Kurtic, Eldar, Torsten Hoefler, and Dan-Adrian Alistarh. “How to Prune Your Language Model: Recovering Accuracy on the ‘Sparsity May Cry’ Benchmark.” In Proceedings of Machine Learning Research, 234:542–53. ML Research Press, 2024.

Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. “Fast and Scalable Channels in Kotlin Coroutines.”https://doi.org/10.1145/3572848.3577481__In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 107–18. Association for Computing Machinery, 2023.https://doi.org/10.1145/3572848.3577481 .

Peste, Elena-Alexandra, Adrian Vladu, Eldar Kurtic, Christoph Lampert, and Dan-Adrian Alistarh. “CrAM: A Compression-Aware Minimizer.” In 11th International Conference on Learning Representations , n.d.

Koval, Nikita, Dmitry Khalanskiy, and Dan-Adrian Alistarh. “CQS: A Formally-Verified Framework for Fair and Abortable Synchronization.”https://doi.org/10.1145/3591230__Proceedings of the ACM on Programming Languages. Association for Computing Machinery , 2023.https://doi.org/10.1145/3591230 .

Alistarh, Dan-Adrian, Faith Ellen, and Joel Rybicki. “Wait-Free Approximate Agreement on Graphs.”https://doi.org/10.1016/j.tcs.2023.113733__Theoretical Computer Science. Elsevier, 2023.https://doi.org/10.1016/j.tcs.2023.113733 .

Aksenov, Vitalii, Dan-Adrian Alistarh, Alexandra Drozdova, and Amirkeivan Mohtashami. “The Splay-List: A Distribution-Adaptive Concurrent Skip-List.”https://doi.org/10.1007/s00446-022-00441-x__Distributed Computing. Springer Nature, 2023.https://doi.org/10.1007/s00446-022-00441-x .

Markov, Ilia, Adrian Vladu, Qi Guo, and Dan-Adrian Alistarh. “Quantized Distributed Training of Large Models with Convergence Guarantees.” In Proceedings of the 40th International Conference on Machine Learning, 202:24020–44. ML Research Press, 2023.

Nikdan, Mahdi, Tommaso Pegolotti, Eugenia B Iofinova, Eldar Kurtic, and Dan-Adrian Alistarh. “SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.” In Proceedings of the 40th International Conference on Machine Learning, 202:26215–27. ML Research Press, 2023.

Frantar, Elias, and Dan-Adrian Alistarh. “SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.” In Proceedings of the 40th International Conference on Machine Learning, 202:10323–37. ML Research Press, 2023.

Alistarh, Dan-Adrian, James Aspnes, Faith Ellen, Rati Gelashvili, and Leqi Zhu. “Why Extension-Based Proofs Fail.”https://doi.org/10.1137/20M1375851__SIAM Journal on Computing. Society for Industrial and Applied Mathematics, 2023.https://doi.org/10.1137/20M1375851 .

Iofinova, Eugenia B, Elena-Alexandra Peste, and Dan-Adrian Alistarh. “Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures.”https://doi.org/10.1109/cvpr52729.2023.02334__In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 24364–73. IEEE, 2023.https://doi.org/10.1109/cvpr52729.2023.02334 .

Koval, Nikita, Alexander Fedorov, Maria Sokolova, Dmitry Tsitelov, and Dan-Adrian Alistarh. “Lincheck: A Practical Framework for Testing Concurrent Data Structures on JVM.”https://doi.org/10.1007/978-3-031-37706-8_8__In 35th International Conference on Computer Aided Verification , 13964:156–69. Springer Nature, 2023.https://doi.org/10.1007/978-3-031-37706-8_8 .

Koval, Nikita, Alexander Fedorov, Maria Sokolova, Dmitry Tsitelov, and Dan-Adrian Alistarh. “Lincheck: A Practical Framework for Testing Concurrent Data Structures on JVM.”https://doi.org/10.5281/ZENODO.7877757__Zenodo, 2023.https://doi.org/10.5281/ZENODO.7877757 .

