Dan-Adrian Alistarh
Alistarh Group
76 Publications
2020 | Conference Paper | IST-REx-ID: 8191
D.-A. Alistarh, T. A. Brown, and N. Singhal, “Memory tagging: Minimalist synchronization for scalable concurrent data structures,” in Annual ACM Symposium on Parallelism in Algorithms and Architectures, Virtual Event, United States, 2020, no. 7, pp. 37–49.
View
| DOI
2020 | Journal Article | IST-REx-ID: 8268 |

N. M. Gurel et al., “Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications,” IEEE Transactions on Signal Processing, vol. 68. IEEE, pp. 4268–4282, 2020.
View
| DOI
| Download Preprint (ext.)
| arXiv
2020 | Conference Paper | IST-REx-ID: 8286 |

D.-A. Alistarh, G. Nadiradze, and A. Sabour, “Dynamic averaging load balancing on cycles,” in 47th International Colloquium on Automata, Languages, and Programming, Virtual, Online; Germany, 2020, vol. 168.
View
| Files available
| DOI
| arXiv
2020 | Conference Paper | IST-REx-ID: 8722 |

S. Li, T. B.-N. Tal Ben-Nun, S. D. Girolamo, D.-A. Alistarh, and T. Hoefler, “Taming unbalanced training workloads in deep learning with partial collective operations,” in Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, San Diego, CA, United States, 2020, pp. 45–61.
View
| DOI
| Download Preprint (ext.)
| arXiv
2020 | Journal Article | IST-REx-ID: 8723 |

S. Li et al., “Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging,” IEEE Transactions on Parallel and Distributed Systems. IEEE, 2020.
View
| DOI
| Download Preprint (ext.)
| arXiv
2020 | Conference Paper | IST-REx-ID: 8724 |

N. H. Konstantinov, E. Frantar, D.-A. Alistarh, and C. Lampert, “On the sample complexity of adversarial multi-source PAC learning,” in Proceedings of the 37th International Conference on Machine Learning, Online, 2020, vol. 119, pp. 5416–5425.
View
| Files available
| arXiv
2020 | Preprint | IST-REx-ID: 8725 |

V. Aksenov, D.-A. Alistarh, A. Drozdova, and A. Mohtashami, “The splay-list: A distribution-adaptive concurrent skip-list,” arXiv. .
View
| Download Preprint (ext.)
| arXiv
2020 | Conference Paper | IST-REx-ID: 7605 |

D.-A. Alistarh, A. Fedorov, and N. Koval, “In search of the fastest concurrent union-find algorithm,” in 23rd International Conference on Principles of Distributed Systems, Neuchatal, Switzerland, 2020, vol. 153, p. 15:1-15:16.
View
| Files available
| DOI
| arXiv
2020 | Conference Paper | IST-REx-ID: 7635
N. Koval, M. Sokolova, A. Fedorov, D.-A. Alistarh, and D. Tsitelov, “Testing concurrency on the JVM with Lincheck,” in Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, San Diego, CA, United States, 2020, pp. 423–424.
View
| DOI
2020 | Conference Paper | IST-REx-ID: 7636
T. A. Brown, A. Prokopec, and D.-A. Alistarh, “Non-blocking interpolation search trees with doubly-logarithmic running time,” in Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, San Diego, CA, United States, 2020, pp. 276–291.
View
| DOI
2019 | Conference Paper | IST-REx-ID: 7201 |

C. Renggli, S. Ashkboos, M. Aghagolzadeh, D.-A. Alistarh, and T. Hoefler, “SparCML: High-performance sparse communication for machine learning,” in International Conference for High Performance Computing, Networking, Storage and Analysis, SC, Denver, CO, Unites States, 2019.
View
| DOI
| Download Preprint (ext.)
| arXiv
2019 | Conference Paper | IST-REx-ID: 7228
N. Koval, D.-A. Alistarh, and R. Elizarov, “Scalable FIFO channels for programming via communicating sequential processes,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Göttingen, Germany, 2019, vol. 11725, pp. 317–333.
View
| DOI
2019 | Conference Paper | IST-REx-ID: 7437 |

