh, and J. Korhonen, “Scalable belief propagation via relaxed scheduling,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 22361–22372.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[78]
2020 | Published | Conference Paper | IST-REx-ID: 9632 | OA
S. P. Singh and D.-A. Alistarh, “WoodFisher: Efficient second-order approximation for neural network compression,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 18098–18109.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[77]
2020 | Published | Conference Paper | IST-REx-ID: 8722 | OA
S. Li, T. B.-N. Tal Ben-Nun, S. D. Girolamo, D.-A. Alistarh, and T. Hoefler, “Taming unbalanced training workloads in deep learning with partial collective operations,” in Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, San Diego, CA, United States, 2020, pp. 45–61.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[76]
2020 | Published | Conference Paper | IST-REx-ID: 8724 | OA
N. H. Konstantinov, E. Frantar, D.-A. Alistarh, and C. Lampert, “On the sample complexity of adversarial multi-source PAC learning,” in Proceedings of the 37th International Conference on Machine Learning, Online, 2020, vol. 119, pp. 5416–5425.
[Published Version] View | Files available | arXiv
 
[75]
2020 | Published | Conference Paper | IST-REx-ID: 8725 | OA
V. Aksenov, D.-A. Alistarh, A. Drozdova, and A. Mohtashami, “The splay-list: A distribution-adaptive concurrent skip-list,” in 34th International Symposium on Distributed Computing, Freiburg, Germany, 2020, vol. 179, p. 3:1-3:18.
[Published Version] View | Files available | DOI | arXiv
 
[74]
2020 | Conference Paper | IST-REx-ID: 9415 | OA
M. Kurtz et al., “Inducing and exploiting activation sparsity for fast neural network inference,” in 37th International Conference on Machine Learning, ICML 2020, Online, 2020, vol. 119, pp. 5533–5543.
[Published Version] View | Files available
 
[73]
2020 | Published | Conference Paper | IST-REx-ID: 15077 | OA
D.-A. Alistarh, G. Nadiradze, and A. Sabour, “Dynamic averaging load balancing on cycles,” in 47th International Colloquium on Automata, Languages, and Programming, Saarbrücken, Germany, Virtual, 2020, vol. 168.
[Published Version] View | Files available | DOI | arXiv
 
[72]
2020 | Published | Conference Paper | IST-REx-ID: 15086 | OA
F. Faghri, I. Tabrizian, I. Markov, D.-A. Alistarh, D. Roy, and A. Ramezani-Kebrya, “Adaptive gradient quantization for data-parallel SGD,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[71]
2020 | Published | Conference Paper | IST-REx-ID: 7605 | OA
D.-A. Alistarh, A. Fedorov, and N. Koval, “In search of the fastest concurrent union-find algorithm,” in 23rd International Conference on Principles of Distributed Systems, Neuchatal, Switzerland, 2020, vol. 153, p. 15:1-15:16.
[Published Version] View | Files available | DOI | arXiv
 
[70]
2020 | Published | Conference Paper | IST-REx-ID: 7635
N. Koval, M. Sokolova, A. Fedorov, D.-A. Alistarh, and D. Tsitelov, “Testing concurrency on the JVM with Lincheck,” in Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, San Diego, CA, United States, 2020, pp. 423–424.
View | DOI
 
[69]
2020 | Published | Conference Paper | IST-REx-ID: 7636 | OA
T. A. Brown, A. Prokopec, and D.-A. Alistarh, “Non-blocking interpolation search trees with doubly-logarithmic running time,” in Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, San Diego, CA, United States, 2020, pp. 276–291.
[Published Version] View | DOI | Download Published Version (ext.) | WoS
 
[68]
2020 | Published | Conference Paper | IST-REx-ID: 8191
D.-A. Alistarh, T. A. Brown, and N. Singhal, “Memory tagging: Minimalist synchronization for scalable concurrent data structures,” in Annual ACM Symposium on Parallelism in Algorithms and Architectures, Virtual Event, United States, 2020, no. 7, pp. 37–49.
View | DOI | WoS
 
[67]
2020 | Published | Journal Article | IST-REx-ID: 8268 | OA
N. M. Gurel et al., “Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications,” IEEE Transactions on Signal Processing, vol. 68. IEEE, pp. 4268–4282, 2020.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[66]
2020 | Published | Conference Paper | IST-REx-ID: 8383
D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, and L. Zhu, “Brief Announcement: Why Extension-Based Proofs Fail,” in Proceedings of the 39th Symposium on Principles of Distributed Computing, Virtual, Italy, 2020, pp. 54–56.
View | DOI
 
[65]
2019 | Published | Conference Paper | IST-REx-ID: 7437 | OA
C. Yu et al., “Distributed learning over unreliable networks,” in 36th International Conference on Machine Learning, ICML 2019, Long Beach, CA, United States, 2019, vol. 2019–June, pp. 12481–12512.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
[64]
2019 | Published | Conference Paper | IST-REx-ID: 7542 | OA
C. Wendler, D.-A. Alistarh, and M. Püschel, “Powerset convolutional neural networks,” presented at the NIPS: Conference on Neural Information Processing Systems, Vancouver, Canada, 2019, vol. 32, pp. 927–938.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 
[63]
2019 | Published | Conference Paper | IST-REx-ID: 6676 | OA
D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, and L. Zhu, “Why extension-based proofs fail,” in Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, Phoenix, AZ, United States, 2019, pp. 986–996.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[62]
2019 | Published | Conference Paper | IST-REx-ID: 7122
S. Khirirat, M. Johansson, and D.-A. Alistarh, “Gradient compression for communication-limited convex optimization,” in 2018 IEEE Conference on Decision and Control, Miami Beach, FL, United States, 2019.
View | DOI | WoS
 
[61]
2019 | Published | Conference Paper | IST-REx-ID: 7201 | OA
C. Renggli, S. Ashkboos, M. Aghagolzadeh, D.-A. Alistarh, and T. Hoefler, “SparCML: High-performance sparse communication for machine learning,” in International Conference for High Performance Computing, Networking, Storage and Analysis, SC, Denver, CO, Unites States, 2019.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[60]
2019 | Published | Conference Paper | IST-REx-ID: 7228
N. Koval, D.-A. Alistarh, and R. Elizarov, “Scalable FIFO channels for programming via communicating sequential processes,” in 25th Anniversary of Euro-Par, Göttingen, Germany, 2019, vol. 11725, pp. 317–333.
View | DOI | WoS
 
[59]
2019 | Published | Conference Paper | IST-REx-ID: 6673 | OA
D.-A. Alistarh, G. Nadiradze, and N. Koval, “Efficiency guarantees for parallel incremental algorithms under relaxed schedulers,” in 31st ACM Symposium on Parallelism in Algorithms and Architectures, Phoenix, AZ, United States, 2019, pp. 145–154.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[58]
2019 | Published | Conference Poster | IST-REx-ID: 6485
N. Koval, D.-A. Alistarh, and R. Elizarov, Lock-free channels for programming via communicating sequential processes. ACM, 2019, pp. 417–418.
View | DOI | WoS
 
[57]
2018 | Published | Journal Article | IST-REx-ID: 536 | OA
D.-A. Alistarh, J. Aspnes, V. King, and J. Saia, “Communication-efficient randomized consensus,” Distributed Computing, vol. 31, no. 6. Springer, pp. 489–501, 2018.
[Published Version] View | Files available | DOI
 
[56]
2018 | Published | Conference Paper | IST-REx-ID: 5961
D.-A. Alistarh, “A brief tutorial on distributed and concurrent machine learning,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 487–488.
View | DOI | WoS
 
[55]
2018 | Published | Conference Paper | IST-REx-ID: 5962 | OA
D.-A. Alistarh, C. De Sa, and N. H. Konstantinov, “The convergence of stochastic gradient descent in asynchronous shared memory,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 169–178.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[54]
2018 | Published | Conference Paper | IST-REx-ID: 5963 | OA
D.-A. Alistarh, T. A. Brown, J. Kopinsky, and G. Nadiradze, “Relaxed schedulers can efficiently parallelize iterative algorithms,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 377–386.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[53]
2018 | Published | Conference Paper | IST-REx-ID: 5964 | OA
V. Aksenov, D.-A. Alistarh, and P. Kuznetsov, “Brief Announcement: Performance prediction for coarse-grained locking,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 411–413.
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[52]
2018 | Published | Conference Paper | IST-REx-ID: 5966 | OA
D.-A. Alistarh, S. K. Haider, R. Kübler, and G. Nadiradze, “The transactional conflict problem,” in Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, Vienna, Austria, 2018, pp. 383–392.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[51]
2018 | Published | Journal Article | IST-REx-ID: 6001
D.-A. Alistarh, W. Leiserson, A. Matveev, and N. Shavit, “ThreadScan: Automatic and scalable memory reclamation,” ACM Transactions on Parallel Computing, vol. 4, no. 4. Association for Computing Machinery, 2018.
View | Files available | DOI
 
