Kurtic, E., Hoefler, T., &; Alistarh, D.-A. (2024). How to prune your language model: Recovering accuracy on the “Sparsity May Cry” benchmark. In Proceedings of Machine Learning Research (Vol. 234, pp. 542–553). Hongkong, China: ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/15011
arXiv: 2312.13547

Koval, N., Alistarh, D.-A., &; Elizarov, R. (2023). Fast and scalable channels in Kotlin Coroutines. In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 107–118). Montreal, QC, Canada: Association for Computing Machinery.https://doi.org/10.1145/3572848.3577481 
LibreCat: https://research-explorer.ista.ac.at/record/12735
DOI: 10.1145/3572848.3577481
arXiv: 2211.04986

Peste, E.-A., Vladu, A., Kurtic, E., Lampert, C., &; Alistarh, D.-A. (n.d.). CrAM: A Compression-Aware Minimizer. In 11th International Conference on Learning Representations . Kigali, Rwanda .
LibreCat: https://research-explorer.ista.ac.at/record/13053
arXiv: 2207.14200

Koval, N., Khalanskiy, D., &; Alistarh, D.-A. (2023). CQS: A formally-verified framework for fair and abortable synchronization. Proceedings of the ACM on Programming Languages. Association for Computing Machinery .https://doi.org/10.1145/3591230 
LibreCat: https://research-explorer.ista.ac.at/record/13179
DOI: 10.1145/3591230

Alistarh, D.-A., Ellen, F., &; Rybicki, J. (2023). Wait-free approximate agreement on graphs. Theoretical Computer Science. Elsevier.https://doi.org/10.1016/j.tcs.2023.113733 
LibreCat: https://research-explorer.ista.ac.at/record/12566
DOI: 10.1016/j.tcs.2023.113733
WoS: 000934262700001

Aksenov, V., Alistarh, D.-A., Drozdova, A., &; Mohtashami, A. (2023). The splay-list: A distribution-adaptive concurrent skip-list. Distributed Computing. Springer Nature.https://doi.org/10.1007/s00446-022-00441-x 
LibreCat: https://research-explorer.ista.ac.at/record/12330
DOI: 10.1007/s00446-022-00441-x
WoS: 000913424000001
arXiv: 2008.01009

Markov, I., Vladu, A., Guo, Q., &; Alistarh, D.-A. (2023). Quantized distributed training of large models with convergence guarantees. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 24020–24044). Honolulu, Hawaii, HI, United States: ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/14461
arXiv: 2302.02390

Nikdan, M., Pegolotti, T., Iofinova, E. B., Kurtic, E., &; Alistarh, D.-A. (2023). SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 26215–26227). Honolulu, Hawaii, HI, United States: ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/14460
arXiv: 2302.04852

Frantar, E., &; Alistarh, D.-A. (2023). SparseGPT: Massive language models can be accurately pruned in one-shot. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 10323–10337). Honolulu, Hawaii, HI, United States: ML Research Press.
LibreCat: https://research-explorer.ista.ac.at/record/14458
arXiv: 2301.00774

Alistarh, D.-A., Aspnes, J., Ellen, F., Gelashvili, R., &; Zhu, L. (2023). Why extension-based proofs fail. SIAM Journal on Computing. Society for Industrial and Applied Mathematics.https://doi.org/10.1137/20M1375851 
LibreCat: https://research-explorer.ista.ac.at/record/14364
DOI: 10.1137/20M1375851
WoS: 001082972300004
arXiv: 1811.01421

Iofinova, E. B., Peste, E.-A., &; Alistarh, D.-A. (2023). Bias in pruned vision models: In-depth analysis and countermeasures. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 24364–24373). Vancouver, BC, Canada: IEEE.https://doi.org/10.1109/cvpr52729.2023.02334 
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WoS: 001062531308068
arXiv: 2304.12622

Koval, N., Fedorov, A., Sokolova, M., Tsitelov, D., &; Alistarh, D.-A. (2023). Lincheck: A practical framework for testing concurrent data structures on JVM. In 35th International Conference on Computer Aided Verification  (Vol. 13964, pp. 156–169). Paris, France: Springer Nature.https://doi.org/10.1007/978-3-031-37706-8_8 
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Koval, N., Fedorov, A., Sokolova, M., Tsitelov, D., &; Alistarh, D.-A. (2023). Lincheck: A practical framework for testing concurrent data structures on JVM. Zenodo.https://doi.org/10.5281/ZENODO.7877757 
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Fedorov, A., Hashemi, D., Nadiradze, G., &; Alistarh, D.-A. (2023). Provably-efficient and internally-deterministic parallel Union-Find. In Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures (pp. 261–271). Orlando, FL, United States: Association for Computing Machinery.https://doi.org/10.1145/3558481.3591082 
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arXiv: 2304.09331

