Dan-Adrian Alistarh
Alistarh Group
121 Publications
2024 |Published| Conference Paper | IST-REx-ID: 15011 |
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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.
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2024 |Published| Conference Paper | IST-REx-ID: 17093 |
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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.
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2023 |Published| Conference Paper | IST-REx-ID: 12735 |
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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.
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2023 |Accepted| Conference Paper | IST-REx-ID: 13053 |
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E.-A. Peste, A. Vladu, E. Kurtic, C. Lampert, and D.-A. Alistarh, “CrAM: A Compression-Aware Minimizer,” in 11th International Conference on Learning Representations , Kigali, Rwanda .
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2023 |Published| Journal Article | IST-REx-ID: 13179 |
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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.
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2023 |Published| Journal Article | IST-REx-ID: 12566 |
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D.-A. Alistarh, F. Ellen, and J. Rybicki, “Wait-free approximate agreement on graphs,” Theoretical Computer Science, vol. 948, no. 2. Elsevier, 2023.
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2023 |Published| Journal Article | IST-REx-ID: 12330 |
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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.
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2023 |Published| Conference Paper | IST-REx-ID: 14461 |
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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.
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2023 |Published| Conference Paper | IST-REx-ID: 14460 |
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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.
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2023 |Published| Conference Paper | IST-REx-ID: 14458 |
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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.
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2023 |Published| Journal Article | IST-REx-ID: 14364 |
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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.
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2023 |Published| Conference Paper | IST-REx-ID: 14771 |
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E. B. Iofinova, E.-A. Peste, 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.
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2023 |Published| Conference Paper | IST-REx-ID: 14260 |
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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.
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2023 | Research Data Reference | IST-REx-ID: 14995 |
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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.
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2023 |Published| Conference Paper | IST-REx-ID: 13262 |
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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.
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2023 |Published| Conference Paper | IST-REx-ID: 15363 |
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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.
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2022 |Published| Conference Paper | IST-REx-ID: 11184 |
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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.
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2022 |Published| Conference Paper | IST-REx-ID: 12780 |
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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.
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2022 |Published| Conference Paper | IST-REx-ID: 11844 |
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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.
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2022 |Published| Conference Paper | IST-REx-ID: 11181 |
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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.
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2022 |Published| Conference Paper | IST-REx-ID: 11180 |
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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.
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2022 | Research Data Reference | IST-REx-ID: 13076 |
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A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can be state-of-the-art priority schedulers.” Zenodo, 2022.
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2022 |Published| Conference Paper | IST-REx-ID: 12299 |
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E. B. Iofinova, E.-A. Peste, 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.
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2022 |Published| Journal Article | IST-REx-ID: 8286 |
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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.
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| arXiv
2021 |Published| Journal Article | IST-REx-ID: 10180 |
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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.
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2021 |Published| Conference Paper | IST-REx-ID: 10218 |
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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.
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2021 |Published| Conference Paper | IST-REx-ID: 10217 |
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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.
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2021 |Published| Conference Paper | IST-REx-ID: 10853 |
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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.
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2021 |Published| Conference Paper | IST-REx-ID: 11436 |
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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.
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2021 |Published| Conference Paper | IST-REx-ID: 11452 |
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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.
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2021 |Published| Conference Paper | IST-REx-ID: 11463 |
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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.
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2021 |Published| Conference Paper | IST-REx-ID: 11464 |
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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.
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2021 |Published| Conference Paper | IST-REx-ID: 9543 |
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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.
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2021 |Published| Conference Paper | IST-REx-ID: 9620 |
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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.
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2021 |Published| Conference Paper | IST-REx-ID: 9823 |
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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.
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2021 |Published| Conference Paper | IST-REx-ID: 11458 |
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E.-A. Peste, 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.
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2021 |Published| Conference Paper | IST-REx-ID: 13147 |
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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.