[50]
2018 | Published | Conference Paper | IST-REx-ID: 6031
A. Stojanov, T. M. Smith, D.-A. Alistarh, and M. Puschel, “Fast quantized arithmetic on x86: Trading compute for data movement,” in 2018 IEEE International Workshop on Signal Processing Systems, Cape Town, South Africa, 2018, vol. 2018–October.
View | DOI | WoS
 
[49]
2018 | Published | Conference Paper | IST-REx-ID: 7812 | OA
A. Polino, R. Pascanu, and D.-A. Alistarh, “Model compression via distillation and quantization,” in 6th International Conference on Learning Representations, Vancouver, Canada, 2018.
[Published Version] View | Files available | arXiv
 
[48]
2018 | Published | Conference Paper | IST-REx-ID: 6558 | OA
D.-A. Alistarh, Z. Allen-Zhu, and J. Li, “Byzantine stochastic gradient descent,” in Advances in Neural Information Processing Systems, Montreal, Canada, 2018, vol. 2018, pp. 4613–4623.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 
[47]
2018 | Published | Conference Paper | IST-REx-ID: 6589 | OA
D.-A. Alistarh, T. Hoefler, M. Johansson, N. H. Konstantinov, S. Khirirat, and C. Renggli, “The convergence of sparsified gradient methods,” in Advances in Neural Information Processing Systems 31, Montreal, Canada, 2018, vol. Volume 2018, pp. 5973–5983.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
[46]
2018 | Published | Conference Paper | IST-REx-ID: 7116 | OA
D. Grubic, L. Tam, D.-A. Alistarh, and C. Zhang, “Synchronous multi-GPU training for deep learning with low-precision communications: An empirical study,” in Proceedings of the 21st International Conference on Extending Database Technology, Vienna, Austria, 2018, pp. 145–156.
[Published Version] View | Files available | DOI
 
[45]
2018 | Published | Conference Paper | IST-REx-ID: 7123 | OA
D.-A. Alistarh, J. Aspnes, and R. Gelashvili, “Space-optimal majority in population protocols,” in Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, United States, 2018, pp. 2221–2239.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[44]
2018 | Published | Conference Paper | IST-REx-ID: 5965 | OA
D.-A. Alistarh, T. A. Brown, J. Kopinsky, J. Z. Li, and G. Nadiradze, “Distributionally linearizable data structures,” in Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, Vienna, Austria, 2018, pp. 133–142.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[43]
2017 | Published | Conference Paper | IST-REx-ID: 431 | OA
D.-A. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnović, “QSGD: Communication-efficient SGD via gradient quantization and encoding,” presented at the NIPS: Neural Information Processing System, Long Beach, CA, United States, 2017, vol. 2017, pp. 1710–1721.
[Submitted Version] View | Download Submitted Version (ext.) | arXiv
 
[42]
2017 | Published | Conference Paper | IST-REx-ID: 432 | OA
H. Zhang, J. Li, K. Kara, D.-A. Alistarh, J. Liu, and C. Zhang, “ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning,” in Proceedings of Machine Learning Research, Sydney, Australia, 2017, vol. 70, pp. 4035–4043.
[Submitted Version] View | Files available
 
[41]
2017 | Published | Conference Paper | IST-REx-ID: 487
G. Baig, B. Radunovic, D.-A. Alistarh, M. Balkwill, T. Karagiannis, and L. Qiu, “Towards unlicensed cellular networks in TV white spaces,” in Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies, Incheon, South Korea, 2017, pp. 2–14.
View | DOI
 
[40]
2017 | Published | Conference Paper | IST-REx-ID: 787 | OA
D.-A. Alistarh, J. Aspnes, D. Eisenstat, R. Rivest, and R. Gelashvili, “Time-space trade-offs in population protocols,” presented at the SODA: Symposium on Discrete Algorithms, 2017, pp. 2560–2579.
View | DOI | Download None (ext.)
 
[39]
2017 | Published | Conference Paper | IST-REx-ID: 788 | OA
D.-A. Alistarh, B. Dudek, A. Kosowski, D. Soloveichik, and P. Uznański, “Robust detection in leak-prone population protocols,” presented at the DNA Computing and Molecular Programming, 2017, vol. 10467 LNCS, pp. 155–171.
View | DOI | Download None (ext.) | arXiv
 
[38]
2017 | Published | Conference Paper | IST-REx-ID: 789
D.-A. Alistarh, W. Leiserson, A. Matveev, and N. Shavit, “Forkscan: Conservative memory reclamation for modern operating systems,” presented at the EuroSys: European Conference on Computer Systems, 2017, pp. 483–498.
View | DOI
 
[37]
2017 | Published | Conference Paper | IST-REx-ID: 790
K. Kara, D.-A. Alistarh, G. Alonso, O. Mutlu, and C. Zhang, “FPGA-accelerated dense linear machine learning: A precision-convergence trade-off,” presented at the FCCM: Field-Programmable Custom Computing Machines, 2017, pp. 160–167.
View | DOI
 
[36]
2017 | Published | Conference Paper | IST-REx-ID: 791 | OA
D.-A. Alistarh, J. Kopinsky, J. Li, and G. Nadiradze, “The power of choice in priority scheduling,” in Proceedings of the ACM Symposium on Principles of Distributed Computing, Washington, WA, USA, 2017, vol. Part F129314, pp. 283–292.
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[35]
2016 | Published | Conference Paper | IST-REx-ID: 785
S. Haider, W. Hasenplaugh, and D.-A. Alistarh, “Lease/Release: Architectural support for scaling contended data structures,” presented at the PPoPP: Principles and Practice of Parallel Pogramming, 2016, vol. 12-16-March-2016.
View | DOI
 
[34]
2016 | Published | Journal Article | IST-REx-ID: 786 | OA
D.-A. Alistarh, K. Censor Hillel, and N. Shavit, “Are lock free concurrent algorithms practically wait free ,” Journal of the ACM, vol. 63, no. 4. ACM, 2016.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[33]
2015 | Published | Conference Paper | IST-REx-ID: 776
D.-A. Alistarh, J. Kopinsky, J. Li, and N. Shavit, “The SprayList: A scalable relaxed priority queue,” presented at the PPoPP: Principles and Practice of Parallel Pogramming, 2015, vol. 2015–January, pp. 11–20.
View | DOI
 
[32]
2015 | Published | Conference Paper | IST-REx-ID: 777
D.-A. Alistarh, J. Iglesias, and M. Vojnović, “Streaming min-max hypergraph partitioning,” presented at the NIPS: Neural Information Processing Systems, 2015, vol. 2015–January, pp. 1900–1908.
View | Download None (ext.)
 
[31]
2015 | Published | Conference Paper | IST-REx-ID: 778 | OA
D.-A. Alistarh, J. Kopinsky, P. Kuznetsov, S. Ravi, and N. Shavit, “Inherent limitations of hybrid transactional memory,” presented at the DISC: Distributed Computing, 2015, vol. 9363, pp. 185–199.
View | DOI | Download None (ext.) | arXiv
 
[30]
2015 | Published | Conference Paper | IST-REx-ID: 779
D.-A. Alistarh, A. Matveev, W. Leiserson, and N. Shavit, “ThreadScan: Automatic and scalable memory reclamation,” presented at the SPAA: Symposium on Parallelism in Algorithms and Architectures, 2015, vol. 2015–June, pp. 123–132.
View | Files available | DOI
 
[29]
2015 | Published | Conference Paper | IST-REx-ID: 780 | OA
D.-A. Alistarh and R. Gelashvili, “Polylogarithmic-time leader election in population protocols,” presented at the ICALP: International Colloquium on Automota, Languages and Programming, 2015, vol. 9135, pp. 479–491.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[28]
2015 | Published | Conference Paper | IST-REx-ID: 781
D.-A. Alistarh, R. Gelashvili, and M. Vojnović, “Fast and exact majority in population protocols,” presented at the PODC: Principles of Distributed Computing, 2015, vol. 2015–July, pp. 47–56.
View | DOI
 
[27]
2015 | Published | Conference Paper | IST-REx-ID: 782
D.-A. Alistarh, T. Sauerwald, and M. Vojnović, “Lock-Free algorithms under stochastic schedulers,” presented at the PODC: Principles of Distributed Computing, 2015, vol. 2015–July, pp. 251–260.
View | DOI
 
[26]
2015 | Published | Conference Paper | IST-REx-ID: 783 | OA
D.-A. Alistarh, R. Gelashvili, and A. Vladu, “How to elect a leader faster than a tournament,” presented at the PODC: Principles of Distributed Computing, 2015, vol. 2015–July, pp. 365–374.
View | DOI | Download None (ext.)
 