Fedorov, Alexander, Diba Hashemi, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Provably-Efficient and Internally-Deterministic Parallel Union-Find.”https://doi.org/10.1145/3558481.3591082__In Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures, 261–71. Association for Computing Machinery, 2023.https://doi.org/10.1145/3558481.3591082 .

Alistarh, Dan-Adrian, Rati Gelashvili, and Joel Rybicki. “Fast Graphical Population Protocols.”https://doi.org/10.4230/LIPIcs.OPODIS.2021.14__In 25th International Conference on Principles of Distributed Systems, edited by Quentin Bramas, Vincent Gramoli, and Alessia Milani, Vol. 217. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022.https://doi.org/10.4230/LIPIcs.OPODIS.2021.14 .

Markov, Ilia, Hamidreza Ramezanikebrya, and Dan-Adrian Alistarh. “CGX: Adaptive System Support for Communication-Efficient Deep Learning.”https://doi.org/10.1145/3528535.3565248__In Proceedings of the 23rd ACM/IFIP International Middleware Conference, 241–54. Association for Computing Machinery, 2022.https://doi.org/10.1145/3528535.3565248 .

Alistarh, Dan-Adrian, Joel Rybicki, and Sasha Voitovych. “Near-Optimal Leader Election in Population Protocols on Graphs.”https://doi.org/10.1145/3519270.3538435__In Proceedings of the Annual ACM Symposium on Principles of Distributed Computing, 246–56. Association for Computing Machinery, 2022.https://doi.org/10.1145/3519270.3538435 .

Brown, Trevor A, William Sigouin, and Dan-Adrian Alistarh. “PathCAS: An Efficient Middle Ground for Concurrent Search Data Structures.”https://doi.org/10.1145/3503221.3508410__In Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 385–99. Association for Computing Machinery, 2022.https://doi.org/10.1145/3503221.3508410 .

Postnikova, Anastasiia, Nikita Koval, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Multi-Queues Can Be State-of-the-Art Priority Schedulers.”https://doi.org/10.1145/3503221.3508432__In Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 353–67. Association for Computing Machinery, 2022.https://doi.org/10.1145/3503221.3508432 .

Postnikova, Anastasiia, Nikita Koval, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Multi-Queues Can Be State-of-the-Art Priority Schedulers.”https://doi.org/10.5281/ZENODO.5733408__Zenodo, 2022.https://doi.org/10.5281/ZENODO.5733408 .

Iofinova, Eugenia B, Elena-Alexandra Peste, Mark Kurtz, and Dan-Adrian Alistarh. “How Well Do Sparse ImageNet Models Transfer?”https://doi.org/10.1109/cvpr52688.2022.01195__In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 12256–66. Institute of Electrical and Electronics Engineers, 2022.https://doi.org/10.1109/cvpr52688.2022.01195 .

Hoefler, Torsten, Dan-Adrian Alistarh, Tal Ben-Nun, Nikoli Dryden, and Elena-Alexandra Peste. “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, 2021.

Alistarh, Dan-Adrian, Rati Gelashvili, and Joel Rybicki. “Brief Announcement: Fast Graphical Population Protocols.”https://doi.org/10.4230/LIPIcs.DISC.2021.43__In 35th International Symposium on Distributed Computing, Vol. 209. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021.https://doi.org/10.4230/LIPIcs.DISC.2021.43 .

Alistarh, Dan-Adrian, Rati Gelashvili, and Giorgi Nadiradze. “Lower Bounds for Shared-Memory Leader Election under Bounded Write Contention.”https://doi.org/10.4230/LIPIcs.DISC.2021.4__In 35th International Symposium on Distributed Computing, Vol. 209. Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021.https://doi.org/10.4230/LIPIcs.DISC.2021.4 .

