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168 Publications
2021 |
Published |
Conference Paper |
IST-REx-ID: 9933 |
A. Czumaj, P. Davies, and M. Parter, “Component stability in low-space massively parallel computation,” in Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, Virtual, Italy, 2021, pp. 481–491.
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| arXiv
2021 |
Published |
Conference Paper |
IST-REx-ID: 9935 |
A. Czumaj, P. Davies, and M. Parter, “Improved deterministic (Δ+1) coloring in low-space MPC,” in Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, Virtual, Italy, 2021, pp. 469–479.
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| WoS
2021 |
Published |
Conference Paper |
IST-REx-ID: 11458 |
A. Krumes, E. B. Iofinova, A. Vladu, and D.-A. Alistarh, “AC/DC: Alternating Compressed/DeCompressed training of deep neural networks,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 8557–8570.
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| arXiv
2021 |
Published |
Thesis | PhD |
IST-REx-ID: 10429 |
G. Nadiradze, “On achieving scalability through relaxation,” Institute of Science and Technology Austria, 2021.
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2021 |
Published |
Conference Paper |
IST-REx-ID: 10435 |
G. Nadiradze, A. Sabour, P. Davies, S. Li, and D.-A. Alistarh, “Asynchronous decentralized SGD with quantized and local updates,” in 35th Conference on Neural Information Processing Systems, Sydney, Australia, 2021.
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| arXiv
2021 |
Published |
Conference Paper |
IST-REx-ID: 10432 |
G. Nadiradze, I. Markov, B. Chatterjee, V. Kungurtsev, and D.-A. Alistarh, “Elastic consistency: A practical consistency model for distributed stochastic gradient descent,” in Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35, no. 10, pp. 9037–9045.
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| arXiv
2021 |
Published |
Conference Paper |
IST-REx-ID: 10049 |
K. Klein et al., “Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement,” in 2021 IEEE Symposium on Security and Privacy , San Francisco, CA, United States, 2021, pp. 268–284.
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2021 |
Published |
Conference Paper |
IST-REx-ID: 9823 |
D.-A. Alistarh, F. Ellen, and J. Rybicki, “Wait-free approximate agreement on graphs,” in Structural Information and Communication Complexity, Wrocław, Poland, 2021, vol. 12810, pp. 87–105.
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| arXiv
2020 |
Published |
Conference Paper |
IST-REx-ID: 15074 |
S. Brandt, B. Keller, J. Rybicki, J. Suomela, and J. Uitto, “Brief announcement: Efficient load-balancing through distributed token dropping,” in 34th International Symposium on Distributed Computing, Virtual, 2020, vol. 179.
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| arXiv
2020 |
Published |
Conference Paper |
IST-REx-ID: 15077 |
D.-A. Alistarh, G. Nadiradze, and A. Sabour, “Dynamic averaging load balancing on cycles,” in 47th International Colloquium on Automata, Languages, and Programming, Saarbrücken, Germany, Virtual, 2020, vol. 168.
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| arXiv
2020 |
Published |
Conference Paper |
IST-REx-ID: 15086 |
F. Faghri, I. Tabrizian, I. Markov, D.-A. Alistarh, D. Roy, and A. Ramezani-Kebrya, “Adaptive gradient quantization for data-parallel SGD,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33.
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| arXiv
2020 |
Published |
Conference Paper |
IST-REx-ID: 7802 |
A. Czumaj, P. Davies, and M. Parter, “Graph sparsification for derandomizing massively parallel computation with low space,” in Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2020), Virtual Event, United States, 2020, no. 7, pp. 175–185.
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| arXiv
2020 |
Published |
Conference Paper |
IST-REx-ID: 7803 |
A. Czumaj, P. Davies, and M. Parter, “Simple, deterministic, constant-round coloring in the congested clique,” in Proceedings of the 2020 ACM Symposium on Principles of Distributed Computing, Salerno, Italy, 2020, pp. 309–318.
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| arXiv
2020 |
Published |
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.
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2020 |
Published |
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.
[Preprint]
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| arXiv
2020 |
Published |
Conference Paper |
IST-REx-ID: 8383
D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, and L. Zhu, “Brief Announcement: Why Extension-Based Proofs Fail,” in Proceedings of the 39th Symposium on Principles of Distributed Computing, Virtual, Italy, 2020, pp. 54–56.
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2020 |
Published |
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.
[Preprint]
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| arXiv
2020 |
Published |
Conference Paper |
IST-REx-ID: 8725 |
V. Aksenov, D.-A. Alistarh, A. Drozdova, and A. Mohtashami, “The splay-list: A distribution-adaptive concurrent skip-list,” in 34th International Symposium on Distributed Computing, Freiburg, Germany, 2020, vol. 179, p. 3:1-3:18.
[Published Version]
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| arXiv
2020 |
Conference Paper |
IST-REx-ID: 9415 |
M. Kurtz et al., “Inducing and exploiting activation sparsity for fast neural network inference,” in 37th International Conference on Machine Learning, ICML 2020, Online, 2020, vol. 119, pp. 5533–5543.
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