[25]
2015 | Published | Conference Paper | IST-REx-ID: 784
D.-A. Alistarh et al., “A high-radix, low-latency optical switch for data centers,” presented at the SIGCOMM: Special Interest Group on Data Communication, London, United Kindgdom, 2015, pp. 367–368.
View | DOI
 
[24]
2014 | Published | Conference Paper | IST-REx-ID: 768
D.-A. Alistarh, J. Aspnes, M. Bender, R. Gelashvili, and S. Gilbert, “Dynamic task allocation in asynchronous shared memory,” presented at the SODA: Symposium on Discrete Algorithms, 2014, pp. 416–435.
View | DOI
 
[23]
2014 | Published | Journal Article | IST-REx-ID: 769
D.-A. Alistarh, J. Aspnes, K. Censor Hillel, S. Gilbert, and R. Guerraoui, “Tight bounds for asynchronous renaming,” Journal of the ACM, vol. 61, no. 3. ACM, 2014.
View | DOI
 
[22]
2014 | Published | Conference Paper | IST-REx-ID: 770
D.-A. Alistarh, P. Eugster, M. Herlihy, A. Matveev, and N. Shavit, “StackTrack: An automated transactional approach to concurrent memory reclamation,” presented at the EuroSys: European Conference on Computer Systems, 2014.
View | DOI
 
[21]
2014 | Published | Conference Paper | IST-REx-ID: 771
D.-A. Alistarh, O. Denysyuk, L. Rodrígues, and N. Shavit, “Balls-into-Leaves: Sub-logarithmic renaming in synchronous message-passing systems,” presented at the PODC: Principles of Distributed Computing, 2014, pp. 232–241.
View | DOI
 
[20]
2014 | Published | Conference Paper | IST-REx-ID: 772 | OA
D.-A. Alistarh, K. Censor Hillel, and N. Shavit, “Are lock-free concurrent algorithms practically wait-free?,” presented at the STOC: Symposium on Theory of Computing, 2014, pp. 714–723.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[19]
2014 | Published | Conference Paper | IST-REx-ID: 773
D.-A. Alistarh, J. Aspnes, V. King, and J. Saia, “Communication-efficient randomized consensus,” presented at the DISC: Distributed Computing, Austin, USA, 2014, vol. 8784, pp. 61–75.
View | DOI
 
[18]
2014 | Published | Conference Paper | IST-REx-ID: 774
D.-A. Alistarh, K. Censor Hille, and N. Shavit, “Brief announcement: Are lock-free concurrent algorithms practically wait-free?,” presented at the PODC: Principles of Distributed Computing, 2014, pp. 50–52.
View | DOI
 
[17]
2014 | Published | Conference Paper | IST-REx-ID: 775 | OA
D.-A. Alistarh, J. Kopinsky, A. Matveev, and N. Shavit, “The levelarray: A fast, practical long-lived renaming algorithm,” presented at the ICDCS: International Conference on Distributed Computing Systems, 2014, pp. 348–357.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[16]
2013 | Published | Conference Paper | IST-REx-ID: 765
D.-A. Alistarh, J. Aspnes, G. Giakkoupis, and P. Woelfel, “Randomized loose renaming in O(loglogn) time,” presented at the PODC: Principles of Distributed Computing, 2013, pp. 200–209.
View | DOI
 
[15]
2012 | Published | Conference Paper | IST-REx-ID: 762
D.-A. Alistarh, R. Guerraoui, P. Kuznetsov, and G. Losa, “On the cost of composing shared-memory algorithms,” presented at the SPAA: Symposium on Parallelism in Algorithms and Architectures, 2012, pp. 298–307.
View | DOI
 
[14]
2012 | Published | Conference Paper | IST-REx-ID: 763
D.-A. Alistarh, H. Attiya, R. Guerraoui, and C. Travers, “Early deciding synchronous renaming in O(log f) rounds or less,” presented at the SIROCCO: Structural Information and Communication Complexity, 2012, vol. 7355 LNCS, pp. 195–206.
View | DOI
 
[13]
2012 | Published | Journal Article | IST-REx-ID: 764
D.-A. Alistarh, S. Gilbert, R. Guerraoui, and C. Travers, “Of choices, failures and asynchrony: the many faces of set agreement,” Algorithmica (New York), vol. 62, no. 1–2. Springer, pp. 595–629, 2012.
View | DOI
 
[12]
2012 | Published | Conference Paper | IST-REx-ID: 766
D.-A. Alistarh, M. Bender, S. Gilbert, and R. Guerraoui, “How to allocate tasks asynchronously,” presented at the FOCS: Foundations of Computer Science, 2012, pp. 331–340.
View | DOI
 
[11]
2012 | Published | Journal Article | IST-REx-ID: 767
D.-A. Alistarh, S. Gilbert, R. Guerraoui, and C. Travers, “Generating Fast Indulgent Algorithms,” Theory of Computing Systems, vol. 51, no. 4. Elsevier, pp. 404–424, 2012.
View | DOI
 
[10]
2011 | Published | Conference Paper | IST-REx-ID: 757
D.-A. Alistarh, S. Gilbert, R. Guerraoui, and C. Travers, “Generating fast indulgent algorithms,” presented at the ICDCN: International Conference on Distributed Computing and Networking, 2011, vol. 6522 LNCS, pp. 41–52.
View | DOI
 
[9]
2011 | Published | Conference Paper | IST-REx-ID: 759
D.-A. Alistarh, J. Aspnes, S. Gilbert, and R. Guerraoui, “The complexity of renaming,” presented at the FOCS: Foundations of Computer Science, 2011, pp. 718–727.
View | DOI
 
[8]
2011 | Published | Conference Paper | IST-REx-ID: 760
D.-A. Alistarh and J. Aspnes, “Sub-logarithmic test-and-set against a weak adversary,” presented at the DISC: Distributed Computing, 2011, vol. 6950 LNCS, pp. 97–109.
View | DOI
 
[7]
2011 | Published | Conference Paper | IST-REx-ID: 761
D.-A. Alistarh, J. Aspnes, K. Censor Hillel, S. Gilbert, and M. Zadimoghaddam, “Optimal-time adaptive strong renaming, with applications to counting,” presented at the PODC: Principles of Distributed Computing, 2011, pp. 239–248.
View | DOI
 
[6]
2010 | Published | Conference Paper | IST-REx-ID: 754
D.-A. Alistarh, H. Attiya, S. Gilbert, A. Giurgiu, and R. Guerraoui, “Fast randomized test-and-set and renaming,” presented at the DISC: Distributed Computing, 2010, vol. 6343 LNCS, pp. 94–108.
View | DOI
 
[5]
2010 | Published | Conference Paper | IST-REx-ID: 755
D.-A. Alistarh, S. Gilbert, R. Guerraoui, and M. Zadimoghaddam, “How efficient can gossip be? (On the cost of resilient information exchange),” presented at the ICALP: International Colloquium on Automota, Languages and Programming, 2010, vol. 6199 LNCS, no. PART 2, pp. 115–126.
View | DOI
 
[4]
2010 | Published | Conference Paper | IST-REx-ID: 756
D.-A. Alistarh, S. Gilbert, R. Guerraoui, Ž. Milošević, and C. Newport, “Securing every bit: Authenticated broadcast in radio networks,” presented at the SPAA: Symposium on Parallelism in Algorithms and Architectures, 2010, pp. 50–59.
View | DOI
 
[3]
2010 | Published | Conference Paper | IST-REx-ID: 758
D.-A. Alistarh, S. Gilbert, R. Guerraoui, and C. Travers, “Brief announcement: New bounds for partially synchronous set agreement,” presented at the DISC: Distributed Computing, 2010, vol. 6343 LNCS, pp. 404–405.
View | DOI
 
[2]
2009 | Published | Conference Paper | IST-REx-ID: 752
D.-A. Alistarh, S. Gilbert, R. Guerraoui, and C. Travers, “Of choices, failures and asynchrony: the many faces of set agreement,” presented at the ISAAC: International Symposium on Algorithms and Computation, 2009, vol. 5878 LNCS, pp. 943–953.
View | DOI
 
[1]
2008 | Published | Conference Paper | IST-REx-ID: 753
D.-A. Alistarh, S. Gilbert, R. Guerraoui, and C. Travers, “How to solve consensus in the smallest window of synchrony,” presented at the DISC: Distributed Computing, 2008, vol. 5218 LNCS, pp. 32–46.
View | DOI
 
