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174 Publications
2019 |
Published |
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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earlier version | 2019 |
Published |
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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| arXiv
2019 |
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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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| arXiv
2019 |
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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 |
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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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| arXiv
2019 |
Published |
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 |
Published |
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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| arXiv
2019 |
Published |
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 |
Published |
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 |
Published |
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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| arXiv
2019 |
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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. Vol 2019-June. IMLS; 2019:12481-12512.
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| arXiv
2019 |
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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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| arXiv
2018 |
Published |
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 |
Published |
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; 2018:487-488. doi:10.1145/3212734.3212798
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2018 |
Published |
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; 2018:169-178. doi:10.1145/3212734.3212763
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| arXiv
2018 |
Published |
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; 2018:377-386. doi:10.1145/3212734.3212756
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| arXiv
2018 |
Published |
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; 2018:411-413. doi:10.1145/3212734.3212785
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2018 |
Published |
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; 2018:133-142. doi:10.1145/3210377.3210411
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| arXiv
2018 |
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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; 2018:383-392. doi:10.1145/3210377.3210406
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2018 |
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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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