C. Yu et al., “Distributed learning over unreliable networks,” in 36th International Conference on Machine Learning, ICML 2019, Long Beach, CA, United States, 2019, vol. 2019–June, pp. 12481–12512.
View
| Download Preprint (ext.)
| arXiv
2019 | Conference Paper | IST-REx-ID: 7542 |

C. Wendler, D.-A. Alistarh, and M. Püschel, “Powerset convolutional neural networks,” presented at the NIPS: Conference on Neural Information Processing Systems, Vancouver, Canada, 2019, vol. 32, pp. 927–938.
View
| Download Published Version (ext.)
| arXiv
2019 | Conference Paper | IST-REx-ID: 6673 |

D.-A. Alistarh, G. Nadiradze, and N. Koval, “Efficiency guarantees for parallel incremental algorithms under relaxed schedulers,” in 31st ACM Symposium on Parallelism in Algorithms and Architectures, Phoenix, AZ, United States, 2019, pp. 145–154.
View
| DOI
| Download Preprint (ext.)
| arXiv
2019 | Conference Paper | IST-REx-ID: 6676 |

D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, and L. Zhu, “Why extension-based proofs fail,” in Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, Phoenix, AZ, United States, 2019, pp. 986–996.
View
| DOI
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 7812 |

A. Polino, R. Pascanu, and D.-A. Alistarh, “Model compression via distillation and quantization,” in 6th International Conference on Learning Representations, Vancouver, Canada, 2018.
View
| Files available
| arXiv
2018 | Journal Article | IST-REx-ID: 536 |

D.-A. Alistarh, J. Aspnes, V. King, and J. Saia, “Communication-efficient randomized consensus,” Distributed Computing, vol. 31, no. 6. Springer, pp. 489–501, 2018.
View
| Files available
| DOI
2018 | Conference Paper | IST-REx-ID: 5962 |

D.-A. Alistarh, C. De Sa, and N. H. Konstantinov, “The convergence of stochastic gradient descent in asynchronous shared memory,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC ’18, Egham, United Kingdom, 2018, pp. 169–178.
View
| DOI
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 5963 |

D.-A. Alistarh, T. A. Brown, J. Kopinsky, and G. Nadiradze, “Relaxed schedulers can efficiently parallelize iterative algorithms,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC ’18, Egham, United Kingdom, 2018, pp. 377–386.
View
| DOI
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 5964 |

V. Aksenov, D.-A. Alistarh, and P. Kuznetsov, “Brief Announcement: Performance prediction for coarse-grained locking,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC ’18, Egham, United Kingdom, 2018, pp. 411–413.
View
| DOI
| Download Submitted Version (ext.)
2018 | Conference Paper | IST-REx-ID: 5965 |

D.-A. Alistarh, T. A. Brown, J. Kopinsky, J. Z. Li, and G. Nadiradze, “Distributionally linearizable data structures,” in Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA ’18, Vienna, Austria, 2018, pp. 133–142.
View
| DOI
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 5966 |

D.-A. Alistarh, S. K. Haider, R. Kübler, and G. Nadiradze, “The transactional conflict problem,” in Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA ’18, Vienna, Austria, 2018, pp. 383–392.
View
| DOI
| Download Preprint (ext.)
| arXiv
2018 | Journal Article | IST-REx-ID: 6001
D.-A. Alistarh, W. Leiserson, A. Matveev, and N. Shavit, “ThreadScan: Automatic and scalable memory reclamation,” ACM Transactions on Parallel Computing, vol. 4, no. 4. Association for Computing Machinery, 2018.
View
| Files available
| DOI
2018 | Conference Paper | IST-REx-ID: 6558 |

D.-A. Alistarh, Z. Allen-Zhu, and J. Li, “Byzantine Stochastic Gradient Descent,” in Advances in Neural Information Processing Systems, Montreal, Canada, 2018, vol. Volume 2018, pp. 4613–4623.
View
| Download Published Version (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 6589 |