Marked Publication(s)

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3 Grants


Elastic Coordination for Scalable Machine Learning (ScaleML)

2019-03-01 – 2024-02-29
EC/H2020

Algorithms for a Computational Revolution

2018-10-01 – 2019-08-31
SNF

Vienna Graduate School on Computational Optimization (VGSCO)

2020-03-01 – 2024-02-28
FWF

134 Publications

Mark all

[134]
2024 | Published | Conference Paper | IST-REx-ID: 17093 | OA
H. Zakerinia, S. Talaei, G. Nadiradze, and D.-A. Alistarh, “Communication-efficient federated learning with data and client heterogeneity,” in Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, Valencia, Spain, 2024, vol. 238, pp. 3448–3456.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[133]
2024 | Published | Conference Paper | IST-REx-ID: 17329 | OA
D.-A. Alistarh, K. Chatterjee, M. Karrabi, and J. M. Lazarsfeld, “Game dynamics and equilibrium computation in the population protocol model,” in Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing, Nantes, France, 2024, pp. 40–49.
[Published Version] View | Files available | DOI
 
[132]
2024 | Published | Conference Paper | IST-REx-ID: 17332 | OA
I. Kokorin, V. Yudov, V. Aksenov, and D.-A. Alistarh, “Wait-free trees with asymptotically-efficient range queries,” in 2024 IEEE International Parallel and Distributed Processing Symposium, San Francisco, CA, United States, 2024, pp. 169–179.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[131]
2024 | Published | Conference Paper | IST-REx-ID: 17456 | OA
I. Markov, K. Alimohammadi, E. Frantar, and D.-A. Alistarh, “L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning,” in Proceedings of Machine Learning and Systems , Athens, Greece, 2024, vol. 6.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[130]
2024 | Published | Conference Paper | IST-REx-ID: 15011 | OA
E. Kurtic, T. Hoefler, and D.-A. Alistarh, “How to prune your language model: Recovering accuracy on the ‘Sparsity May Cry’ benchmark,” in Proceedings of Machine Learning Research, Hongkong, China, 2024, vol. 234, pp. 542–553.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[129]
2024 | Published | Conference Paper | IST-REx-ID: 18061 | OA
E. Frantar and D.-A. Alistarh, “QMoE: Sub-1-bit compression of trillion parameter models,” in Proceedings of Machine Learning and Systems, Santa Clara, CA, USA, 2024, vol. 6.
[Published Version] View | Files available | Download Published Version (ext.)
 
[128]
2024 | Published | Conference Paper | IST-REx-ID: 18062 | OA
E. Frantar, C. R. Ruiz, N. Houlsby, D.-A. Alistarh, and U. Evci, “Scaling laws for sparsely-connected foundation models,” in The Twelfth International Conference on Learning Representations, Vienna, Austria, 2024.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[127]
2024 | Published | Conference Paper | IST-REx-ID: 18070
B. Chatterjee, V. Kungurtsev, and D.-A. Alistarh, “Federated SGD with local asynchrony,” in Proceedings of the 44th International Conference on Distributed Computing Systems, Jersey City, NJ, United States, 2024, pp. 857–868.
View | DOI
 
[126]
2024 | Published | Conference Paper | IST-REx-ID: 18113 | OA
V. Egiazarian, A. Panferov, D. Kuznedelev, E. Frantar, A. Babenko, and D.-A. Alistarh, “Extreme compression of large language models via additive quantization,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 12284–12303.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[125]
2024 | Published | Conference Paper | IST-REx-ID: 18117 | OA
M. Nikdan, S. Tabesh, E. Crncevic, and D.-A. Alistarh, “RoSA: Accurate parameter-efficient fine-tuning via robust adaptation,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 38187–38206.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[124]
2024 | Published | Conference Paper | IST-REx-ID: 18121 | OA
A. S. Moakhar, E. B. Iofinova, E. Frantar, and D.-A. Alistarh, “SPADE: Sparsity-guided debugging for deep neural networks,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 45955–45987.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[123]
2023 | Published | Journal Article | IST-REx-ID: 13179 | OA
N. Koval, D. Khalanskiy, and D.-A. Alistarh, “CQS: A formally-verified framework for fair and abortable synchronization,” Proceedings of the ACM on Programming Languages, vol. 7. Association for Computing Machinery , 2023.
[Published Version] View | Files available | DOI
 
[122]
2023 | Published | Conference Paper | IST-REx-ID: 13262 | OA
A. Fedorov, D. Hashemi, G. Nadiradze, and D.-A. Alistarh, “Provably-efficient and internally-deterministic parallel Union-Find,” in Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures, Orlando, FL, United States, 2023, pp. 261–271.
[Published Version] View | Files available | DOI | arXiv
 
[121]
2023 | Published | Conference Paper | IST-REx-ID: 14260 | OA
N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, and D.-A. Alistarh, “Lincheck: A practical framework for testing concurrent data structures on JVM,” in 35th International Conference on Computer Aided Verification , Paris, France, 2023, vol. 13964, pp. 156–169.
[Published Version] View | Files available | DOI
 
[120]
2023 | Published | Journal Article | IST-REx-ID: 14364 | OA
D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, and L. Zhu, “Why extension-based proofs fail,” SIAM Journal on Computing, vol. 52, no. 4. Society for Industrial and Applied Mathematics, pp. 913–944, 2023.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[119]
2023 | Published | Conference Paper | IST-REx-ID: 14458 | OA
E. Frantar and D.-A. Alistarh, “SparseGPT: Massive language models can be accurately pruned in one-shot,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 10323–10337.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[118]
2023 | Published | Conference Paper | IST-REx-ID: 14460 | OA
M. Nikdan, T. Pegolotti, E. B. Iofinova, E. Kurtic, and D.-A. Alistarh, “SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 26215–26227.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[117]
2023 | Published | Conference Paper | IST-REx-ID: 14461 | OA
I. Markov, A. Vladu, Q. Guo, and D.-A. Alistarh, “Quantized distributed training of large models with convergence guarantees,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 24020–24044.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[116]
2023 | Published | Conference Paper | IST-REx-ID: 17378 | OA
E. Frantar, S. Ashkboos, T. Hoefler, and D.-A. Alistarh, “OPTQ: Accurate post-training quantization for generative pre-trained transformers,” in 11th International Conference on Learning Representations , Kigali, Rwanda, 2023.
[Published Version] View | Files available
 
[115]
2023 | Published | Journal Article | IST-REx-ID: 12330 | OA
V. Aksenov, D.-A. Alistarh, A. Drozdova, and A. Mohtashami, “The splay-list: A distribution-adaptive concurrent skip-list,” Distributed Computing, vol. 36. Springer Nature, pp. 395–418, 2023.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[114]
2023 | Published | Journal Article | IST-REx-ID: 12566 | OA
D.-A. Alistarh, F. Ellen, and J. Rybicki, “Wait-free approximate agreement on graphs,” Theoretical Computer Science, vol. 948, no. 2. Elsevier, 2023.
[Published Version] View | Files available | DOI | WoS
 
[113]
2023 | Published | Conference Paper | IST-REx-ID: 12735 | OA
N. Koval, D.-A. Alistarh, and R. Elizarov, “Fast and scalable channels in Kotlin Coroutines,” in Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Montreal, QC, Canada, 2023, pp. 107–118.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[112]
2023 | Published | Conference Paper | IST-REx-ID: 13053 | OA
A. Krumes, A. Vladu, E. Kurtic, C. Lampert, and D.-A. Alistarh, “CrAM: A Compression-Aware Minimizer,” in 11th International Conference on Learning Representations , Kigali, Rwanda , 2023.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[111]
2023 | Published | Conference Paper | IST-REx-ID: 14771 | OA
E. B. Iofinova, A. Krumes, and D.-A. Alistarh, “Bias in pruned vision models: In-depth analysis and countermeasures,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 2023, pp. 24364–24373.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[110]
2023 | Research Data Reference | IST-REx-ID: 14995 | OA
N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, and D.-A. Alistarh, “Lincheck: A practical framework for testing concurrent data structures on JVM.” Zenodo, 2023.
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[109]
2023 | Published | Conference Paper | IST-REx-ID: 15363 | OA
M. Safaryan, A. Krumes, and D.-A. Alistarh, “Knowledge distillation performs partial variance reduction,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2023, vol. 36.
[Published Version] View | Files available | arXiv
 
[108]
2022 | Published | Conference Paper | IST-REx-ID: 11180 | OA
A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can be state-of-the-art priority schedulers,” in Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Seoul, Republic of Korea, 2022, pp. 353–367.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[107]
2022 | Published | Conference Paper | IST-REx-ID: 11181 | OA
T. A. Brown, W. Sigouin, and D.-A. Alistarh, “PathCAS: An efficient middle ground for concurrent search data structures,” in Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Seoul, Republic of Korea, 2022, pp. 385–399.
[Published Version] View | Files available | DOI | WoS
 
