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122 Publications


2019 | Conference Paper | IST-REx-ID: 6673 | OA
Alistarh, D.-A., Nadiradze, G., & Koval, N. (2019). Efficiency guarantees for parallel incremental algorithms under relaxed schedulers. In 31st ACM Symposium on Parallelism in Algorithms and Architectures (pp. 145–154). Phoenix, AZ, United States: ACM Press. https://doi.org/10.1145/3323165.3323201
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

2019 | Conference Paper | IST-REx-ID: 7542 | OA
Wendler, C., Alistarh, D.-A., & Püschel, M. (2019). Powerset convolutional neural networks (Vol. 32, pp. 927–938). Presented at the NIPS: Conference on Neural Information Processing Systems, Vancouver, Canada: Neural Information Processing Systems Foundation.
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2019 | Conference Paper | IST-REx-ID: 6935 | OA
Foerster, K.-T., Korhonen, J., Rybicki, J., & Schmid, S. (2019). Does preprocessing help under congestion? In Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing (pp. 259–261). Toronto, ON, Canada: ACM. https://doi.org/10.1145/3293611.3331581
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2019 | Conference Paper | IST-REx-ID: 6676 | OA
Alistarh, D.-A., Aspnes, J., Ellen, F., Gelashvili, R., & Zhu, L. (2019). Why extension-based proofs fail. In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing (pp. 986–996). Phoenix, AZ, United States: ACM Press. https://doi.org/10.1145/3313276.3316407
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2019 | Conference Paper | IST-REx-ID: 6933 | OA
Censor-Hillel, K., Dory, M., Korhonen, J., & Leitersdorf, D. (2019). Fast approximate shortest paths in the congested clique. In Proceedings of the 2019 ACM Symposium on Principles of Distributed Computin (pp. 74–83). Toronto, ON, Canada: ACM. https://doi.org/10.1145/3293611.3331633
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2018 | Journal Article | IST-REx-ID: 536 | OA
Alistarh, D.-A., Aspnes, J., King, V., & Saia, J. (2018). Communication-efficient randomized consensus. Distributed Computing. Springer. https://doi.org/10.1007/s00446-017-0315-1
[Published Version] View | Files available | DOI
 

2018 | Conference Paper | IST-REx-ID: 7116 | OA
Grubic, D., Tam, L., Alistarh, D.-A., & Zhang, C. (2018). 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 (pp. 145–156). Vienna, Austria: OpenProceedings. https://doi.org/10.5441/002/EDBT.2018.14
[Published Version] View | Files available | DOI
 

2018 | Journal Article | IST-REx-ID: 6001
Alistarh, D.-A., Leiserson, W., Matveev, A., & Shavit, N. (2018). ThreadScan: Automatic and scalable memory reclamation. ACM Transactions on Parallel Computing. Association for Computing Machinery. https://doi.org/10.1145/3201897
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2018 | Conference Paper | IST-REx-ID: 7812 | OA
Polino, A., Pascanu, R., & Alistarh, D.-A. (2018). Model compression via distillation and quantization. In 6th International Conference on Learning Representations. Vancouver, Canada.
[Published Version] View | Files available | arXiv
 

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
[Preprint] View | Files available | DOI | WoS
 

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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