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168 Publications
2021 |
Published |
Conference Paper |
IST-REx-ID: 9933 |
Czumaj A, Davies P, Parter M. Component stability in low-space massively parallel computation. In: Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing. Association for Computing Machinery; 2021:481–491. doi:10.1145/3465084.3467903
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| arXiv
2021 |
Published |
Conference Paper |
IST-REx-ID: 9935 |
Czumaj A, Davies P, Parter M. Improved deterministic (Δ+1) coloring in low-space MPC. In: Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing. Association for Computing Machinery; 2021:469–479. doi:10.1145/3465084.3467937
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2021 |
Published |
Conference Paper |
IST-REx-ID: 9951
Alistarh D-A, Töpfer M, Uznański P. Comparison dynamics in population protocols. In: Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing. Association for Computing Machinery; 2021:55-65. doi:10.1145/3465084.3467915
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2021 |
Published |
Conference Paper |
IST-REx-ID: 11458 |
Krumes A, Iofinova EB, Vladu A, Alistarh D-A. AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. In: 35th Conference on Neural Information Processing Systems. Vol 34. Neural Information Processing Systems Foundation; 2021:8557-8570.
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| arXiv
2021 |
Published |
Thesis | PhD |
IST-REx-ID: 10429 |
Nadiradze G. On achieving scalability through relaxation. 2021. doi:10.15479/at:ista:10429
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2021 |
Published |
Conference Paper |
IST-REx-ID: 10435 |
Nadiradze G, Sabour A, Davies P, Li S, Alistarh D-A. Asynchronous decentralized SGD with quantized and local updates. In: 35th Conference on Neural Information Processing Systems. Neural Information Processing Systems Foundation; 2021.
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| arXiv
2021 |
Published |
Conference Paper |
IST-REx-ID: 10432 |
Nadiradze G, Markov I, Chatterjee B, Kungurtsev V, Alistarh D-A. Elastic consistency: A practical consistency model for distributed stochastic gradient descent. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol 35. ; 2021:9037-9045.
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| arXiv
2021 |
Published |
Conference Paper |
IST-REx-ID: 10049 |
Klein K, Pascual Perez G, Walter M, et al. Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement. In: 2021 IEEE Symposium on Security and Privacy . IEEE; 2021:268-284. doi:10.1109/sp40001.2021.00035
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2021 |
Published |
Conference Paper |
IST-REx-ID: 9823 |
Alistarh D-A, Ellen F, Rybicki J. Wait-free approximate agreement on graphs. In: Structural Information and Communication Complexity. Vol 12810. Springer Nature; 2021:87-105. doi:10.1007/978-3-030-79527-6_6
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| arXiv
2020 |
Published |
Conference Paper |
IST-REx-ID: 15074 |
Brandt S, Keller B, Rybicki J, Suomela J, Uitto J. Brief announcement: Efficient load-balancing through distributed token dropping. In: 34th International Symposium on Distributed Computing. Vol 179. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2020. doi:10.4230/LIPIcs.DISC.2020.40
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| arXiv
2020 |
Published |
Conference Paper |
IST-REx-ID: 15077 |
Alistarh D-A, Nadiradze G, Sabour A. Dynamic averaging load balancing on cycles. In: 47th International Colloquium on Automata, Languages, and Programming. Vol 168. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2020. doi:10.4230/LIPIcs.ICALP.2020.7
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| arXiv
2020 |
Published |
Conference Paper |
IST-REx-ID: 15086 |
Faghri F, Tabrizian I, Markov I, Alistarh D-A, Roy D, Ramezani-Kebrya A. Adaptive gradient quantization for data-parallel SGD. In: Advances in Neural Information Processing Systems. Vol 33. Neural Information Processing Systems Foundation; 2020.
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| arXiv
2020 |
Published |
Conference Paper |
IST-REx-ID: 7802 |
Czumaj A, Davies P, Parter M. Graph sparsification for derandomizing massively parallel computation with low space. In: Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2020). Association for Computing Machinery; 2020:175-185. doi:10.1145/3350755.3400282
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| arXiv
2020 |
Published |
Conference Paper |
IST-REx-ID: 7803 |
Czumaj A, Davies P, Parter M. Simple, deterministic, constant-round coloring in the congested clique. In: Proceedings of the 2020 ACM Symposium on Principles of Distributed Computing. Association for Computing Machinery; 2020:309-318. doi:10.1145/3382734.3405751
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| arXiv
2020 |
Published |
Conference Paper |
IST-REx-ID: 8191
Alistarh D-A, Brown TA, Singhal N. Memory tagging: Minimalist synchronization for scalable concurrent data structures. In: Annual ACM Symposium on Parallelism in Algorithms and Architectures. Association for Computing Machinery; 2020:37-49. doi:10.1145/3350755.3400213
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2020 |
Published |
Journal Article |
IST-REx-ID: 8268 |
Gurel NM, Kara K, Stojanov A, et al. Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications. IEEE Transactions on Signal Processing. 2020;68:4268-4282. doi:10.1109/TSP.2020.3010355
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| arXiv
2020 |
Published |
Conference Paper |
IST-REx-ID: 8383
Alistarh D-A, Aspnes J, Ellen F, Gelashvili R, Zhu L. Brief Announcement: Why Extension-Based Proofs Fail. In: Proceedings of the 39th Symposium on Principles of Distributed Computing. Association for Computing Machinery; 2020:54-56. doi:10.1145/3382734.3405743
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2020 |
Published |
Conference Paper |
IST-REx-ID: 8722 |
Li S, Tal Ben-Nun TB-N, Girolamo SD, Alistarh D-A, Hoefler T. Taming unbalanced training workloads in deep learning with partial collective operations. In: Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Association for Computing Machinery; 2020:45-61. doi:10.1145/3332466.3374528
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| arXiv
2020 |
Published |
Conference Paper |
IST-REx-ID: 8725 |
Aksenov V, Alistarh D-A, Drozdova A, Mohtashami A. The splay-list: A distribution-adaptive concurrent skip-list. In: 34th International Symposium on Distributed Computing. Vol 179. LIPIcs. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2020:3:1-3:18. doi:10.4230/LIPIcs.DISC.2020.3
[Published Version]
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| arXiv
2020 |
Conference Paper |
IST-REx-ID: 9415 |
Kurtz M, Kopinsky J, Gelashvili R, et al. Inducing and exploiting activation sparsity for fast neural network inference. In: 37th International Conference on Machine Learning, ICML 2020. Vol 119. ; 2020:5533-5543.
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