D.-A. Alistarh, T. Hoefler, M. Johansson, N. H. Konstantinov, S. Khirirat, and C. Renggli, “The convergence of sparsified gradient methods,” in Advances in Neural Information Processing Systems 31, Montreal, Canada, 2018, vol. Volume 2018, pp. 5973–5983.
View
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 7116 |

D. Grubic, L. Tam, D.-A. Alistarh, and C. Zhang, “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, Vienna, Austria, 2018, pp. 145–156.
View
| Files available
| DOI
2018 | Conference Paper | IST-REx-ID: 7123 |

D.-A. Alistarh, J. Aspnes, and R. Gelashvili, “Space-optimal majority in population protocols,” in Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, United States, 2018, pp. 2221–2239.
View
| DOI
| Download Preprint (ext.)
| arXiv
2017 | Conference Paper | IST-REx-ID: 787 |

D.-A. Alistarh, J. Aspnes, D. Eisenstat, R. Rivest, and R. Gelashvili, “Time-space trade-offs in population protocols,” presented at the SODA: Symposium on Discrete Algorithms, 2017, pp. 2560–2579.
View
| DOI
| Download None (ext.)
2017 | Conference Paper | IST-REx-ID: 788 |

D.-A. Alistarh, B. Dudek, A. Kosowski, D. Soloveichik, and P. Uznański, “Robust detection in leak-prone population protocols,” presented at the DNA Computing and Molecular Programming, 2017, vol. 10467 LNCS, pp. 155–171.
View
| DOI
| Download None (ext.)
2017 | Conference Paper | IST-REx-ID: 791 |

D.-A. Alistarh, J. Kopinsky, J. Li, and G. Nadiradze, “The power of choice in priority scheduling,” in Proceedings of the ACM Symposium on Principles of Distributed Computing, Washington, WA, USA, 2017, vol. Part F129314, pp. 283–292.
View
| DOI
| Download Submitted Version (ext.)
2017 | Conference Paper | IST-REx-ID: 487
G. Baig, B. Radunovic, D.-A. Alistarh, M. Balkwill, T. Karagiannis, and L. Qiu, “Towards unlicensed cellular networks in TV white spaces,” in Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies, Incheon, South Korea, 2017, pp. 2–14.
View
| DOI
2017 | Conference Paper | IST-REx-ID: 431 |

D.-A. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnović, “QSGD: Communication-efficient SGD via gradient quantization and encoding,” presented at the NIPS: Neural Information Processing System, Long Beach, CA, United States, 2017, vol. 2017, pp. 1710–1721.
View
| Download Submitted Version (ext.)
| arXiv
2017 | Conference Paper | IST-REx-ID: 432 |

H. Zhang, J. Li, K. Kara, D.-A. Alistarh, J. Liu, and C. Zhang, “ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning,” in Proceedings of Machine Learning Research, Sydney, Australia, 2017, vol. 70, pp. 4035–4043.
View
| Files available
2016 | Journal Article | IST-REx-ID: 786 |

D.-A. Alistarh, K. Censor Hillel, and N. Shavit, “Are lock free concurrent algorithms practically wait free ,” Journal of the ACM, vol. 63, no. 4. ACM, 2016.
View
| DOI
| Download Preprint (ext.)
| arXiv
2015 | Conference Paper | IST-REx-ID: 777
D.-A. Alistarh, J. Iglesias, and M. Vojnović, “Streaming min-max hypergraph partitioning,” presented at the NIPS: Neural Information Processing Systems, 2015, vol. 2015–January, pp. 1900–1908.
View
| Download None (ext.)
2015 | Conference Paper | IST-REx-ID: 778 |

D.-A. Alistarh, J. Kopinsky, P. Kuznetsov, S. Ravi, and N. Shavit, “Inherent limitations of hybrid transactional memory,” presented at the DISC: Distributed Computing, 2015, vol. 9363, pp. 185–199.
View
| DOI
| Download None (ext.)
| arXiv
2015 | Conference Paper | IST-REx-ID: 779
D.-A. Alistarh, A. Matveev, W. Leiserson, and N. Shavit, “ThreadScan: Automatic and scalable memory reclamation,” presented at the SPAA: Symposium on Parallelism in Algorithms and Architectures, 2015, vol. 2015–June, pp. 123–132.
View
| Files available
| DOI
2015 | Conference Paper | IST-REx-ID: 780 |