[106]
2022 | Published | Conference Paper | IST-REx-ID: 11184 | OA
D.-A. Alistarh, R. Gelashvili, and J. Rybicki, “Fast graphical population protocols,” in 25th International Conference on Principles of Distributed Systems, Strasbourg, France, 2022, vol. 217.
[Published Version] View | Files available | DOI | arXiv
 
[105]
2022 | Published | Conference Paper | IST-REx-ID: 11844 | OA
D.-A. Alistarh, J. Rybicki, and S. Voitovych, “Near-optimal leader election in population protocols on graphs,” in Proceedings of the Annual ACM Symposium on Principles of Distributed Computing, Salerno, Italy, 2022, pp. 246–256.
[Published Version] View | Files available | DOI | arXiv
 
[104]
2022 | Published | Conference Paper | IST-REx-ID: 17059 | OA
E. Frantar and D.-A. Alistarh, “SPDY: Accurate pruning with speedup guarantees,” in 39th International Conference on Machine Learning, Baltimore, MD, United States, 2022, vol. 162, pp. 6726–6743.
[Published Version] View | Files available | WoS
 
[103]
2022 | Published | Conference Paper | IST-REx-ID: 17087 | OA
E. Frantar, S. P. Singh, and D.-A. Alistarh, “Optimal brain compression: A framework for accurate post-training quantization and pruning,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2022, vol. 35.
[Submitted Version] View | Files available | arXiv
 
[102]
2022 | Published | Conference Paper | IST-REx-ID: 17088 | OA
E. Kurtic et al., “The optimal BERT surgeon: Scalable and accurate second-order pruning for large language models,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022, pp. 4163–4181.
[Published Version] View | Files available | DOI | arXiv
 
[101]
2022 | Published | Conference Paper | IST-REx-ID: 12299 | OA
E. B. Iofinova, A. Krumes, M. Kurtz, and D.-A. Alistarh, “How well do sparse ImageNet models transfer?,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, United States, 2022, pp. 12256–12266.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[100]
2022 | Published | Conference Paper | IST-REx-ID: 12780 | OA
I. Markov, H. Ramezanikebrya, and D.-A. Alistarh, “CGX: Adaptive system support for communication-efficient deep learning,” in Proceedings of the 23rd ACM/IFIP International Middleware Conference, Quebec, QC, Canada, 2022, pp. 241–254.
[Published Version] View | Files available | DOI | arXiv
 
[99]
2022 | Research Data Reference | IST-REx-ID: 13076 | OA
A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can be state-of-the-art priority schedulers.” Zenodo, 2022.
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
[98]
2022 | Published | Journal Article | IST-REx-ID: 8286 | OA
D.-A. Alistarh, G. Nadiradze, and A. Sabour, “Dynamic averaging load balancing on cycles,” Algorithmica, vol. 84, no. 4. Springer Nature, pp. 1007–1029, 2022.
[Published Version] View | Files available | DOI | WoS | arXiv
 
[97]
2021 | Published | Journal Article | IST-REx-ID: 10180 | OA
T. Hoefler, D.-A. Alistarh, T. Ben-Nun, N. Dryden, and E.-A. Peste, “Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks,” Journal of Machine Learning Research, vol. 22, no. 241. Journal of Machine Learning Research, pp. 1–124, 2021.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[96]
2021 | Published | Conference Paper | IST-REx-ID: 10217 | OA
D.-A. Alistarh, R. Gelashvili, and G. Nadiradze, “Lower bounds for shared-memory leader election under bounded write contention,” in 35th International Symposium on Distributed Computing, Freiburg, Germany, 2021, vol. 209.
[Published Version] View | Files available | DOI
 
[95]
2021 | Published | Conference Paper | IST-REx-ID: 10218 | OA
D.-A. Alistarh, R. Gelashvili, and J. Rybicki, “Brief announcement: Fast graphical population protocols,” in 35th International Symposium on Distributed Computing, Freiburg, Germany, 2021, vol. 209.
[Published Version] View | Files available | DOI | arXiv
 
[94]
2021 | Published | Conference Paper | IST-REx-ID: 9543 | OA
P. Davies, V. Gurunanthan, N. Moshrefi, S. Ashkboos, and D.-A. Alistarh, “New bounds for distributed mean estimation and variance reduction,” in 9th International Conference on Learning Representations, Virtual, 2021.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[93]
2021 | Published | Journal Article | IST-REx-ID: 9571 | OA
A. Ramezani-Kebrya, F. Faghri, I. Markov, V. Aksenov, D.-A. Alistarh, and D. M. Roy, “NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization,” Journal of Machine Learning Research, vol. 22, no. 114. Journal of Machine Learning Research, p. 1−43, 2021.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[92]
2021 | Published | Conference Paper | IST-REx-ID: 9620 | OA
D.-A. Alistarh and P. Davies, “Collecting coupons is faster with friends,” in Structural Information and Communication Complexity, Wrocław, Poland, 2021, vol. 12810, pp. 3–12.
[Preprint] View | Files available | DOI
 
[91]
2021 | Published | Conference Paper | IST-REx-ID: 9823 | OA
D.-A. Alistarh, F. Ellen, and J. Rybicki, “Wait-free approximate agreement on graphs,” in Structural Information and Communication Complexity, Wrocław, Poland, 2021, vol. 12810, pp. 87–105.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[90]
2021 | Published | Conference Paper | IST-REx-ID: 9951
D.-A. Alistarh, M. Töpfer, and P. Uznański, “Comparison dynamics in population protocols,” in Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, Virtual, Italy, 2021, pp. 55–65.
View | DOI | WoS
 
[89]
2021 | Published | Journal Article | IST-REx-ID: 8723 | OA
S. Li et al., “Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging,” IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 7. IEEE, 2021.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[88]
2021 | Published | Conference Paper | IST-REx-ID: 13147 | OA
F. Alimisis, P. Davies, and D.-A. Alistarh, “Communication-efficient distributed optimization with quantized preconditioners,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 196–206.
[Published Version] View | Files available | arXiv
 
[87]
2021 | Published | Conference Paper | IST-REx-ID: 10853 | OA
A. Fedorov, N. Koval, and D.-A. Alistarh, “A scalable concurrent algorithm for dynamic connectivity,” in Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures, Virtual, Online, 2021, pp. 208–220.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[86]
2021 | Published | Conference Paper | IST-REx-ID: 11436 | OA
V. Kungurtsev, M. Egan, B. Chatterjee, and D.-A. Alistarh, “Asynchronous optimization methods for efficient training of deep neural networks with guarantees,” in 35th AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual, Online, 2021, vol. 35, no. 9B, pp. 8209–8216.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[85]
2021 | Published | Conference Paper | IST-REx-ID: 11452 | OA
F. Alimisis, P. Davies, B. Vandereycken, and D.-A. Alistarh, “Distributed principal component analysis with limited communication,” in Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 4, pp. 2823–2834.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[84]
2021 | Published | Conference Paper | IST-REx-ID: 11458 | OA
A. Krumes, E. B. Iofinova, A. Vladu, and D.-A. Alistarh, “AC/DC: Alternating Compressed/DeCompressed training of deep neural networks,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 8557–8570.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[83]
2021 | Published | Conference Paper | IST-REx-ID: 11463 | OA
E. Frantar, E. Kurtic, and D.-A. Alistarh, “M-FAC: Efficient matrix-free approximations of second-order information,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 14873–14886.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[82]
2021 | Published | Conference Paper | IST-REx-ID: 11464 | OA
D.-A. Alistarh and J. Korhonen, “Towards tight communication lower bounds for distributed optimisation,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 7254–7266.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[81]
2021 | Published | Conference Paper | IST-REx-ID: 10435 | OA
G. Nadiradze, A. Sabour, P. Davies, S. Li, and D.-A. Alistarh, “Asynchronous decentralized SGD with quantized and local updates,” in 35th Conference on Neural Information Processing Systems, Sydney, Australia, 2021.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[80]
2021 | Published | Conference Paper | IST-REx-ID: 10432 | OA
G. Nadiradze, I. Markov, B. Chatterjee, V. Kungurtsev, and D.-A. Alistarh, “Elastic consistency: A practical consistency model for distributed stochastic gradient descent,” in Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35, no. 10, pp. 9037–9045.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[79]
2020 | Published | Conference Paper | IST-REx-ID: 9631 | OA
V. Aksenov, D.-A. Alistarh, and J. Korhonen, “Scalable belief propagation via relaxed scheduling,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 22361–22372.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[78]
2020 | Published | Conference Paper | IST-REx-ID: 9632 | OA
S. P. Singh and D.-A. Alistarh, “WoodFisher: Efficient second-order approximation for neural network compression,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 18098–18109.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[77]
2020 | Published | Conference Paper | IST-REx-ID: 8722 | OA
S. Li, T. B.-N. Tal Ben-Nun, S. D. Girolamo, D.-A. Alistarh, and T. Hoefler, “Taming unbalanced training workloads in deep learning with partial collective operations,” in Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, San Diego, CA, United States, 2020, pp. 45–61.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[76]
2020 | Published | Conference Paper | IST-REx-ID: 8724 | OA
N. H. Konstantinov, E. Frantar, D.-A. Alistarh, and C. Lampert, “On the sample complexity of adversarial multi-source PAC learning,” in Proceedings of the 37th International Conference on Machine Learning, Online, 2020, vol. 119, pp. 5416–5425.
[Published Version] View | Files available | arXiv
 
