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123 Publications
2021 |Published| Journal Article | IST-REx-ID: 10855 |
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K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, and S. Schmid, “Input-dynamic distributed algorithms for communication networks,” Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 5, no. 1. Association for Computing Machinery, pp. 1–33, 2021.
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2021 |Published| Thesis | IST-REx-ID: 10429 |
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G. Nadiradze, “On achieving scalability through relaxation,” Institute of Science and Technology Austria, 2021.
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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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| arXiv
2021 |Published| Journal Article | IST-REx-ID: 9541 |
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A. Czumaj, P. Davies, and M. Parter, “Graph sparsification for derandomizing massively parallel computation with low space,” ACM Transactions on Algorithms, vol. 17, no. 2. Association for Computing Machinery, 2021.
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| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 9678 |
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S. Brandt, B. Keller, J. Rybicki, J. Suomela, and J. Uitto, “Efficient load-balancing through distributed token dropping,” in Annual ACM Symposium on Parallelism in Algorithms and Architectures, Virtual Event, United States, 2021, pp. 129–139.
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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
2021 |Published| Journal Article | IST-REx-ID: 7939 |
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K. Censor-Hillel, M. Dory, J. Korhonen, and D. Leitersdorf, “Fast approximate shortest paths in the congested clique,” Distributed Computing, vol. 34. Springer Nature, pp. 463–487, 2021.
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| arXiv
2021 |Published| Journal Article | IST-REx-ID: 15267 |
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A. Czumaj and P. Davies, “Exploiting spontaneous transmissions for broadcasting and leader election in radio networks,” Journal of the ACM, vol. 68, no. 2. Association for Computing Machinery, 2021.
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| arXiv
2021 |Published| Conference Paper | IST-REx-ID: 15263 |
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F. Alimisis, A. Orvieto, G. Becigneul, and A. Lucchi, “Momentum improves optimization on Riemannian manifolds,” in Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, San Diego, CA, United States; Virtual, 2021, vol. 130, pp. 1351–1359.
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| arXiv
2020 |Published| Conference Paper | IST-REx-ID: 7272 |
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M. Arbel-Raviv, T. A. Brown, and A. Morrison, “Getting to the root of concurrent binary search tree performance,” in Proceedings of the 2018 USENIX Annual Technical Conference, Boston, MA, United States, 2020, pp. 295–306.
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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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| arXiv
2020 |Published| Conference Paper | IST-REx-ID: 7803 |
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A. Czumaj, P. Davies, and M. Parter, “Simple, deterministic, constant-round coloring in the congested clique,” in Proceedings of the 2020 ACM Symposium on Principles of Distributed Computing, Salerno, Italy, 2020, pp. 309–318.
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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| Journal Article | IST-REx-ID: 7224 |
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J. Rybicki, N. Abrego, and O. Ovaskainen, “Habitat fragmentation and species diversity in competitive communities,” Ecology Letters, vol. 23, no. 3. Wiley, pp. 506–517, 2020.
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