Fedorov, Alexander, Nikita Koval, and Dan-Adrian Alistarh. “A Scalable Concurrent Algorithm for Dynamic Connectivity.”https://doi.org/10.1145/3409964.3461810__In Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures, 208–20. Association for Computing Machinery, 2021.https://doi.org/10.1145/3409964.3461810 .

Kungurtsev, Vyacheslav, Malcolm Egan, Bapi Chatterjee, and Dan-Adrian Alistarh. “Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees.” In 35th AAAI Conference on Artificial Intelligence, AAAI 2021, 35:8209–16. AAAI Press, 2021.

Alimisis, Foivos, Peter Davies, Bart Vandereycken, and Dan-Adrian Alistarh. “Distributed Principal Component Analysis with Limited Communication.” In Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, 4:2823–34. Neural Information Processing Systems Foundation, 2021.

Frantar, Elias, Eldar Kurtic, and Dan-Adrian Alistarh. “M-FAC: Efficient Matrix-Free Approximations of Second-Order Information.” In 35th Conference on Neural Information Processing Systems, 34:14873–86. Curran Associates, 2021.

Alistarh, Dan-Adrian, and Janne Korhonen. “Towards Tight Communication Lower Bounds for Distributed Optimisation.” In 35th Conference on Neural Information Processing Systems, 34:7254–66. Curran Associates, 2021.

Davies, Peter, Vijaykrishna Gurunanthan, Niusha  Moshrefi, Saleh Ashkboos, and Dan-Adrian Alistarh. “New Bounds for Distributed Mean Estimation and Variance Reduction.” In 9th International Conference on Learning Representations, 2021.

Alistarh, Dan-Adrian, and Peter Davies. “Collecting Coupons Is Faster with Friends.”https://doi.org/10.1007/978-3-030-79527-6_1__In Structural Information and Communication Complexity, 12810:3–12. Springer Nature, 2021.https://doi.org/10.1007/978-3-030-79527-6_1 .

Alistarh, Dan-Adrian, Faith Ellen, and Joel Rybicki. “Wait-Free Approximate Agreement on Graphs.”https://doi.org/10.1007/978-3-030-79527-6_6__In Structural Information and Communication Complexity, 12810:87–105. Springer Nature, 2021.https://doi.org/10.1007/978-3-030-79527-6_6 .

Peste, Elena-Alexandra, Eugenia B Iofinova, Adrian Vladu, and Dan-Adrian Alistarh. “AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks.” In 35th Conference on Neural Information Processing Systems, 34:8557–70. Curran Associates, 2021.

Alimisis, Foivos, Peter Davies, and Dan-Adrian Alistarh. “Communication-Efficient Distributed Optimization with Quantized Preconditioners.” In Proceedings of the 38th International Conference on Machine Learning, 139:196–206. ML Research Press, 2021.

Li, Shigang, Tal Ben-Nun Tal Ben-Nun, Giorgi Nadiradze, Salvatore Di Girolamo, Nikoli Dryden, Dan-Adrian Alistarh, and Torsten Hoefler. “Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging.”https://doi.org/10.1109/TPDS.2020.3040606__IEEE Transactions on Parallel and Distributed Systems. IEEE, 2021.https://doi.org/10.1109/TPDS.2020.3040606 .

Alistarh, Dan-Adrian, Martin Töpfer, and Przemysaw Uznaski. “Comparison Dynamics in Population Protocols.”https://doi.org/10.1145/3465084.3467915__In Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, 55–65. Association for Computing Machinery, 2021.https://doi.org/10.1145/3465084.3467915 .

Nadiradze, Giorgi, Ilia Markov, Bapi Chatterjee, Vyacheslav  Kungurtsev, and Dan-Adrian Alistarh. “Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent.” In Proceedings of the AAAI Conference on Artificial Intelligence, 35:9037–45, 2021.

Nadiradze, Giorgi, Amirmojtaba Sabour, Peter Davies, Shigang Li, and Dan-Adrian Alistarh. “Asynchronous Decentralized SGD with Quantized and Local Updates.” In 35th Conference on Neural Information Processing Systems. Neural Information Processing Systems Foundation, 2021.