Alistarh, D.-A., Gelashvili, R., &; Rybicki, J. (2022). Fast graphical population protocols. In Q. Bramas, V. Gramoli, &; A. Milani (Eds.), 25th International Conference on Principles of Distributed Systems (Vol. 217). Strasbourg, France: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.OPODIS.2021.14 
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arXiv: 2102.08808

Markov, I., Ramezanikebrya, H., &; Alistarh, D.-A. (2022). CGX: Adaptive system support for communication-efficient deep learning. In Proceedings of the 23rd ACM/IFIP International Middleware Conference (pp. 241–254). Quebec, QC, Canada: Association for Computing Machinery.https://doi.org/10.1145/3528535.3565248 
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arXiv: 2111.08617

Alistarh, D.-A., Rybicki, J., &; Voitovych, S. (2022). Near-optimal leader election in population protocols on graphs. In Proceedings of the Annual ACM Symposium on Principles of Distributed Computing (pp. 246–256). Salerno, Italy: Association for Computing Machinery.https://doi.org/10.1145/3519270.3538435 
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arXiv: 2205.12597

Brown, T. A., Sigouin, W., &; Alistarh, D.-A. (2022). PathCAS: An efficient middle ground for concurrent search data structures. In Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 385–399). Seoul, Republic of Korea: Association for Computing Machinery.https://doi.org/10.1145/3503221.3508410 
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Postnikova, A., Koval, N., Nadiradze, G., &; Alistarh, D.-A. (2022). Multi-queues can be state-of-the-art priority schedulers. In Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 353–367). Seoul, Republic of Korea: Association for Computing Machinery.https://doi.org/10.1145/3503221.3508432 
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DOI: 10.1145/3503221.3508432
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arXiv: 2109.00657

Postnikova, A., Koval, N., Nadiradze, G., &; Alistarh, D.-A. (2022). Multi-queues can be state-of-the-art priority schedulers. Zenodo.https://doi.org/10.5281/ZENODO.5733408 
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DOI: 10.5281/ZENODO.5733408

Iofinova, E. B., Peste, E.-A., Kurtz, M., &; Alistarh, D.-A. (2022). How well do sparse ImageNet models transfer? In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12256–12266). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers.https://doi.org/10.1109/cvpr52688.2022.01195 
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arXiv: 2111.13445

Hoefler, T., Alistarh, D.-A., Ben-Nun, T., Dryden, N., &; Peste, E.-A. (2021). Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks. Journal of Machine Learning Research. Journal of Machine Learning Research.
LibreCat: https://research-explorer.ista.ac.at/record/10180
arXiv: 2102.00554

Alistarh, D.-A., Gelashvili, R., &; Rybicki, J. (2021). Brief announcement: Fast graphical population protocols. In 35th International Symposium on Distributed Computing (Vol. 209). Freiburg, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.DISC.2021.43 
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arXiv: 2102.08808

Alistarh, D.-A., Gelashvili, R., &; Nadiradze, G. (2021). Lower bounds for shared-memory leader election under bounded write contention. In 35th International Symposium on Distributed Computing (Vol. 209). Freiburg, Germany: Schloss Dagstuhl - Leibniz Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.DISC.2021.4 
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Fedorov, A., Koval, N., &; Alistarh, D.-A. (2021). A scalable concurrent algorithm for dynamic connectivity. In Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures (pp. 208–220). Virtual, Online: Association for Computing Machinery.https://doi.org/10.1145/3409964.3461810 
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arXiv: 2105.08098

Kungurtsev, V., Egan, M., Chatterjee, B., &; Alistarh, D.-A. (2021). Asynchronous optimization methods for efficient training of deep neural networks with guarantees. In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (Vol. 35, pp. 8209–8216). Virtual, Online: AAAI Press.
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arXiv: 1905.11845