D.-A. Alistarh and R. Gelashvili, “Polylogarithmic-time leader election in population protocols,” presented at the ICALP: International Colloquium on Automota, Languages and Programming, 2015, vol. 9135, pp. 479–491.
View
| DOI
| Download Preprint (ext.)
| arXiv
2015 | Conference Paper | IST-REx-ID: 783 |

D.-A. Alistarh, R. Gelashvili, and A. Vladu, “How to elect a leader faster than a tournament,” presented at the PODC: Principles of Distributed Computing, 2015, vol. 2015–July, pp. 365–374.
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| DOI
| Download None (ext.)
2014 | Conference Paper | IST-REx-ID: 772 |

D.-A. Alistarh, K. Censor Hillel, and N. Shavit, “Are lock-free concurrent algorithms practically wait-free?,” presented at the STOC: Symposium on Theory of Computing, 2014, pp. 714–723.
View
| DOI
| Download Preprint (ext.)
| arXiv
2014 | Conference Paper | IST-REx-ID: 775 |

D.-A. Alistarh, J. Kopinsky, A. Matveev, and N. Shavit, “The levelarray: A fast, practical long-lived renaming algorithm,” presented at the ICDCS: International Conference on Distributed Computing Systems, 2014, pp. 348–357.
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| DOI
| Download Preprint (ext.)
| arXiv
2010 | Conference Paper | IST-REx-ID: 755
D.-A. Alistarh, S. Gilbert, R. Guerraoui, and M. Zadimoghaddam, “How efficient can gossip be? (On the cost of resilient information exchange),” presented at the ICALP: International Colloquium on Automota, Languages and Programming, 2010, vol. 6199 LNCS, no. PART 2, pp. 115–126.
View
| DOI
76 Publications
2020 | Conference Paper | IST-REx-ID: 8191
D.-A. Alistarh, T. A. Brown, and N. Singhal, “Memory tagging: Minimalist synchronization for scalable concurrent data structures,” in Annual ACM Symposium on Parallelism in Algorithms and Architectures, Virtual Event, United States, 2020, no. 7, pp. 37–49.
View
| DOI
2020 | Journal Article | IST-REx-ID: 8268 |

N. M. Gurel et al., “Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications,” IEEE Transactions on Signal Processing, vol. 68. IEEE, pp. 4268–4282, 2020.
View
| DOI
| Download Preprint (ext.)
| arXiv
2020 | Conference Paper | IST-REx-ID: 8286 |

D.-A. Alistarh, G. Nadiradze, and A. Sabour, “Dynamic averaging load balancing on cycles,” in 47th International Colloquium on Automata, Languages, and Programming, Virtual, Online; Germany, 2020, vol. 168.
View
| Files available
| DOI
| arXiv
2020 | Conference Paper | IST-REx-ID: 8722 |

S. Li, T. B.-N. Tal Ben-Nun, S. D. Girolamo, D.-A. Alistarh, and T. Hoefler, “Taming unbalanced training workloads in deep learning with partial collective operations,” in Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, San Diego, CA, United States, 2020, pp. 45–61.
View
| DOI
| Download Preprint (ext.)
| arXiv
2020 | Journal Article | IST-REx-ID: 8723 |

S. Li et al., “Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging,” IEEE Transactions on Parallel and Distributed Systems. IEEE, 2020.
View
| DOI
| Download Preprint (ext.)
| arXiv
2020 | Conference Paper | IST-REx-ID: 8724 |

N. H. Konstantinov, E. Frantar, D.-A. Alistarh, and C. Lampert, “On the sample complexity of adversarial multi-source PAC learning,” in Proceedings of the 37th International Conference on Machine Learning, Online, 2020, vol. 119, pp. 5416–5425.
View
| Files available
| arXiv
2020 | Preprint | IST-REx-ID: 8725 |