Alistarh, Dan-Adrian, Giorgi Nadiradze, and Amirmojtaba Sabour. “Dynamic Averaging Load Balancing on Cycles.”https://doi.org/10.1007/s00453-021-00905-9__Algorithmica. Springer Nature, 2021.https://doi.org/10.1007/s00453-021-00905-9 .

Ramezani-Kebrya, Ali, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan-Adrian Alistarh, and Daniel M. Roy. “NUQSGD: Provably Communication-Efficient Data-Parallel SGD via Nonuniform Quantization.” Journal of Machine Learning Research. Journal of Machine Learning Research, 2021.

Alistarh, Dan-Adrian, Alexander Fedorov, and Nikita Koval. “In Search of the Fastest Concurrent Union-Find Algorithm.”https://doi.org/10.4230/LIPIcs.OPODIS.2019.15__In 23rd International Conference on Principles of Distributed Systems, 153:15:1-15:16. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.https://doi.org/10.4230/LIPIcs.OPODIS.2019.15 .

Aksenov, Vitaly, Dan-Adrian Alistarh, Alexandra Drozdova, and Amirkeivan Mohtashami. “The Splay-List: A Distribution-Adaptive Concurrent Skip-List.”https://doi.org/10.4230/LIPIcs.DISC.2020.3__In 34th International Symposium on Distributed Computing, 179:3:1-3:18. LIPIcs. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.https://doi.org/10.4230/LIPIcs.DISC.2020.3 .

Singh, Sidak Pal, and Dan-Adrian Alistarh. “WoodFisher: Efficient Second-Order Approximation for Neural Network Compression.” In Advances in Neural Information Processing Systems, 33:18098–109. Curran Associates, 2020.

Aksenov, Vitaly, Dan-Adrian Alistarh, and Janne Korhonen. “Scalable Belief Propagation via Relaxed Scheduling.” In Advances in Neural Information Processing Systems, 33:22361–72. Curran Associates, 2020.

Kurtz, Mark, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William Leiserson, et al. “Inducing and Exploiting Activation Sparsity for Fast Neural Network Inference.” In 37th International Conference on Machine Learning, ICML 2020, 119:5533–43, 2020.

Gurel, Nezihe Merve, Kaan Kara, Alen Stojanov, Tyler Smith, Thomas Lemmin, Dan-Adrian Alistarh, Markus Puschel, and Ce Zhang. “Compressive Sensing Using Iterative Hard Thresholding with Low Precision Data Representation: Theory and Applications.”https://doi.org/10.1109/TSP.2020.3010355__IEEE Transactions on Signal Processing. IEEE, 2020.https://doi.org/10.1109/TSP.2020.3010355 .

Li, Shigang, Tal Ben-Nun Tal Ben-Nun, Salvatore Di Girolamo, Dan-Adrian Alistarh, and Torsten Hoefler. “Taming Unbalanced Training Workloads in Deep Learning with Partial Collective Operations.”https://doi.org/10.1145/3332466.3374528__In Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 45–61. Association for Computing Machinery, 2020.https://doi.org/10.1145/3332466.3374528 .

Konstantinov, Nikola H, Elias Frantar, Dan-Adrian Alistarh, and Christoph Lampert. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” In Proceedings of the 37th International Conference on Machine Learning, 119:5416–25. ML Research Press, 2020.

Brown, Trevor A, Aleksandar Prokopec, and Dan-Adrian Alistarh. “Non-Blocking Interpolation Search Trees with Doubly-Logarithmic Running Time.”https://doi.org/10.1145/3332466.3374542__In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 276–91. Association for Computing Machinery, 2020.https://doi.org/10.1145/3332466.3374542 .

Alistarh, Dan-Adrian, Trevor A Brown, and Nandini Singhal. “Memory Tagging: Minimalist Synchronization for Scalable Concurrent Data Structures.”https://doi.org/10.1145/3350755.3400213__In Annual ACM Symposium on Parallelism in Algorithms and Architectures, 37–49. Association for Computing Machinery, 2020.https://doi.org/10.1145/3350755.3400213 .