Alimisis, F., Davies, P., Vandereycken, B., &; Alistarh, D.-A. (2021). Distributed principal component analysis with limited communication. In Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems (Vol. 4, pp. 2823–2834). Virtual, Online: Neural Information Processing Systems Foundation.
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arXiv: 2110.14391

Frantar, E., Kurtic, E., &; Alistarh, D.-A. (2021). M-FAC: Efficient matrix-free approximations of second-order information. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 14873–14886). Virtual, Online: Curran Associates.
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arXiv: 2010.08222

Alistarh, D.-A., &; Korhonen, J. (2021). Towards tight communication lower bounds for distributed optimisation. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 7254–7266). Virtual, Online: Curran Associates.
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arXiv: 2010.08222

Davies, P., Gurunanthan, V., Moshrefi, N., Ashkboos, S., &; Alistarh, D.-A. (2021). New bounds for distributed mean estimation and variance reduction. In 9th International Conference on Learning Representations. Virtual.
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arXiv: 2002.09268

Alistarh, D.-A., &; Davies, P. (2021). Collecting coupons is faster with friends. In Structural Information and Communication Complexity (Vol. 12810, pp. 3–12). Wrocaw, Poland: Springer Nature.https://doi.org/10.1007/978-3-030-79527-6_1 
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Alistarh, D.-A., Ellen, F., &; Rybicki, J. (2021). Wait-free approximate agreement on graphs. In Structural Information and Communication Complexity (Vol. 12810, pp. 87–105). Wrocaw, Poland: Springer Nature.https://doi.org/10.1007/978-3-030-79527-6_6 
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arXiv: 2103.08949

Peste, E.-A., Iofinova, E. B., Vladu, A., &; Alistarh, D.-A. (2021). AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 8557–8570). Virtual, Online: Curran Associates.
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arXiv: 2106.12379

Alimisis, F., Davies, P., &; Alistarh, D.-A. (2021). Communication-efficient distributed optimization with quantized preconditioners. In Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 196–206). Virtual: ML Research Press.
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arXiv: 2102.07214

Li, S., Tal Ben-Nun, T. B.-N., Nadiradze, G., Girolamo, S. D., Dryden, N., Alistarh, D.-A., &; Hoefler, T. (2021). Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging. IEEE Transactions on Parallel and Distributed Systems. IEEE.https://doi.org/10.1109/TPDS.2020.3040606 
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arXiv: 2005.00124

Alistarh, D.-A., Töpfer, M., &; Uznaski, P. (2021). Comparison dynamics in population protocols. In Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing (pp. 55–65). Virtual, Italy: Association for Computing Machinery.https://doi.org/10.1145/3465084.3467915 
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Nadiradze, G., Markov, I., Chatterjee, B., Kungurtsev, V., &; Alistarh, D.-A. (2021). Elastic consistency: A practical consistency model for distributed stochastic gradient descent. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, pp. 9037–9045). Virtual.
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arXiv: 2001.05918

Nadiradze, G., Sabour, A., Davies, P., Li, S., &; Alistarh, D.-A. (2021). Asynchronous decentralized SGD with quantized and local updates. In 35th Conference on Neural Information Processing Systems. Sydney, Australia: Neural Information Processing Systems Foundation.
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arXiv: 1910.12308

Alistarh, D.-A., Nadiradze, G., &; Sabour, A. (2021). Dynamic averaging load balancing on cycles. Algorithmica. Virtual, Online; Germany: Springer Nature.https://doi.org/10.1007/s00453-021-00905-9 
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arXiv: 2003.09297

Ramezani-Kebrya, A., Faghri, F., Markov, I., Aksenov, V., Alistarh, D.-A., &; Roy, D. M. (2021). NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization. Journal of Machine Learning Research. Journal of Machine Learning Research.
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arXiv: 1908.06077

Alistarh, D.-A., Fedorov, A., &; Koval, N. (2020). In search of the fastest concurrent union-find algorithm. In 23rd International Conference on Principles of Distributed Systems (Vol. 153, p. 15:1-15:16). Neuchatal, Switzerland: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.OPODIS.2019.15 
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arXiv: 1911.06347

Aksenov, V., Alistarh, D.-A., Drozdova, A., &; Mohtashami, A. (2020). The splay-list: A distribution-adaptive concurrent skip-list. In 34th International Symposium on Distributed Computing (Vol. 179, p. 3:1-3:18). Freiburg, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.DISC.2020.3 
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arXiv: 2008.01009

