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118 Publications


2018 | Conference Paper | IST-REx-ID: 397
Arbel Raviv, M., & Brown, T. A. (2018). Harnessing epoch-based reclamation for efficient range queries (Vol. 53, pp. 14–27). Presented at the PPoPP: Principles and Practice of Parallel Programming, Vienna, Austria: ACM. https://doi.org/10.1145/3178487.3178489
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2018 | Journal Article | IST-REx-ID: 43 | OA
Rybicki, J., Kisdi, E., & Anttila, J. (2018). Model of bacterial toxin-dependent pathogenesis explains infective dose. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1721061115
[Submitted Version] View | Files available | DOI | WoS
 

2018 | Journal Article | IST-REx-ID: 76 | OA
Lenzen, C., & Rybicki, J. (2018). Near-optimal self-stabilising counting and firing squads. Distributed Computing. Springer. https://doi.org/10.1007/s00446-018-0342-6
[Published Version] View | Files available | DOI | WoS
 

2018 | Conference Paper | IST-REx-ID: 85 | OA
Gilad, E., Brown, T. A., Oskin, M., & Etsion, Y. (2018). Snapshot based synchronization: A fast replacement for Hand-over-Hand locking (Vol. 11014, pp. 465–479). Presented at the Euro-Par: European Conference on Parallel Processing, Turin, Italy: Springer. https://doi.org/10.1007/978-3-319-96983-1_33
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2018 | Conference Paper | IST-REx-ID: 5962 | OA
Alistarh, D.-A., De Sa, C., & Konstantinov, N. H. (2018). The convergence of stochastic gradient descent in asynchronous shared memory. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 169–178). Egham, United Kingdom: ACM Press. https://doi.org/10.1145/3212734.3212763
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2018 | Conference Paper | IST-REx-ID: 5961
Alistarh, D.-A. (2018). A brief tutorial on distributed and concurrent machine learning. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 487–488). Egham, United Kingdom: ACM Press. https://doi.org/10.1145/3212734.3212798
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2018 | Conference Paper | IST-REx-ID: 5963 | OA
Alistarh, D.-A., Brown, T. A., Kopinsky, J., & Nadiradze, G. (2018). Relaxed schedulers can efficiently parallelize iterative algorithms. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 377–386). Egham, United Kingdom: ACM Press. https://doi.org/10.1145/3212734.3212756
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2018 | Conference Paper | IST-REx-ID: 5965 | OA
Alistarh, D.-A., Brown, T. A., Kopinsky, J., Li, J. Z., & Nadiradze, G. (2018). Distributionally linearizable data structures. In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18 (pp. 133–142). Vienna, Austria: ACM Press. https://doi.org/10.1145/3210377.3210411
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

2018 | Conference Paper | IST-REx-ID: 5966 | OA
Alistarh, D.-A., Haider, S. K., Kübler, R., & Nadiradze, G. (2018). The transactional conflict problem. In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18 (pp. 383–392). Vienna, Austria: ACM Press. https://doi.org/10.1145/3210377.3210406
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2018 | Conference Paper | IST-REx-ID: 5964 | OA
Aksenov, V., Alistarh, D.-A., & Kuznetsov, P. (2018). Brief Announcement: Performance prediction for coarse-grained locking. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 411–413). Egham, United Kingdom: ACM Press. https://doi.org/10.1145/3212734.3212785
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2018 | Conference Paper | IST-REx-ID: 6031
Stojanov, A., Smith, T. M., Alistarh, D.-A., & Puschel, M. (2018). Fast quantized arithmetic on x86: Trading compute for data movement. In 2018 IEEE International Workshop on Signal Processing Systems (Vol. 2018–October). Cape Town, South Africa: IEEE. https://doi.org/10.1109/SiPS.2018.8598402
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2018 | Conference Paper | IST-REx-ID: 7123 | OA
Alistarh, D.-A., Aspnes, J., & Gelashvili, R. (2018). Space-optimal majority in population protocols. In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 2221–2239). New Orleans, LA, United States: ACM. https://doi.org/10.1137/1.9781611975031.144
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2018 | Conference Paper | IST-REx-ID: 6558 | OA
Alistarh, D.-A., Allen-Zhu, Z., & Li, J. (2018). Byzantine stochastic gradient descent. In Advances in Neural Information Processing Systems (Vol. 2018, pp. 4613–4623). Montreal, Canada: Neural Information Processing Systems Foundation.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 

2018 | Conference Paper | IST-REx-ID: 6589 | OA
Alistarh, D.-A., Hoefler, T., Johansson, M., Konstantinov, N. H., Khirirat, S., & Renggli, C. (2018). The convergence of sparsified gradient methods. In Advances in Neural Information Processing Systems 31 (Vol. Volume 2018, pp. 5973–5983). Montreal, Canada: Neural Information Processing Systems Foundation.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 

2017 | Conference Paper | IST-REx-ID: 487
Baig, G., Radunovic, B., Alistarh, D.-A., Balkwill, M., Karagiannis, T., & Qiu, L. (2017). Towards unlicensed cellular networks in TV white spaces. In Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies (pp. 2–14). Incheon, South Korea: ACM. https://doi.org/10.1145/3143361.3143367
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2017 | Conference Paper | IST-REx-ID: 791 | OA
Alistarh, D.-A., Kopinsky, J., Li, J., & Nadiradze, G. (2017). The power of choice in priority scheduling. In Proceedings of the ACM Symposium on Principles of Distributed Computing (Vol. Part F129314, pp. 283–292). Washington, WA, USA: ACM. https://doi.org/10.1145/3087801.3087810
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 

2017 | Conference Paper | IST-REx-ID: 431 | OA
Alistarh, D.-A., Grubic, D., Li, J., Tomioka, R., & Vojnović, M. (2017). QSGD: Communication-efficient SGD via gradient quantization and encoding (Vol. 2017, pp. 1710–1721). Presented at the NIPS: Neural Information Processing System, Long Beach, CA, United States: Neural Information Processing Systems Foundation.
[Submitted Version] View | Download Submitted Version (ext.) | arXiv
 

2017 | Conference Paper | IST-REx-ID: 432 | OA
Zhang, H., Li, J., Kara, K., Alistarh, D.-A., Liu, J., & Zhang, C. (2017). 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, pp. 4035–4043). Sydney, Australia: ML Research Press.
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