V. Aksenov, D.-A. Alistarh, A. Drozdova, and A. Mohtashami, “The splay-list: A distribution-adaptive concurrent skip-list,” arXiv. .
View
| Download Preprint (ext.)
| arXiv
2020 | Conference Paper | IST-REx-ID: 7605 |

D.-A. Alistarh, A. Fedorov, and N. Koval, “In search of the fastest concurrent union-find algorithm,” in 23rd International Conference on Principles of Distributed Systems, Neuchatal, Switzerland, 2020, vol. 153, p. 15:1-15:16.
View
| Files available
| DOI
| arXiv
2020 | Conference Paper | IST-REx-ID: 7635
N. Koval, M. Sokolova, A. Fedorov, D.-A. Alistarh, and D. Tsitelov, “Testing concurrency on the JVM with Lincheck,” in Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, San Diego, CA, United States, 2020, pp. 423–424.
View
| DOI
2020 | Conference Paper | IST-REx-ID: 7636
T. A. Brown, A. Prokopec, and D.-A. Alistarh, “Non-blocking interpolation search trees with doubly-logarithmic running time,” in Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, San Diego, CA, United States, 2020, pp. 276–291.
View
| DOI
2019 | Conference Paper | IST-REx-ID: 7201 |

C. Renggli, S. Ashkboos, M. Aghagolzadeh, D.-A. Alistarh, and T. Hoefler, “SparCML: High-performance sparse communication for machine learning,” in International Conference for High Performance Computing, Networking, Storage and Analysis, SC, Denver, CO, Unites States, 2019.
View
| DOI
| Download Preprint (ext.)
| arXiv
2019 | Conference Paper | IST-REx-ID: 7228
N. Koval, D.-A. Alistarh, and R. Elizarov, “Scalable FIFO channels for programming via communicating sequential processes,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Göttingen, Germany, 2019, vol. 11725, pp. 317–333.
View
| DOI
2019 | Conference Paper | IST-REx-ID: 7437 |

C. Yu et al., “Distributed learning over unreliable networks,” in 36th International Conference on Machine Learning, ICML 2019, Long Beach, CA, United States, 2019, vol. 2019–June, pp. 12481–12512.
View
| Download Preprint (ext.)
| arXiv
2019 | Conference Paper | IST-REx-ID: 7542 |

C. Wendler, D.-A. Alistarh, and M. Püschel, “Powerset convolutional neural networks,” presented at the NIPS: Conference on Neural Information Processing Systems, Vancouver, Canada, 2019, vol. 32, pp. 927–938.
View
| Download Published Version (ext.)
| arXiv
2019 | Conference Paper | IST-REx-ID: 6673 |

D.-A. Alistarh, G. Nadiradze, and N. Koval, “Efficiency guarantees for parallel incremental algorithms under relaxed schedulers,” in 31st ACM Symposium on Parallelism in Algorithms and Architectures, Phoenix, AZ, United States, 2019, pp. 145–154.
View
| DOI
| Download Preprint (ext.)
| arXiv
2019 | Conference Paper | IST-REx-ID: 6676 |

D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, and L. Zhu, “Why extension-based proofs fail,” in Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, Phoenix, AZ, United States, 2019, pp. 986–996.
View
| DOI
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 7812 |

A. Polino, R. Pascanu, and D.-A. Alistarh, “Model compression via distillation and quantization,” in 6th International Conference on Learning Representations, Vancouver, Canada, 2018.
View
| Files available
| arXiv
2018 | Journal Article | IST-REx-ID: 536 |

D.-A. Alistarh, J. Aspnes, V. King, and J. Saia, “Communication-efficient randomized consensus,” Distributed Computing, vol. 31, no. 6. Springer, pp. 489–501, 2018.
View
| Files available
| DOI
2018 | Conference Paper | IST-REx-ID: 5962 |