Singh, S. P., &; Alistarh, D.-A. (2020). WoodFisher: Efficient second-order approximation for neural network compression. In Advances in Neural Information Processing Systems (Vol. 33, pp. 18098–18109). Vancouver, Canada: Curran Associates.
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arXiv: 2004.14340

Aksenov, V., Alistarh, D.-A., &; Korhonen, J. (2020). Scalable belief propagation via relaxed scheduling. In Advances in Neural Information Processing Systems (Vol. 33, pp. 22361–22372). Vancouver, Canada: Curran Associates.
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arXiv: 2002.11505

Kurtz, M., Kopinsky, J., Gelashvili, R., Matveev, A., Carr, J., Goin, M.,  Alistarh, D.-A. (2020). Inducing and exploiting activation sparsity for fast neural network inference. In 37th International Conference on Machine Learning, ICML 2020 (Vol. 119, pp. 5533–5543). Online.
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Gurel, N. M., Kara, K., Stojanov, A., Smith, T., Lemmin, T., Alistarh, D.-A.,https://doi.org/10.1109/TSP.2020.3010355__Zhang, C. (2020). Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications. IEEE Transactions on Signal Processing. IEEE.https://doi.org/10.1109/TSP.2020.3010355 
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arXiv: 1802.04907

Li, S., Tal Ben-Nun, T. B.-N., Girolamo, S. D., Alistarh, D.-A., &; Hoefler, T. (2020). 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 (pp. 45–61). San Diego, CA, United States: Association for Computing Machinery.https://doi.org/10.1145/3332466.3374528 
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arXiv: 1908.04207

Konstantinov, N. H., Frantar, E., Alistarh, D.-A., &; Lampert, C. (2020). On the sample complexity of adversarial multi-source PAC learning. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 5416–5425). Online: ML Research Press.
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arXiv: 2002.10384

Brown, T. A., Prokopec, A., &; Alistarh, D.-A. (2020). Non-blocking interpolation search trees with doubly-logarithmic running time. In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 276–291). San Diego, CA, United States: Association for Computing Machinery.https://doi.org/10.1145/3332466.3374542 
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Alistarh, D.-A., Brown, T. A., &; Singhal, N. (2020). Memory tagging: Minimalist synchronization for scalable concurrent data structures. In Annual ACM Symposium on Parallelism in Algorithms and Architectures (pp. 37–49). Virtual Event, United States: Association for Computing Machinery.https://doi.org/10.1145/3350755.3400213 
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Koval, N., Sokolova, M., Fedorov, A., Alistarh, D.-A., &; Tsitelov, D. (2020). Testing concurrency on the JVM with Lincheck. In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP (pp. 423–424). San Diego, CA, United States: Association for Computing Machinery.https://doi.org/10.1145/3332466.3374503 
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Alistarh, D.-A., Aspnes, J., Ellen, F., Gelashvili, R., &; Zhu, L. (2020). Brief Announcement: Why Extension-Based Proofs Fail. In Proceedings of the 39th Symposium on Principles of Distributed Computing (pp. 54–56). Virtual, Italy: Association for Computing Machinery.https://doi.org/10.1145/3382734.3405743 
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Alistarh, D.-A., Nadiradze, G., &; Sabour, A. (2020). Dynamic averaging load balancing on cycles. In 47th International Colloquium on Automata, Languages, and Programming (Vol. 168). Saarbrücken, Germany, Virtual: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.ICALP.2020.7 
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arXiv: 2003.09297

Faghri, F., Tabrizian, I., Markov, I., Alistarh, D.-A., Roy, D., &; Ramezani-Kebrya, A. (2020). Adaptive gradient quantization for data-parallel SGD. In Advances in Neural Information Processing Systems (Vol. 33). Vancouver, Canada: Neural Information Processing Systems Foundation.
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arXiv: 2010.12460