[75]
2020 | Published | Conference Paper | IST-REx-ID: 8725 | OA
V. Aksenov, D.-A. Alistarh, A. Drozdova, and A. Mohtashami, “The splay-list: A distribution-adaptive concurrent skip-list,” in 34th International Symposium on Distributed Computing, Freiburg, Germany, 2020, vol. 179, p. 3:1-3:18.
[Published Version] View | Files available | DOI | arXiv
 
[74]
2020 | Conference Paper | IST-REx-ID: 9415 | OA
M. Kurtz et al., “Inducing and exploiting activation sparsity for fast neural network inference,” in 37th International Conference on Machine Learning, ICML 2020, Online, 2020, vol. 119, pp. 5533–5543.
[Published Version] View | Files available
 
[73]
2020 | Published | Conference Paper | IST-REx-ID: 15077 | OA
D.-A. Alistarh, G. Nadiradze, and A. Sabour, “Dynamic averaging load balancing on cycles,” in 47th International Colloquium on Automata, Languages, and Programming, Saarbrücken, Germany, Virtual, 2020, vol. 168.
[Published Version] View | Files available | DOI | arXiv
 
[72]
2020 | Published | Conference Paper | IST-REx-ID: 15086 | OA
F. Faghri, I. Tabrizian, I. Markov, D.-A. Alistarh, D. Roy, and A. Ramezani-Kebrya, “Adaptive gradient quantization for data-parallel SGD,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[71]
2020 | Published | Conference Paper | IST-REx-ID: 7605 | OA
D.-A. Alistarh, A. Fedorov, and N. Koval, “In search of the fastest concurrent union-find algorithm,” in 23rd International Conference on Principles of Distributed Systems, Neuchatal, Switzerland, 2020, vol. 153, p. 15:1-15:16.
[Published Version] View | Files available | DOI | arXiv
 
[70]
2020 | Published | Conference Paper | IST-REx-ID: 7635
N. Koval, M. Sokolova, A. Fedorov, D.-A. Alistarh, and D. Tsitelov, “Testing concurrency on the JVM with Lincheck,” in Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, San Diego, CA, United States, 2020, pp. 423–424.
View | DOI
 
[69]
2020 | Published | Conference Paper | IST-REx-ID: 7636 | OA
T. A. Brown, A. Prokopec, and D.-A. Alistarh, “Non-blocking interpolation search trees with doubly-logarithmic running time,” in Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, San Diego, CA, United States, 2020, pp. 276–291.
[Published Version] View | DOI | Download Published Version (ext.) | WoS
 
[68]
2020 | Published | Conference Paper | IST-REx-ID: 8191
D.-A. Alistarh, T. A. Brown, and N. Singhal, “Memory tagging: Minimalist synchronization for scalable concurrent data structures,” in Annual ACM Symposium on Parallelism in Algorithms and Architectures, Virtual Event, United States, 2020, no. 7, pp. 37–49.
View | DOI | WoS
 
[67]
2020 | Published | Journal Article | IST-REx-ID: 8268 | OA
N. M. Gurel et al., “Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications,” IEEE Transactions on Signal Processing, vol. 68. IEEE, pp. 4268–4282, 2020.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[66]
2020 | Published | Conference Paper | IST-REx-ID: 8383
D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, and L. Zhu, “Brief Announcement: Why Extension-Based Proofs Fail,” in Proceedings of the 39th Symposium on Principles of Distributed Computing, Virtual, Italy, 2020, pp. 54–56.
View | DOI
 
[65]
2019 | Published | Conference Paper | IST-REx-ID: 7437 | OA
C. Yu et al., “Distributed learning over unreliable networks,” in 36th International Conference on Machine Learning, ICML 2019, Long Beach, CA, United States, 2019, vol. 2019–June, pp. 12481–12512.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
[64]
2019 | Published | Conference Paper | IST-REx-ID: 7542 | OA
C. Wendler, D.-A. Alistarh, and M. Püschel, “Powerset convolutional neural networks,” presented at the NIPS: Conference on Neural Information Processing Systems, Vancouver, Canada, 2019, vol. 32, pp. 927–938.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 
[63]
2019 | Published | Conference Paper | IST-REx-ID: 6676 | OA
D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, and L. Zhu, “Why extension-based proofs fail,” in Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, Phoenix, AZ, United States, 2019, pp. 986–996.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[62]
2019 | Published | Conference Paper | IST-REx-ID: 7122
S. Khirirat, M. Johansson, and D.-A. Alistarh, “Gradient compression for communication-limited convex optimization,” in 2018 IEEE Conference on Decision and Control, Miami Beach, FL, United States, 2019.
View | DOI | WoS
 
[61]
2019 | Published | Conference Paper | IST-REx-ID: 7201 | OA
C. Renggli, S. Ashkboos, M. Aghagolzadeh, D.-A. Alistarh, and T. Hoefler, “SparCML: High-performance sparse communication for machine learning,” in International Conference for High Performance Computing, Networking, Storage and Analysis, SC, Denver, CO, Unites States, 2019.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[60]
2019 | Published | Conference Paper | IST-REx-ID: 7228
N. Koval, D.-A. Alistarh, and R. Elizarov, “Scalable FIFO channels for programming via communicating sequential processes,” in 25th Anniversary of Euro-Par, Göttingen, Germany, 2019, vol. 11725, pp. 317–333.
View | DOI | WoS
 
[59]
2019 | Published | Conference Paper | IST-REx-ID: 6673 | OA
D.-A. Alistarh, G. Nadiradze, and N. Koval, “Efficiency guarantees for parallel incremental algorithms under relaxed schedulers,” in 31st ACM Symposium on Parallelism in Algorithms and Architectures, Phoenix, AZ, United States, 2019, pp. 145–154.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[58]
2019 | Published | Conference Poster | IST-REx-ID: 6485
N. Koval, D.-A. Alistarh, and R. Elizarov, Lock-free channels for programming via communicating sequential processes. ACM, 2019, pp. 417–418.
View | DOI | WoS
 
[57]
2018 | Published | Journal Article | IST-REx-ID: 536 | OA
D.-A. Alistarh, J. Aspnes, V. King, and J. Saia, “Communication-efficient randomized consensus,” Distributed Computing, vol. 31, no. 6. Springer, pp. 489–501, 2018.
[Published Version] View | Files available | DOI
 
[56]
2018 | Published | Conference Paper | IST-REx-ID: 5961
D.-A. Alistarh, “A brief tutorial on distributed and concurrent machine learning,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 487–488.
View | DOI | WoS
 
[55]
2018 | Published | Conference Paper | IST-REx-ID: 5962 | OA
D.-A. Alistarh, C. De Sa, and N. H. Konstantinov, “The convergence of stochastic gradient descent in asynchronous shared memory,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 169–178.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[54]
2018 | Published | Conference Paper | IST-REx-ID: 5963 | OA
D.-A. Alistarh, T. A. Brown, J. Kopinsky, and G. Nadiradze, “Relaxed schedulers can efficiently parallelize iterative algorithms,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 377–386.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[53]
2018 | Published | Conference Paper | IST-REx-ID: 5964 | OA
V. Aksenov, D.-A. Alistarh, and P. Kuznetsov, “Brief Announcement: Performance prediction for coarse-grained locking,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 411–413.
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[52]
2018 | Published | Conference Paper | IST-REx-ID: 5966 | OA
D.-A. Alistarh, S. K. Haider, R. Kübler, and G. Nadiradze, “The transactional conflict problem,” in Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, Vienna, Austria, 2018, pp. 383–392.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[51]
2018 | Published | Journal Article | IST-REx-ID: 6001
D.-A. Alistarh, W. Leiserson, A. Matveev, and N. Shavit, “ThreadScan: Automatic and scalable memory reclamation,” ACM Transactions on Parallel Computing, vol. 4, no. 4. Association for Computing Machinery, 2018.
View | Files available | DOI
 