D.-A. Alistarh, C. De Sa, and N. H. Konstantinov, “The convergence of stochastic gradient descent in asynchronous shared memory,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC ’18, Egham, United Kingdom, 2018, pp. 169–178.
View
| DOI
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 5963 |

D.-A. Alistarh, T. A. Brown, J. Kopinsky, and G. Nadiradze, “Relaxed schedulers can efficiently parallelize iterative algorithms,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC ’18, Egham, United Kingdom, 2018, pp. 377–386.
View
| DOI
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 5964 |

V. Aksenov, D.-A. Alistarh, and P. Kuznetsov, “Brief Announcement: Performance prediction for coarse-grained locking,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC ’18, Egham, United Kingdom, 2018, pp. 411–413.
View
| DOI
| Download Submitted Version (ext.)
2018 | Conference Paper | IST-REx-ID: 5965 |

D.-A. Alistarh, T. A. Brown, J. Kopinsky, J. Z. Li, and G. Nadiradze, “Distributionally linearizable data structures,” in Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA ’18, Vienna, Austria, 2018, pp. 133–142.
View
| DOI
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 5966 |

D.-A. Alistarh, S. K. Haider, R. Kübler, and G. Nadiradze, “The transactional conflict problem,” in Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA ’18, Vienna, Austria, 2018, pp. 383–392.
View
| DOI
| Download Preprint (ext.)
| arXiv
2018 | Journal Article | IST-REx-ID: 6001
D.-A. Alistarh, W. Leiserson, A. Matveev, and N. Shavit, “ThreadScan: Automatic and scalable memory reclamation,” ACM Transactions on Parallel Computing, vol. 4, no. 4. Association for Computing Machinery, 2018.
View
| Files available
| DOI
2018 | Conference Paper | IST-REx-ID: 6558 |

D.-A. Alistarh, Z. Allen-Zhu, and J. Li, “Byzantine Stochastic Gradient Descent,” in Advances in Neural Information Processing Systems, Montreal, Canada, 2018, vol. Volume 2018, pp. 4613–4623.
View
| Download Published Version (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 6589 |

D.-A. Alistarh, T. Hoefler, M. Johansson, N. H. Konstantinov, S. Khirirat, and C. Renggli, “The convergence of sparsified gradient methods,” in Advances in Neural Information Processing Systems 31, Montreal, Canada, 2018, vol. Volume 2018, pp. 5973–5983.
View
| Download Preprint (ext.)
| arXiv
2018 | Conference Paper | IST-REx-ID: 7116 |

D. Grubic, L. Tam, D.-A. Alistarh, and C. Zhang, “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, Vienna, Austria, 2018, pp. 145–156.
View
| Files available
| DOI
2018 | Conference Paper | IST-REx-ID: 7123 |

D.-A. Alistarh, J. Aspnes, and R. Gelashvili, “Space-optimal majority in population protocols,” in Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, United States, 2018, pp. 2221–2239.
View
| DOI
| Download Preprint (ext.)
| arXiv
2017 | Conference Paper | IST-REx-ID: 787 |

D.-A. Alistarh, J. Aspnes, D. Eisenstat, R. Rivest, and R. Gelashvili, “Time-space trade-offs in population protocols,” presented at the SODA: Symposium on Discrete Algorithms, 2017, pp. 2560–2579.
View
| DOI
| Download None (ext.)
2017 | Conference Paper | IST-REx-ID: 788 |

D.-A. Alistarh, B. Dudek, A. Kosowski, D. Soloveichik, and P. Uznański, “Robust detection in leak-prone population protocols,” presented at the DNA Computing and Molecular Programming, 2017, vol. 10467 LNCS, pp. 155–171.
View
| DOI
| Download None (ext.)
2017 | Conference Paper | IST-REx-ID: 791 |

