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122 Publications


2019 | Conference Paper | IST-REx-ID: 7122
S. Khirirat, M. Johansson, and D.-A. Alistarh, “Gradient compression for communication-limited convex optimization,” in 2018 IEEE Conference on Decision and Control, Miami Beach, FL, United States, 2019.
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2019 | Conference Paper | IST-REx-ID: 7201 | OA
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.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 

2019 | Journal Article | IST-REx-ID: 7214 | OA
S. Aganezov, I. Zban, V. Aksenov, N. Alexeev, and M. C. Schatz, “Recovering rearranged cancer chromosomes from karyotype graphs,” BMC Bioinformatics, vol. 20. BMC, 2019.
[Published Version] View | Files available | DOI | WoS
 

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 25th Anniversary of Euro-Par, Göttingen, Germany, 2019, vol. 11725, pp. 317–333.
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2019 | Conference Paper | IST-REx-ID: 7437 | OA
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.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 

2019 | Conference Paper | IST-REx-ID: 6673 | OA
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.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

2019 | Conference Paper | IST-REx-ID: 7542 | OA
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.
[Published Version] View | Download Published Version (ext.) | WoS | arXiv
 

2019 | Conference Paper | IST-REx-ID: 6935 | OA
K.-T. Foerster, J. Korhonen, J. Rybicki, and S. Schmid, “Does preprocessing help under congestion?,” in Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing, Toronto, ON, Canada, 2019, pp. 259–261.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 

2019 | Conference Paper | IST-REx-ID: 6676 | OA
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.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

2019 | Conference Paper | IST-REx-ID: 6933 | OA
K. Censor-Hillel, M. Dory, J. Korhonen, and D. Leitersdorf, “Fast approximate shortest paths in the congested clique,” in Proceedings of the 2019 ACM Symposium on Principles of Distributed Computin, Toronto, ON, Canada, 2019, pp. 74–83.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 

2018 | Journal Article | IST-REx-ID: 536 | OA
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.
[Published Version] View | Files available | DOI
 

2018 | Conference Paper | IST-REx-ID: 7116 | OA
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.
[Published Version] View | Files available | DOI
 

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.
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2018 | Conference Paper | IST-REx-ID: 7812 | OA
A. Polino, R. Pascanu, and D.-A. Alistarh, “Model compression via distillation and quantization,” in 6th International Conference on Learning Representations, Vancouver, Canada, 2018.
[Published Version] View | Files available | arXiv
 

2018 | Conference Paper | IST-REx-ID: 397
M. Arbel Raviv and T. A. Brown, “Harnessing epoch-based reclamation for efficient range queries,” presented at the PPoPP: Principles and Practice of Parallel Programming, Vienna, Austria, 2018, vol. 53, no. 1, pp. 14–27.
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2018 | Journal Article | IST-REx-ID: 43 | OA
J. Rybicki, E. Kisdi, and J. Anttila, “Model of bacterial toxin-dependent pathogenesis explains infective dose,” PNAS, vol. 115, no. 42. National Academy of Sciences, pp. 10690–10695, 2018.
[Submitted Version] View | Files available | DOI | WoS
 

2018 | Journal Article | IST-REx-ID: 76 | OA
C. Lenzen and J. Rybicki, “Near-optimal self-stabilising counting and firing squads,” Distributed Computing. Springer, 2018.
[Published Version] View | Files available | DOI | WoS
 

2018 | Conference Paper | IST-REx-ID: 85 | OA
E. Gilad, T. A. Brown, M. Oskin, and Y. Etsion, “Snapshot based synchronization: A fast replacement for Hand-over-Hand locking,” presented at the Euro-Par: European Conference on Parallel Processing, Turin, Italy, 2018, vol. 11014, pp. 465–479.
[Preprint] View | Files available | DOI | WoS
 

2018 | Conference Paper | IST-REx-ID: 5962 | OA
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.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 

2018 | Conference Paper | IST-REx-ID: 5961
D.-A. Alistarh, “A brief tutorial on distributed and concurrent machine learning,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 487–488.
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