[50]
2018 | Published | Conference Paper | IST-REx-ID: 6031
A. Stojanov, T. M. Smith, D.-A. Alistarh, and M. Puschel, “Fast quantized arithmetic on x86: Trading compute for data movement,” in 2018 IEEE International Workshop on Signal Processing Systems, Cape Town, South Africa, 2018, vol. 2018–October.
View | DOI | WoS
 
[49]
2018 | Published | Conference Paper | IST-REx-ID: 7812 | OA
A. Polino, R. Pascanu, and D.-A. Alistarh, “Model compression via distillation and quantization,” in 6th International Conference on Learning Representations, Vancouver, Canada, 2018.
[Published Version] View | Files available | arXiv
 
[48]
2018 | Published | Conference Paper | IST-REx-ID: 6558 | OA
D.-A. Alistarh, Z. Allen-Zhu, and J. Li, “Byzantine stochastic gradient descent,” in Advances in Neural Information Processing Systems, Montreal, Canada, 2018, vol. 2018, pp. 4613–4623.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 
[47]
2018 | Published | Conference Paper | IST-REx-ID: 6589 | OA
D.-A. Alistarh, T. Hoefler, M. Johansson, N. H. Konstantinov, S. Khirirat, and C. Renggli, “The convergence of sparsified gradient methods,” in Advances in Neural Information Processing Systems 31, Montreal, Canada, 2018, vol. Volume 2018, pp. 5973–5983.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
[46]
2018 | Published | Conference Paper | IST-REx-ID: 7116 | OA
D. Grubic, L. Tam, D.-A. Alistarh, and C. Zhang, “Synchronous multi-GPU training for deep learning with low-precision communications: An empirical study,” in Proceedings of the 21st International Conference on Extending Database Technology, Vienna, Austria, 2018, pp. 145–156.
[Published Version] View | Files available | DOI
 
[45]
2018 | Published | Conference Paper | IST-REx-ID: 7123 | OA
D.-A. Alistarh, J. Aspnes, and R. Gelashvili, “Space-optimal majority in population protocols,” in Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, United States, 2018, pp. 2221–2239.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[44]
2018 | Published | Conference Paper | IST-REx-ID: 5965 | OA
D.-A. Alistarh, T. A. Brown, J. Kopinsky, J. Z. Li, and G. Nadiradze, “Distributionally linearizable data structures,” in Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, Vienna, Austria, 2018, pp. 133–142.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[43]
2017 | Published | Conference Paper | IST-REx-ID: 431 | OA
D.-A. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnović, “QSGD: Communication-efficient SGD via gradient quantization and encoding,” presented at the NIPS: Neural Information Processing System, Long Beach, CA, United States, 2017, vol. 2017, pp. 1710–1721.
[Submitted Version] View | Download Submitted Version (ext.) | arXiv
 
[42]
2017 | Published | Conference Paper | IST-REx-ID: 432 | OA
H. Zhang, J. Li, K. Kara, D.-A. Alistarh, J. Liu, and C. Zhang, “ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning,” in Proceedings of Machine Learning Research, Sydney, Australia, 2017, vol. 70, pp. 4035–4043.
[Submitted Version] View | Files available
 
[41]
2017 | Published | Conference Paper | IST-REx-ID: 487
G. Baig, B. Radunovic, D.-A. Alistarh, M. Balkwill, T. Karagiannis, and L. Qiu, “Towards unlicensed cellular networks in TV white spaces,” in Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies, Incheon, South Korea, 2017, pp. 2–14.
View | DOI
 
[40]
2017 | Published | Conference Paper | IST-REx-ID: 787 | OA
D.-A. Alistarh, J. Aspnes, D. Eisenstat, R. Rivest, and R. Gelashvili, “Time-space trade-offs in population protocols,” presented at the SODA: Symposium on Discrete Algorithms, 2017, pp. 2560–2579.
View | DOI | Download None (ext.)
 
[39]
2017 | Published | Conference Paper | IST-REx-ID: 788 | OA
D.-A. Alistarh, B. Dudek, A. Kosowski, D. Soloveichik, and P. Uznański, “Robust detection in leak-prone population protocols,” presented at the DNA Computing and Molecular Programming, 2017, vol. 10467 LNCS, pp. 155–171.
View | DOI | Download None (ext.) | arXiv
 
[38]
2017 | Published | Conference Paper | IST-REx-ID: 789
D.-A. Alistarh, W. Leiserson, A. Matveev, and N. Shavit, “Forkscan: Conservative memory reclamation for modern operating systems,” presented at the EuroSys: European Conference on Computer Systems, 2017, pp. 483–498.
View | DOI
 
[37]
2017 | Published | Conference Paper | IST-REx-ID: 790
K. Kara, D.-A. Alistarh, G. Alonso, O. Mutlu, and C. Zhang, “FPGA-accelerated dense linear machine learning: A precision-convergence trade-off,” presented at the FCCM: Field-Programmable Custom Computing Machines, 2017, pp. 160–167.
View | DOI
 
[36]
2017 | Published | Conference Paper | IST-REx-ID: 791 | OA
D.-A. Alistarh, J. Kopinsky, J. Li, and G. Nadiradze, “The power of choice in priority scheduling,” in Proceedings of the ACM Symposium on Principles of Distributed Computing, Washington, WA, USA, 2017, vol. Part F129314, pp. 283–292.
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 
[35]
2016 | Published | Conference Paper | IST-REx-ID: 785
S. Haider, W. Hasenplaugh, and D.-A. Alistarh, “Lease/Release: Architectural support for scaling contended data structures,” presented at the PPoPP: Principles and Practice of Parallel Pogramming, 2016, vol. 12-16-March-2016.
View | DOI
 
[34]
2016 | Published | Journal Article | IST-REx-ID: 786 | OA
D.-A. Alistarh, K. Censor Hillel, and N. Shavit, “Are lock free concurrent algorithms practically wait free ,” Journal of the ACM, vol. 63, no. 4. ACM, 2016.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[33]
2015 | Published | Conference Paper | IST-REx-ID: 776
D.-A. Alistarh, J. Kopinsky, J. Li, and N. Shavit, “The SprayList: A scalable relaxed priority queue,” presented at the PPoPP: Principles and Practice of Parallel Pogramming, 2015, vol. 2015–January, pp. 11–20.
View | DOI
 
[32]
2015 | Published | Conference Paper | IST-REx-ID: 777
D.-A. Alistarh, J. Iglesias, and M. Vojnović, “Streaming min-max hypergraph partitioning,” presented at the NIPS: Neural Information Processing Systems, 2015, vol. 2015–January, pp. 1900–1908.
View | Download None (ext.)
 
[31]
2015 | Published | Conference Paper | IST-REx-ID: 778 | OA
D.-A. Alistarh, J. Kopinsky, P. Kuznetsov, S. Ravi, and N. Shavit, “Inherent limitations of hybrid transactional memory,” presented at the DISC: Distributed Computing, 2015, vol. 9363, pp. 185–199.
View | DOI | Download None (ext.) | arXiv
 
[30]
2015 | Published | Conference Paper | IST-REx-ID: 779
D.-A. Alistarh, A. Matveev, W. Leiserson, and N. Shavit, “ThreadScan: Automatic and scalable memory reclamation,” presented at the SPAA: Symposium on Parallelism in Algorithms and Architectures, 2015, vol. 2015–June, pp. 123–132.
View | Files available | DOI
 
[29]
2015 | Published | Conference Paper | IST-REx-ID: 780 | OA
D.-A. Alistarh and R. Gelashvili, “Polylogarithmic-time leader election in population protocols,” presented at the ICALP: International Colloquium on Automota, Languages and Programming, 2015, vol. 9135, pp. 479–491.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[28]
2015 | Published | Conference Paper | IST-REx-ID: 781
D.-A. Alistarh, R. Gelashvili, and M. Vojnović, “Fast and exact majority in population protocols,” presented at the PODC: Principles of Distributed Computing, 2015, vol. 2015–July, pp. 47–56.
View | DOI
 
[27]
2015 | Published | Conference Paper | IST-REx-ID: 782
D.-A. Alistarh, T. Sauerwald, and M. Vojnović, “Lock-Free algorithms under stochastic schedulers,” presented at the PODC: Principles of Distributed Computing, 2015, vol. 2015–July, pp. 251–260.
View | DOI
 
[26]
2015 | Published | Conference Paper | IST-REx-ID: 783 | OA
D.-A. Alistarh, R. Gelashvili, and A. Vladu, “How to elect a leader faster than a tournament,” presented at the PODC: Principles of Distributed Computing, 2015, vol. 2015–July, pp. 365–374.
View | DOI | Download None (ext.)
 