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2021 |Published| Journal Article | IST-REx-ID: 8723 |
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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.
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2021 |Published| Conference Paper | IST-REx-ID: 10432 |
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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.
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2021 |Published| Conference Paper | IST-REx-ID: 10435 |
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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.
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2021 |Published| Journal Article | IST-REx-ID: 9571 |
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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.
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2020 |Published| Conference Paper | IST-REx-ID: 7605 |
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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.
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2020 |Published| Conference Paper | IST-REx-ID: 8725 |
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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.
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2020 |Published| Conference Paper | IST-REx-ID: 9632 |
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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.
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2020 |Published| Conference Paper | IST-REx-ID: 9631 |
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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.
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2020 | Conference Paper | IST-REx-ID: 9415 |
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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.
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2020 |Published| Journal Article | IST-REx-ID: 8268 |
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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.
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2020 |Published| Conference Paper | IST-REx-ID: 8722 |
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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.
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2020 |Published| Conference Paper | IST-REx-ID: 8724 |
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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.
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2020 |Published| Conference Paper | IST-REx-ID: 7636 |
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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.
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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.
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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.
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2020 |Published| Conference Paper | IST-REx-ID: 15086 |
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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.
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2020 |Published| Conference Paper | IST-REx-ID: 15077 |
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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.
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2019 |Published| Conference Paper | IST-REx-ID: 7201 |
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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.
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2019 |Published| Conference Paper | IST-REx-ID: 7437 |
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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.
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2019 |Published| Conference Paper | IST-REx-ID: 6673 |
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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.
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2019 |Published| Conference Paper | IST-REx-ID: 7542 |
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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.
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2019 |Published| Conference Paper | IST-REx-ID: 6676 |
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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.
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2018 |Published| Journal Article | IST-REx-ID: 536 |
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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.
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2018 |Published| Conference Paper | IST-REx-ID: 7116 |
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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.
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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.
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2018 |Published| Conference Paper | IST-REx-ID: 7812 |
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A. Polino, R. Pascanu, and D.-A. Alistarh, “Model compression via distillation and quantization,” in 6th International Conference on Learning Representations, Vancouver, Canada, 2018.
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2018 |Published| Conference Paper | IST-REx-ID: 5962 |
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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.
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2018 |Published| Conference Paper | IST-REx-ID: 5963 |
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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.
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2018 |Published| Conference Paper | IST-REx-ID: 5965 |
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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.
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2018 |Published| Conference Paper | IST-REx-ID: 5966 |
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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.
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2018 |Published| Conference Paper | IST-REx-ID: 5964 |
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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.
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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.
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2018 |Published| Conference Paper | IST-REx-ID: 7123 |
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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.
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2018 |Published| Conference Paper | IST-REx-ID: 6558 |
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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.
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2018 |Published| Conference Paper | IST-REx-ID: 6589 |
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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.
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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.
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2017 |Published| Conference Paper | IST-REx-ID: 788 |
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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.
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2017 |Published| Conference Paper | IST-REx-ID: 787 |
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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.
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2017 |Published| Conference Paper | IST-REx-ID: 791 |
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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.
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2017 |Published| Conference Paper | IST-REx-ID: 431 |
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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.
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2017 |Published| Conference Paper | IST-REx-ID: 432 |
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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.
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2016 |Published| Journal Article | IST-REx-ID: 786 |
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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.
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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.
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2015 |Published| Conference Paper | IST-REx-ID: 778 |
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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.
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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.
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2015 |Published| Conference Paper | IST-REx-ID: 780 |
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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.
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2015 |Published| Conference Paper | IST-REx-ID: 783 |
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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.
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2014 |Published| Conference Paper | IST-REx-ID: 772 |
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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.
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| arXiv
2014 |Published| Conference Paper | IST-REx-ID: 775 |
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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.
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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.
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| DOI
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.
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121 Publications
2024 |Published| Conference Paper | IST-REx-ID: 15011 |
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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.