Koval, N., Alistarh, D.-A., &; Elizarov, R. (2019). Lock-free channels for programming via communicating sequential processes. Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming (pp. 417–418). Washington, NY, United States: ACM Press.https://doi.org/10.1145/3293883.3297000 
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Khirirat, S., Johansson, M., &; Alistarh, D.-A. (2019). Gradient compression for communication-limited convex optimization. In 2018 IEEE Conference on Decision and Control. Miami Beach, FL, United States: IEEE.https://doi.org/10.1109/cdc.2018.8619625 
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Renggli, C., Ashkboos, S., Aghagolzadeh, M., Alistarh, D.-A., &; Hoefler, T. (2019). SparCML: High-performance sparse communication for machine learning. In International Conference for High Performance Computing, Networking, Storage and Analysis, SC. Denver, CO, Unites States: ACM.https://doi.org/10.1145/3295500.3356222 
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Koval, N., Alistarh, D.-A., &; Elizarov, R. (2019). Scalable FIFO channels for programming via communicating sequential processes. In 25th Anniversary of Euro-Par (Vol. 11725, pp. 317–333). Göttingen, Germany: Springer Nature.https://doi.org/10.1007/978-3-030-29400-7_23 
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Yu, C., Tang, H., Renggli, C., Kassing, S., Singla, A., Alistarh, D.-A.,  Liu, J. (2019). Distributed learning over unreliable networks. In 36th International Conference on Machine Learning, ICML 2019 (Vol. 2019–June, pp. 12481–12512). Long Beach, CA, United States: IMLS.
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arXiv: 1810.07766

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 
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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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arXiv: 1909.02253

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 
LibreCat: https://research-explorer.ista.ac.at/record/6676
DOI: 10.1145/3313276.3316407
WoS: 000523199100089
arXiv: 1811.01421

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 
LibreCat: https://research-explorer.ista.ac.at/record/536
DOI: 10.1007/s00446-017-0315-1

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 
LibreCat: https://research-explorer.ista.ac.at/record/7116
DOI: 10.5441/002/EDBT.2018.14

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 
LibreCat: https://research-explorer.ista.ac.at/record/6001
DOI: 10.1145/3201897

Polino, A., Pascanu, R., &; Alistarh, D.-A. (2018). Model compression via distillation and quantization. In 6th International Conference on Learning Representations. Vancouver, Canada.
LibreCat: https://research-explorer.ista.ac.at/record/7812
arXiv: 1802.05668

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 
LibreCat: https://research-explorer.ista.ac.at/record/5962
DOI: 10.1145/3212734.3212763
WoS: 000458186900022
arXiv: 1803.08841

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 
LibreCat: https://research-explorer.ista.ac.at/record/5961
DOI: 10.1145/3212734.3212798
WoS: 000458186900063

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 
LibreCat: https://research-explorer.ista.ac.at/record/5963
DOI: 10.1145/3212734.3212756
WoS: 000458186900048
arXiv: 1808.04155

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 
LibreCat: https://research-explorer.ista.ac.at/record/5965
DOI: 10.1145/3210377.3210411
WoS: 000545269600016
arXiv: 1804.01018

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 
LibreCat: https://research-explorer.ista.ac.at/record/5966
DOI: 10.1145/3210377.3210406
WoS: 000545269600046
arXiv: 1804.00947

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 
LibreCat: https://research-explorer.ista.ac.at/record/5964
DOI: 10.1145/3212734.3212785
WoS: 000458186900052

Stojanov, A., Smith, T. M., Alistarh, D.-A., &; Puschel, M. (2018). Fast quantized arithmetic on : 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 
LibreCat: https://research-explorer.ista.ac.at/record/6031
DOI: 10.1109/SiPS.2018.8598402
WoS: 000465106800060

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 
LibreCat: https://research-explorer.ista.ac.at/record/7123
DOI: 10.1137/1.9781611975031.144
WoS: 000483921200145
arXiv: 1704.04947

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.
LibreCat: https://research-explorer.ista.ac.at/record/6558
WoS: 000461823304061
arXiv: 1803.08917

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.
LibreCat: https://research-explorer.ista.ac.at/record/6589
WoS: 000461852000047
arXiv: 1809.10505

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 
LibreCat: https://research-explorer.ista.ac.at/record/487
DOI: 10.1145/3143361.3143367

Alistarh, D.-A., Dudek, B., Kosowski, A., Soloveichik, D., &; Uznaski, P. (2017). Robust detection in leak-prone population protocols (Vol. 10467 LNCS, pp. 155–171). Presented at the DNA Computing and Molecular Programming, Springer.https://doi.org/10.1007/978-3-319-66799-7_11 
LibreCat: https://research-explorer.ista.ac.at/record/788
DOI: 10.1007/978-3-319-66799-7_11
arXiv: 1706.09937