D.-A. Alistarh, J. Kopinsky, J. Li, and G. Nadiradze, “The power of choice in priority scheduling,” in Proceedings of the ACM Symposium on Principles of Distributed Computing, Washington, WA, USA, 2017, vol. Part F129314, pp. 283–292.
View
| DOI
| Download Submitted Version (ext.)
2017 | Conference Paper | IST-REx-ID: 487
G. Baig, B. Radunovic, D.-A. Alistarh, M. Balkwill, T. Karagiannis, and L. Qiu, “Towards unlicensed cellular networks in TV white spaces,” in Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies, Incheon, South Korea, 2017, pp. 2–14.
View
| DOI
2017 | Conference Paper | IST-REx-ID: 431 |

D.-A. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnović, “QSGD: Communication-efficient SGD via gradient quantization and encoding,” presented at the NIPS: Neural Information Processing System, Long Beach, CA, United States, 2017, vol. 2017, pp. 1710–1721.
View
| Download Submitted Version (ext.)
| arXiv
2017 | Conference Paper | IST-REx-ID: 432 |

H. Zhang, J. Li, K. Kara, D.-A. Alistarh, J. Liu, and C. Zhang, “ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning,” in Proceedings of Machine Learning Research, Sydney, Australia, 2017, vol. 70, pp. 4035–4043.
View
| Files available
2016 | Journal Article | IST-REx-ID: 786 |

D.-A. Alistarh, K. Censor Hillel, and N. Shavit, “Are lock free concurrent algorithms practically wait free ,” Journal of the ACM, vol. 63, no. 4. ACM, 2016.
View
| DOI
| Download Preprint (ext.)
| arXiv
2015 | Conference Paper | IST-REx-ID: 777
D.-A. Alistarh, J. Iglesias, and M. Vojnović, “Streaming min-max hypergraph partitioning,” presented at the NIPS: Neural Information Processing Systems, 2015, vol. 2015–January, pp. 1900–1908.
View
| Download None (ext.)
2015 | Conference Paper | IST-REx-ID: 778 |

D.-A. Alistarh, J. Kopinsky, P. Kuznetsov, S. Ravi, and N. Shavit, “Inherent limitations of hybrid transactional memory,” presented at the DISC: Distributed Computing, 2015, vol. 9363, pp. 185–199.
View
| DOI
| Download None (ext.)
| arXiv
2015 | Conference Paper | IST-REx-ID: 779
D.-A. Alistarh, A. Matveev, W. Leiserson, and N. Shavit, “ThreadScan: Automatic and scalable memory reclamation,” presented at the SPAA: Symposium on Parallelism in Algorithms and Architectures, 2015, vol. 2015–June, pp. 123–132.
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2015 | Conference Paper | IST-REx-ID: 780 |

D.-A. Alistarh and R. Gelashvili, “Polylogarithmic-time leader election in population protocols,” presented at the ICALP: International Colloquium on Automota, Languages and Programming, 2015, vol. 9135, pp. 479–491.
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| arXiv
2015 | Conference Paper | IST-REx-ID: 783 |

D.-A. Alistarh, R. Gelashvili, and A. Vladu, “How to elect a leader faster than a tournament,” presented at the PODC: Principles of Distributed Computing, 2015, vol. 2015–July, pp. 365–374.
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2014 | Conference Paper | IST-REx-ID: 772 |

D.-A. Alistarh, K. Censor Hillel, and N. Shavit, “Are lock-free concurrent algorithms practically wait-free?,” presented at the STOC: Symposium on Theory of Computing, 2014, pp. 714–723.
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| arXiv
2014 | Conference Paper | IST-REx-ID: 775 |

D.-A. Alistarh, J. Kopinsky, A. Matveev, and N. Shavit, “The levelarray: A fast, practical long-lived renaming algorithm,” presented at the ICDCS: International Conference on Distributed Computing Systems, 2014, pp. 348–357.
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| arXiv
2010 | Conference Paper | IST-REx-ID: 755
D.-A. Alistarh, S. Gilbert, R. Guerraoui, and M. Zadimoghaddam, “How efficient can gossip be? (On the cost of resilient information exchange),” presented at the ICALP: International Colloquium on Automota, Languages and Programming, 2010, vol. 6199 LNCS, no. PART 2, pp. 115–126.
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