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2024 |Published| Conference Paper | IST-REx-ID: 17093 |
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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.
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2023 |Published| Conference Paper | IST-REx-ID: 12735 |
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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.
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2023 |Accepted| Conference Paper | IST-REx-ID: 13053 |
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E.-A. Peste, A. Vladu, E. Kurtic, C. Lampert, and D.-A. Alistarh, “CrAM: A Compression-Aware Minimizer,” in 11th International Conference on Learning Representations , Kigali, Rwanda .
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2023 |Published| Journal Article | IST-REx-ID: 13179 |
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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.
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2023 |Published| Journal Article | IST-REx-ID: 12566 |
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D.-A. Alistarh, F. Ellen, and J. Rybicki, “Wait-free approximate agreement on graphs,” Theoretical Computer Science, vol. 948, no. 2. Elsevier, 2023.
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2023 |Published| Journal Article | IST-REx-ID: 12330 |
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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.
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2023 |Published| Conference Paper | IST-REx-ID: 14461 |
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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.
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| arXiv
2023 |Published| Conference Paper | IST-REx-ID: 14460 |
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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.
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2023 |Published| Conference Paper | IST-REx-ID: 14458 |
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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.
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| arXiv
2023 |Published| Journal Article | IST-REx-ID: 14364 |
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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.
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2023 |Published| Conference Paper | IST-REx-ID: 14771 |
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E. B. Iofinova, E.-A. Peste, 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.
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| arXiv
2023 |Published| Conference Paper | IST-REx-ID: 14260 |
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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.
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2023 | Research Data Reference | IST-REx-ID: 14995 |
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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.
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2023 |Published| Conference Paper | IST-REx-ID: 13262 |
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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.
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| arXiv
2023 |Published| Conference Paper | IST-REx-ID: 15363 |
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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.
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| arXiv
2022 |Published| Conference Paper | IST-REx-ID: 11184 |
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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]
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| arXiv
2022 |Published| Conference Paper | IST-REx-ID: 12780 |
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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.
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| arXiv
2022 |Published| Conference Paper | IST-REx-ID: 11844 |
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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.
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| arXiv
2022 |Published| Conference Paper | IST-REx-ID: 11181 |
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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.
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2022 |Published| Conference Paper | IST-REx-ID: 11180 |
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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.
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| arXiv
2022 | Research Data Reference | IST-REx-ID: 13076 |
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A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can be state-of-the-art priority schedulers.” Zenodo, 2022.
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2022 |Published| Conference Paper | IST-REx-ID: 12299 |
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E. B. Iofinova, E.-A. Peste, 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.
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| arXiv
2022 |Published| Journal Article | IST-REx-ID: 8286 |
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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.
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| arXiv
2021 |Published| Journal Article | IST-REx-ID: 10180 |
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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.
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| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 10218 |
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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.
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| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 10217 |
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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.
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2021 |Published| Conference Paper | IST-REx-ID: 10853 |
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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.
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| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 11436 |
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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.
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| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 11452 |
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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.
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| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 11463 |
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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.
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| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 11464 |
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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.
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| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 9543 |
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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.
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| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 9620 |
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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.
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2021 |Published| Conference Paper | IST-REx-ID: 9823 |
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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.
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| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 11458 |
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E.-A. Peste, 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.
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| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 13147 |
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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.
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| arXiv
2021 |Published| Journal Article | IST-REx-ID: 8723 |
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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.
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| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 10432 |
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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.
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| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 10435 |
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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.
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| arXiv
2021 |Published| Journal Article | IST-REx-ID: 9571 |
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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.
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| arXiv
2020 |Published| Conference Paper | IST-REx-ID: 7605 |
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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.
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| arXiv
2020 |Published| Conference Paper | IST-REx-ID: 8725 |
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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.
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| arXiv
2020 |Published| Conference Paper | IST-REx-ID: 9632 |
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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.