Alistarh, D.-A., Aspnes, J., Eisenstat, D., Rivest, R., &; Gelashvili, R. (2017). Time-space trade-offs in population protocols (pp. 2560–2579). Presented at the SODA: Symposium on Discrete Algorithms, SIAM.https://doi.org/doi.org/10.1137/1.9781611974782.169 
LibreCat: https://research-explorer.ista.ac.at/record/787
DOI: doi.org/10.1137/1.9781611974782.169

Alistarh, D.-A., Leiserson, W., Matveev, A., &; Shavit, N. (2017). Forkscan: Conservative memory reclamation for modern operating systems (pp. 483–498). Presented at the EuroSys: European Conference on Computer Systems, ACM.https://doi.org/10.1145/3064176.3064214 
LibreCat: https://research-explorer.ista.ac.at/record/789
DOI: 10.1145/3064176.3064214

Kara, K., Alistarh, D.-A., Alonso, G., Mutlu, O., &; Zhang, C. (2017). FPGA-accelerated dense linear machine learning: A precision-convergence trade-off (pp. 160–167). Presented at the FCCM: Field-Programmable Custom Computing Machines, IEEE.https://doi.org/10.1109/FCCM.2017.39 
LibreCat: https://research-explorer.ista.ac.at/record/790
DOI: 10.1109/FCCM.2017.39

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 9314, pp. 283–292). Washington, WA, USA: ACM.https://doi.org/10.1145/3087801.3087810 
LibreCat: https://research-explorer.ista.ac.at/record/791
DOI: 10.1145/3087801.3087810
WoS: 000462995000035

Alistarh, D.-A., Grubic, D., Li, J., Tomioka, R., &; Vojnovi, 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.
LibreCat: https://research-explorer.ista.ac.at/record/431
arXiv: 1610.02132

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.
LibreCat: https://research-explorer.ista.ac.at/record/432

Alistarh, D.-A., Censor Hillel, K., &; Shavit, N. (2016). Are lock free concurrent algorithms practically wait free . Journal of the ACM. ACM.https://doi.org/10.1145/2903136 
LibreCat: https://research-explorer.ista.ac.at/record/786
DOI: 10.1145/2903136
arXiv: 1311.3200

Haider, S., Hasenplaugh, W., &; Alistarh, D.-A. (2016). Lease/Release: Architectural support for scaling contended data structures (Vol. 12-16-March-2016). Presented at the PPoPP: Principles and Practice of Parallel Pogramming, ACM.https://doi.org/10.1145/2851141.2851155 
LibreCat: https://research-explorer.ista.ac.at/record/785
DOI: 10.1145/2851141.2851155

Alistarh, D.-A., Kopinsky, J., Li, J., &; Shavit, N. (2015). The SprayList: A scalable relaxed priority queue (Vol. 2015–January, pp. 11–20). Presented at the PPoPP: Principles and Practice of Parallel Pogramming, ACM.https://doi.org/10.1145/2688500.2688523 
LibreCat: https://research-explorer.ista.ac.at/record/776
DOI: 10.1145/2688500.2688523

Alistarh, D.-A., Iglesias, J., &; Vojnovi, M. (2015). Streaming min-max hypergraph partitioning (Vol. 2015–January, pp. 1900–1908). Presented at the NIPS: Neural Information Processing Systems, Neural Information Processing Systems.
LibreCat: https://research-explorer.ista.ac.at/record/777

Alistarh, D.-A., Kopinsky, J., Kuznetsov, P., Ravi, S., &; Shavit, N. (2015). Inherent limitations of hybrid transactional memory (Vol. 9363, pp. 185–199). Presented at the DISC: Distributed Computing, Springer.https://doi.org/10.1007/978-3-662-48653-5_13 
LibreCat: https://research-explorer.ista.ac.at/record/778
DOI: 10.1007/978-3-662-48653-5_13
arXiv: 1405.5689

Alistarh, D.-A., Matveev, A., Leiserson, W., &; Shavit, N. (2015). ThreadScan: Automatic and scalable memory reclamation (Vol. 2015–June, pp. 123–132). Presented at the SPAA: Symposium on Parallelism in Algorithms and Architectures, ACM.https://doi.org/10.1145/2755573.2755600 
LibreCat: https://research-explorer.ista.ac.at/record/779
DOI: 10.1145/2755573.2755600

