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121 Publications
2020 | 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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2020 | 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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2020 | 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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2019 | Journal Article | IST-REx-ID: 6759 |
Jelínek V, Töpfer M. On grounded L-graphs and their relatives. Electronic Journal of Combinatorics. 2019;26(3). doi:10.37236/8096
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2019 | Conference Paper | IST-REx-ID: 6931 |
Nowak T, Rybicki J. Byzantine approximate agreement on graphs. In: 33rd International Symposium on Distributed Computing. Vol 146. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2019:29:1--29:17. doi:10.4230/LIPICS.DISC.2019.29
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2019 | Conference Paper | IST-REx-ID: 5947 |
Chatterjee B, Peri S, Sa M, Singhal N. A simple and practical concurrent non-blocking unbounded graph with linearizable reachability queries. In: ACM International Conference Proceeding Series. ACM; 2019:168-177. doi:10.1145/3288599.3288617
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2019 | Conference Poster | IST-REx-ID: 6485
Koval N, Alistarh D-A, Elizarov R. Lock-Free Channels for Programming via Communicating Sequential Processes. ACM Press; 2019:417-418. doi:10.1145/3293883.3297000
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2019 | Journal Article | IST-REx-ID: 6936 |
Ovaskainen O, Rybicki J, Abrego N. What can observational data reveal about metacommunity processes? Ecography. 2019;42(11):1877-1886. doi:10.1111/ecog.04444
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2019 | Journal Article | IST-REx-ID: 6972 |
Lenzen C, Rybicki J. Self-stabilising Byzantine clock synchronisation is almost as easy as consensus. Journal of the ACM. 2019;66(5). doi:10.1145/3339471
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2019 | Conference Paper | IST-REx-ID: 7122
Khirirat S, Johansson M, Alistarh D-A. Gradient compression for communication-limited convex optimization. In: 2018 IEEE Conference on Decision and Control. IEEE; 2019. doi:10.1109/cdc.2018.8619625
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2019 | Conference Paper | IST-REx-ID: 7201 |
Renggli C, Ashkboos S, Aghagolzadeh M, Alistarh D-A, Hoefler T. SparCML: High-performance sparse communication for machine learning. In: International Conference for High Performance Computing, Networking, Storage and Analysis, SC. ACM; 2019. doi:10.1145/3295500.3356222
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2019 | Journal Article | IST-REx-ID: 7214 |
Aganezov S, Zban I, Aksenov V, Alexeev N, Schatz MC. Recovering rearranged cancer chromosomes from karyotype graphs. BMC Bioinformatics. 2019;20. doi:10.1186/s12859-019-3208-4
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2019 | Conference Paper | IST-REx-ID: 7228
Koval N, Alistarh D-A, Elizarov R. Scalable FIFO channels for programming via communicating sequential processes. In: 25th Anniversary of Euro-Par. Vol 11725. Springer Nature; 2019:317-333. doi:10.1007/978-3-030-29400-7_23
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2019 | Conference Paper | IST-REx-ID: 7437 |
Yu C, Tang H, Renggli C, et al. Distributed learning over unreliable networks. In: 36th International Conference on Machine Learning, ICML 2019. Vol 2019-June. IMLS; 2019:12481-12512.
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2019 | Conference Paper | IST-REx-ID: 6673 |
Alistarh D-A, Nadiradze G, Koval N. Efficiency guarantees for parallel incremental algorithms under relaxed schedulers. In: 31st ACM Symposium on Parallelism in Algorithms and Architectures. ACM Press; 2019:145-154. doi:10.1145/3323165.3323201
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2019 | Conference Paper | IST-REx-ID: 7542 |
Wendler C, Alistarh D-A, Püschel M. Powerset convolutional neural networks. In: Vol 32. Neural Information Processing Systems Foundation; 2019:927-938.
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2019 | Conference Paper | IST-REx-ID: 6935 |
Foerster K-T, Korhonen J, Rybicki J, Schmid S. Does preprocessing help under congestion? In: Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing. ACM; 2019:259-261. doi:10.1145/3293611.3331581
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2019 | Conference Paper | IST-REx-ID: 6676 |
Alistarh D-A, Aspnes J, Ellen F, Gelashvili R, Zhu L. Why extension-based proofs fail. In: Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing. ACM Press; 2019:986-996. doi:10.1145/3313276.3316407
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2019 | Conference Paper | IST-REx-ID: 6933 |
Censor-Hillel K, Dory M, Korhonen J, Leitersdorf D. Fast approximate shortest paths in the congested clique. In: Proceedings of the 2019 ACM Symposium on Principles of Distributed Computin. ACM; 2019:74-83. doi:10.1145/3293611.3331633
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2018 | Journal Article | IST-REx-ID: 536 |
Alistarh D-A, Aspnes J, King V, Saia J. Communication-efficient randomized consensus. Distributed Computing. 2018;31(6):489-501. doi:10.1007/s00446-017-0315-1
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2018 | Conference Paper | IST-REx-ID: 7116 |
Grubic D, Tam L, Alistarh D-A, Zhang C. 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. OpenProceedings; 2018:145-156. doi:10.5441/002/EDBT.2018.14
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2018 | Journal Article | IST-REx-ID: 6001
Alistarh D-A, Leiserson W, Matveev A, Shavit N. ThreadScan: Automatic and scalable memory reclamation. ACM Transactions on Parallel Computing. 2018;4(4). doi:10.1145/3201897
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2018 | Conference Paper | IST-REx-ID: 7812 |
Polino A, Pascanu R, Alistarh D-A. Model compression via distillation and quantization. In: 6th International Conference on Learning Representations. ; 2018.
