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121 Publications
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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| arXiv
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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