Alistarh, D.-A., &; Gelashvili, R. (2015). Polylogarithmic-time leader election in population protocols (Vol. 9135, pp. 479–491). Presented at the ICALP: International Colloquium on Automota, Languages and Programming, Springer.https://doi.org/10.1007/978-3-662-47666-6_38 
LibreCat: https://research-explorer.ista.ac.at/record/780
DOI: 10.1007/978-3-662-47666-6_38
arXiv: 1502.05745

Alistarh, D.-A., Gelashvili, R., &; Vojnovi, M. (2015). Fast and exact majority in population protocols (Vol. 2015–July, pp. 47–56). Presented at the PODC: Principles of Distributed Computing, ACM.https://doi.org/10.1145/2767386.2767429 
LibreCat: https://research-explorer.ista.ac.at/record/781
DOI: 10.1145/2767386.2767429

Alistarh, D.-A., Sauerwald, T., &; Vojnovi, M. (2015). Lock-Free algorithms under stochastic schedulers (Vol. 2015–July, pp. 251–260). Presented at the PODC: Principles of Distributed Computing, ACM.https://doi.org/10.1145/2767386.2767430 
LibreCat: https://research-explorer.ista.ac.at/record/782
DOI: 10.1145/2767386.2767430

Alistarh, D.-A., Gelashvili, R., &; Vladu, A. (2015). How to elect a leader faster than a tournament (Vol. 2015–July, pp. 365–374). Presented at the PODC: Principles of Distributed Computing, ACM.https://doi.org/10.1145/2767386.2767420 
LibreCat: https://research-explorer.ista.ac.at/record/783
DOI: 10.1145/2767386.2767420

Alistarh, D.-A., Ballani, H., Costa, P., Funnell, A., Benjamin, J., Watts, P., &; Thomsen, B. (2015). A high-radix, low-latency optical switch for data centers (pp. 367–368). Presented at the SIGCOMM: Special Interest Group on Data Communication, London, United Kindgdom: ACM.https://doi.org/10.1145/2785956.2790035 
LibreCat: https://research-explorer.ista.ac.at/record/784
DOI: 10.1145/2785956.2790035

Alistarh, D.-A., Aspnes, J., Bender, M., Gelashvili, R., &; Gilbert, S. (2014). Dynamic task allocation in asynchronous shared memory (pp. 416–435). Presented at the SODA: Symposium on Discrete Algorithms, SIAM.https://doi.org/10.1137/1.9781611973402.31 
LibreCat: https://research-explorer.ista.ac.at/record/768
DOI: 10.1137/1.9781611973402.31

Alistarh, D.-A., Aspnes, J., Censor Hillel, K., Gilbert, S., &; Guerraoui, R. (2014). Tight bounds for asynchronous renaming. Journal of the ACM. ACM.https://doi.org/10.1145/2597630 
LibreCat: https://research-explorer.ista.ac.at/record/769
DOI: 10.1145/2597630

Alistarh, D.-A., Eugster, P., Herlihy, M., Matveev, A., &; Shavit, N. (2014). StackTrack: An automated transactional approach to concurrent memory reclamation. Presented at the EuroSys: European Conference on Computer Systems, ACM.https://doi.org/10.1145/2592798.2592808 
LibreCat: https://research-explorer.ista.ac.at/record/770
DOI: 10.1145/2592798.2592808

Alistarh, D.-A., Denysyuk, O., Rodrígues, L., &; Shavit, N. (2014). Balls-into-Leaves: Sub-logarithmic renaming in synchronous message-passing systems (pp. 232–241). Presented at the PODC: Principles of Distributed Computing, ACM.https://doi.org/10.1145/2611462.2611499 
LibreCat: https://research-explorer.ista.ac.at/record/771
DOI: 10.1145/2611462.2611499

Alistarh, D.-A., Censor Hillel, K., &; Shavit, N. (2014). Are lock-free concurrent algorithms practically wait-free? (pp. 714–723). Presented at the STOC: Symposium on Theory of Computing, ACM.https://doi.org/10.1145/2591796.2591836 
LibreCat: https://research-explorer.ista.ac.at/record/772
DOI: 10.1145/2591796.2591836
arXiv: 1311.3200