[25]
2015 | Published | Conference Paper | IST-REx-ID: 784
D.-A. Alistarh et al., “A high-radix, low-latency optical switch for data centers,” presented at the SIGCOMM: Special Interest Group on Data Communication, London, United Kindgdom, 2015, pp. 367–368.
View | DOI
 
[24]
2014 | Published | Conference Paper | IST-REx-ID: 768
D.-A. Alistarh, J. Aspnes, M. Bender, R. Gelashvili, and S. Gilbert, “Dynamic task allocation in asynchronous shared memory,” presented at the SODA: Symposium on Discrete Algorithms, 2014, pp. 416–435.
View | DOI
 
[23]
2014 | Published | Journal Article | IST-REx-ID: 769
D.-A. Alistarh, J. Aspnes, K. Censor Hillel, S. Gilbert, and R. Guerraoui, “Tight bounds for asynchronous renaming,” Journal of the ACM, vol. 61, no. 3. ACM, 2014.
View | DOI
 
[22]
2014 | Published | Conference Paper | IST-REx-ID: 770
D.-A. Alistarh, P. Eugster, M. Herlihy, A. Matveev, and N. Shavit, “StackTrack: An automated transactional approach to concurrent memory reclamation,” presented at the EuroSys: European Conference on Computer Systems, 2014.
View | DOI
 
[21]
2014 | Published | Conference Paper | IST-REx-ID: 771
D.-A. Alistarh, O. Denysyuk, L. Rodrígues, and N. Shavit, “Balls-into-Leaves: Sub-logarithmic renaming in synchronous message-passing systems,” presented at the PODC: Principles of Distributed Computing, 2014, pp. 232–241.
View | DOI
 
[20]
2014 | Published | Conference Paper | IST-REx-ID: 772 | OA
D.-A. Alistarh, K. Censor Hillel, and N. Shavit, “Are lock-free concurrent algorithms practically wait-free?,” presented at the STOC: Symposium on Theory of Computing, 2014, pp. 714–723.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[19]
2014 | Published | Conference Paper | IST-REx-ID: 773
D.-A. Alistarh, J. Aspnes, V. King, and J. Saia, “Communication-efficient randomized consensus,” presented at the DISC: Distributed Computing, Austin, USA, 2014, vol. 8784, pp. 61–75.
View | DOI
 
[18]
2014 | Published | Conference Paper | IST-REx-ID: 774
D.-A. Alistarh, K. Censor Hille, and N. Shavit, “Brief announcement: Are lock-free concurrent algorithms practically wait-free?,” presented at the PODC: Principles of Distributed Computing, 2014, pp. 50–52.
View | DOI
 
[17]
2014 | Published | Conference Paper | IST-REx-ID: 775 | OA
D.-A. Alistarh, J. Kopinsky, A. Matveev, and N. Shavit, “The levelarray: A fast, practical long-lived renaming algorithm,” presented at the ICDCS: International Conference on Distributed Computing Systems, 2014, pp. 348–357.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[16]
2013 | Published | Conference Paper | IST-REx-ID: 765
D.-A. Alistarh, J. Aspnes, G. Giakkoupis, and P. Woelfel, “Randomized loose renaming in O(loglogn) time,” presented at the PODC: Principles of Distributed Computing, 2013, pp. 200–209.
View | DOI
 
[15]
2012 | Published | Conference Paper | IST-REx-ID: 762
D.-A. Alistarh, R. Guerraoui, P. Kuznetsov, and G. Losa, “On the cost of composing shared-memory algorithms,” presented at the SPAA: Symposium on Parallelism in Algorithms and Architectures, 2012, pp. 298–307.
View | DOI
 
[14]
2012 | Published | Conference Paper | IST-REx-ID: 763
D.-A. Alistarh, H. Attiya, R. Guerraoui, and C. Travers, “Early deciding synchronous renaming in O(log f) rounds or less,” presented at the SIROCCO: Structural Information and Communication Complexity, 2012, vol. 7355 LNCS, pp. 195–206.
View | DOI
 
[13]
2012 | Published | Journal Article | IST-REx-ID: 764
D.-A. Alistarh, S. Gilbert, R. Guerraoui, and C. Travers, “Of choices, failures and asynchrony: the many faces of set agreement,” Algorithmica (New York), vol. 62, no. 1–2. Springer, pp. 595–629, 2012.
View | DOI
 
[12]
2012 | Published | Conference Paper | IST-REx-ID: 766
D.-A. Alistarh, M. Bender, S. Gilbert, and R. Guerraoui, “How to allocate tasks asynchronously,” presented at the FOCS: Foundations of Computer Science, 2012, pp. 331–340.
View | DOI
 
[11]
2012 | Published | Journal Article | IST-REx-ID: 767
D.-A. Alistarh, S. Gilbert, R. Guerraoui, and C. Travers, “Generating Fast Indulgent Algorithms,” Theory of Computing Systems, vol. 51, no. 4. Elsevier, pp. 404–424, 2012.
View | DOI
 
[10]
2011 | Published | Conference Paper | IST-REx-ID: 757
D.-A. Alistarh, S. Gilbert, R. Guerraoui, and C. Travers, “Generating fast indulgent algorithms,” presented at the ICDCN: International Conference on Distributed Computing and Networking, 2011, vol. 6522 LNCS, pp. 41–52.
View | DOI
 
[9]
2011 | Published | Conference Paper | IST-REx-ID: 759
D.-A. Alistarh, J. Aspnes, S. Gilbert, and R. Guerraoui, “The complexity of renaming,” presented at the FOCS: Foundations of Computer Science, 2011, pp. 718–727.
View | DOI
 
[8]
2011 | Published | Conference Paper | IST-REx-ID: 760
D.-A. Alistarh and J. Aspnes, “Sub-logarithmic test-and-set against a weak adversary,” presented at the DISC: Distributed Computing, 2011, vol. 6950 LNCS, pp. 97–109.
View | DOI
 
[7]
2011 | Published | Conference Paper | IST-REx-ID: 761
D.-A. Alistarh, J. Aspnes, K. Censor Hillel, S. Gilbert, and M. Zadimoghaddam, “Optimal-time adaptive strong renaming, with applications to counting,” presented at the PODC: Principles of Distributed Computing, 2011, pp. 239–248.
View | DOI
 
[6]
2010 | Published | Conference Paper | IST-REx-ID: 754
D.-A. Alistarh, H. Attiya, S. Gilbert, A. Giurgiu, and R. Guerraoui, “Fast randomized test-and-set and renaming,” presented at the DISC: Distributed Computing, 2010, vol. 6343 LNCS, pp. 94–108.
View | DOI
 
[5]
2010 | Published | Conference Paper | IST-REx-ID: 755
D.-A. Alistarh, S. Gilbert, R. Guerraoui, and M. Zadimoghaddam, “How efficient can gossip be? (On the cost of resilient information exchange),” presented at the ICALP: International Colloquium on Automota, Languages and Programming, 2010, vol. 6199 LNCS, no. PART 2, pp. 115–126.
View | DOI
 
[4]
2010 | Published | Conference Paper | IST-REx-ID: 756
D.-A. Alistarh, S. Gilbert, R. Guerraoui, Ž. Milošević, and C. Newport, “Securing every bit: Authenticated broadcast in radio networks,” presented at the SPAA: Symposium on Parallelism in Algorithms and Architectures, 2010, pp. 50–59.
View | DOI
 
[3]
2010 | Published | Conference Paper | IST-REx-ID: 758
D.-A. Alistarh, S. Gilbert, R. Guerraoui, and C. Travers, “Brief announcement: New bounds for partially synchronous set agreement,” presented at the DISC: Distributed Computing, 2010, vol. 6343 LNCS, pp. 404–405.
View | DOI
 
[2]
2009 | Published | Conference Paper | IST-REx-ID: 752
D.-A. Alistarh, S. Gilbert, R. Guerraoui, and C. Travers, “Of choices, failures and asynchrony: the many faces of set agreement,” presented at the ISAAC: International Symposium on Algorithms and Computation, 2009, vol. 5878 LNCS, pp. 943–953.
View | DOI
 
[1]
2008 | Published | Conference Paper | IST-REx-ID: 753
D.-A. Alistarh, S. Gilbert, R. Guerraoui, and C. Travers, “How to solve consensus in the smallest window of synchrony,” presented at the DISC: Distributed Computing, 2008, vol. 5218 LNCS, pp. 32–46.
View | DOI
 
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