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| arXiv
2020 |Published| Conference Paper | IST-REx-ID: 9631 |
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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.
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| arXiv
2020 | Conference Paper | IST-REx-ID: 9415 |
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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.
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2020 |Published| Journal Article | IST-REx-ID: 8268 |
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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.
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| arXiv
2020 |Published| Conference Paper | IST-REx-ID: 8722 |
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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.
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| arXiv
2020 |Published| Conference Paper | IST-REx-ID: 8724 |
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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.
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| arXiv
2020 |Published| Conference Paper | IST-REx-ID: 7636 |
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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.
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| WoS
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.
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| WoS
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.
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| DOI
2020 |Published| Conference Paper | IST-REx-ID: 15086 |
![Open access file OA](https://research-explorer.ista.ac.at/images/access_open.png)
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.
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| arXiv
2020 |Published| Conference Paper | IST-REx-ID: 15077 |
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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.
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| arXiv
2019 |Published| Conference Paper | IST-REx-ID: 7201 |
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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.
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| arXiv
2019 |Published| Conference Paper | IST-REx-ID: 7437 |
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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.
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| arXiv
2019 |Published| Conference Paper | IST-REx-ID: 6673 |
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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.
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| arXiv
2019 |Published| Conference Paper | IST-REx-ID: 7542 |
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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.
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| arXiv
2019 |Published| Conference Paper | IST-REx-ID: 6676 |
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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.
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| arXiv
2018 |Published| Journal Article | IST-REx-ID: 536 |
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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.
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2018 |Published| Conference Paper | IST-REx-ID: 7116 |
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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.
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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.
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| DOI
2018 |Published| Conference Paper | IST-REx-ID: 7812 |
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A. Polino, R. Pascanu, and D.-A. Alistarh, “Model compression via distillation and quantization,” in 6th International Conference on Learning Representations, Vancouver, Canada, 2018.
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| arXiv
2018 |Published| Conference Paper | IST-REx-ID: 5962 |
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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.
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| arXiv
2018 |Published| Conference Paper | IST-REx-ID: 5963 |
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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.
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| arXiv
2018 |Published| Conference Paper | IST-REx-ID: 5965 |
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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.
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| arXiv
2018 |Published| Conference Paper | IST-REx-ID: 5966 |
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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.
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| arXiv
2018 |Published| Conference Paper | IST-REx-ID: 5964 |
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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.
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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.
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2018 |Published| Conference Paper | IST-REx-ID: 7123 |
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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.
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| arXiv
2018 |Published| Conference Paper | IST-REx-ID: 6558 |
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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.
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| arXiv
2018 |Published| Conference Paper | IST-REx-ID: 6589 |
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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.
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| arXiv
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.
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2017 |Published| Conference Paper | IST-REx-ID: 788 |
![Open access file OA](https://research-explorer.ista.ac.at/images/access_open.png)
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.
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| arXiv
2017 |Published| Conference Paper | IST-REx-ID: 787 |
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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.
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2017 |Published| Conference Paper | IST-REx-ID: 791 |
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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.
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2017 |Published| Conference Paper | IST-REx-ID: 431 |
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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.
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| arXiv
2017 |Published| Conference Paper | IST-REx-ID: 432 |
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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.
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2016 |Published| Journal Article | IST-REx-ID: 786 |
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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.
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| arXiv
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.
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2015 |Published| Conference Paper | IST-REx-ID: 778 |
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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.
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| arXiv
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.
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2015 |Published| Conference Paper | IST-REx-ID: 780 |
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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.
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| arXiv
2015 |Published| Conference Paper | IST-REx-ID: 783 |
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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.
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2014 |Published| Conference Paper | IST-REx-ID: 772 |
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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.
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| arXiv
2014 |Published| Conference Paper | IST-REx-ID: 775 |
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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.
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| arXiv
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.
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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.
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