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2018 | Conference Paper | IST-REx-ID: 397
Arbel Raviv M, Brown TA. Harnessing epoch-based reclamation for efficient range queries. In: Vol 53. ACM; 2018:14-27. doi:10.1145/3178487.3178489
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2018 | Journal Article | IST-REx-ID: 43 |
Rybicki J, Kisdi E, Anttila J. Model of bacterial toxin-dependent pathogenesis explains infective dose. PNAS. 2018;115(42):10690-10695. doi:10.1073/pnas.1721061115
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2018 | Journal Article | IST-REx-ID: 76 |
Lenzen C, Rybicki J. Near-optimal self-stabilising counting and firing squads. Distributed Computing. 2018. doi:10.1007/s00446-018-0342-6
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2018 | Conference Paper | IST-REx-ID: 85 |
Gilad E, Brown TA, Oskin M, Etsion Y. Snapshot based synchronization: A fast replacement for Hand-over-Hand locking. In: Vol 11014. Springer; 2018:465-479. doi:10.1007/978-3-319-96983-1_33
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2018 | Conference Paper | IST-REx-ID: 5962 |
Alistarh D-A, De Sa C, Konstantinov NH. The convergence of stochastic gradient descent in asynchronous shared memory. In: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC ’18. ACM Press; 2018:169-178. doi:10.1145/3212734.3212763
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2018 | Conference Paper | IST-REx-ID: 5961
Alistarh D-A. A brief tutorial on distributed and concurrent machine learning. In: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC ’18. ACM Press; 2018:487-488. doi:10.1145/3212734.3212798
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2018 | Conference Paper | IST-REx-ID: 5963 |
Alistarh D-A, Brown TA, Kopinsky J, Nadiradze G. Relaxed schedulers can efficiently parallelize iterative algorithms. In: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC ’18. ACM Press; 2018:377-386. doi:10.1145/3212734.3212756
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2018 | Conference Paper | IST-REx-ID: 5965 |
Alistarh D-A, Brown TA, Kopinsky J, Li JZ, Nadiradze G. Distributionally linearizable data structures. In: Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA ’18. ACM Press; 2018:133-142. doi:10.1145/3210377.3210411
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2018 | Conference Paper | IST-REx-ID: 5966 |
Alistarh D-A, Haider SK, Kübler R, Nadiradze G. The transactional conflict problem. In: Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA ’18. ACM Press; 2018:383-392. doi:10.1145/3210377.3210406
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2018 | Conference Paper | IST-REx-ID: 5964 |
Aksenov V, Alistarh D-A, Kuznetsov P. Brief Announcement: Performance prediction for coarse-grained locking. In: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC ’18. ACM Press; 2018:411-413. doi:10.1145/3212734.3212785
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2018 | Conference Paper | IST-REx-ID: 6031
Stojanov A, Smith TM, Alistarh D-A, Puschel M. Fast quantized arithmetic on x86: Trading compute for data movement. In: 2018 IEEE International Workshop on Signal Processing Systems. Vol 2018-October. IEEE; 2018. doi:10.1109/SiPS.2018.8598402
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2018 | Conference Paper | IST-REx-ID: 7123 |
Alistarh D-A, Aspnes J, Gelashvili R. Space-optimal majority in population protocols. In: Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms. ACM; 2018:2221-2239. doi:10.1137/1.9781611975031.144
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2018 | Conference Paper | IST-REx-ID: 6558 |
Alistarh D-A, Allen-Zhu Z, Li J. Byzantine stochastic gradient descent. In: Advances in Neural Information Processing Systems. Vol 2018. Neural Information Processing Systems Foundation; 2018:4613-4623.
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2018 | Conference Paper | IST-REx-ID: 6589 |
Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli C. The convergence of sparsified gradient methods. In: Advances in Neural Information Processing Systems 31. Vol Volume 2018. Neural Information Processing Systems Foundation; 2018:5973-5983.
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2017 | Conference Paper | IST-REx-ID: 487
Baig G, Radunovic B, Alistarh D-A, Balkwill M, Karagiannis T, Qiu L. Towards unlicensed cellular networks in TV white spaces. In: Proceedings of the 2017 13th International Conference on Emerging Networking EXperiments and Technologies. ACM; 2017:2-14. doi:10.1145/3143361.3143367
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2017 | Conference Paper | IST-REx-ID: 791 |
Alistarh D-A, Kopinsky J, Li J, Nadiradze G. The power of choice in priority scheduling. In: Proceedings of the ACM Symposium on Principles of Distributed Computing. Vol Part F129314. ACM; 2017:283-292. doi:10.1145/3087801.3087810
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2017 | Conference Paper | IST-REx-ID: 431 |
Alistarh D-A, Grubic D, Li J, Tomioka R, Vojnović M. QSGD: Communication-efficient SGD via gradient quantization and encoding. In: Vol 2017. Neural Information Processing Systems Foundation; 2017:1710-1721.
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2017 | Conference Paper | IST-REx-ID: 432 |
Zhang H, Li J, Kara K, Alistarh D-A, Liu J, Zhang C. ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning. In: Proceedings of Machine Learning Research. Vol 70. ML Research Press; 2017:4035-4043.
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