Koval, Nikita, Mariia Sokolova, Alexander Fedorov, Dan-Adrian Alistarh, and Dmitry Tsitelov. “Testing Concurrency on the JVM with Lincheck.”https://doi.org/10.1145/3332466.3374503__In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, 423–24. Association for Computing Machinery, 2020.https://doi.org/10.1145/3332466.3374503 .

Alistarh, Dan-Adrian, James Aspnes, Faith Ellen, Rati Gelashvili, and Leqi Zhu. “Brief Announcement: Why Extension-Based Proofs Fail.”https://doi.org/10.1145/3382734.3405743__In Proceedings of the 39th Symposium on Principles of Distributed Computing, 54–56. Association for Computing Machinery, 2020.https://doi.org/10.1145/3382734.3405743 .

Alistarh, Dan-Adrian, Giorgi Nadiradze, and Amirmojtaba Sabour. “Dynamic Averaging Load Balancing on Cycles.”https://doi.org/10.4230/LIPIcs.ICALP.2020.7__In 47th International Colloquium on Automata, Languages, and Programming, Vol. 168. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.https://doi.org/10.4230/LIPIcs.ICALP.2020.7 .

Faghri, Fartash , Iman  Tabrizian, Ilia Markov, Dan-Adrian Alistarh, Daniel  Roy, and Ali  Ramezani-Kebrya. “Adaptive Gradient Quantization for Data-Parallel SGD.” In Advances in Neural Information Processing Systems, Vol. 33. Neural Information Processing Systems Foundation, 2020.

Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. Lock-Free Channels for Programming via Communicating Sequential Processes. Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming. ACM Press, 2019.https://doi.org/10.1145/3293883.3297000 .

Khirirat, Sarit, Mikael Johansson, and Dan-Adrian Alistarh. “Gradient Compression for Communication-Limited Convex Optimization.”https://doi.org/10.1109/cdc.2018.8619625__In 2018 IEEE Conference on Decision and Control. IEEE, 2019.https://doi.org/10.1109/cdc.2018.8619625 .

Renggli, Cedric, Saleh Ashkboos, Mehdi Aghagolzadeh, Dan-Adrian Alistarh, and Torsten Hoefler. “SparCML: High-Performance Sparse Communication for Machine Learning.”https://doi.org/10.1145/3295500.3356222__In International Conference for High Performance Computing, Networking, Storage and Analysis, SC. ACM, 2019.https://doi.org/10.1145/3295500.3356222 .

Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. “Scalable FIFO Channels for Programming via Communicating Sequential Processes.”https://doi.org/10.1007/978-3-030-29400-7_23__In 25th Anniversary of Euro-Par, 11725:317–33. Springer Nature, 2019.https://doi.org/10.1007/978-3-030-29400-7_23 .

Yu, Chen, Hanlin Tang, Cedric Renggli, Simon Kassing, Ankit Singla, Dan-Adrian Alistarh, Ce Zhang, and Ji Liu. “Distributed Learning over Unreliable Networks.” In 36th International Conference on Machine Learning, ICML 2019, 2019–June:12481–512. IMLS, 2019.

Alistarh, Dan-Adrian, Giorgi Nadiradze, and Nikita Koval. “Efficiency Guarantees for Parallel Incremental Algorithms under Relaxed Schedulers.”https://doi.org/10.1145/3323165.3323201__In 31st ACM Symposium on Parallelism in Algorithms and Architectures, 145–54. ACM Press, 2019.https://doi.org/10.1145/3323165.3323201 .

Wendler, Chris, Dan-Adrian Alistarh, and Markus Püschel. “Powerset Convolutional Neural Networks,” 32:927–38. Neural Information Processing Systems Foundation, 2019.

Alistarh, Dan-Adrian, James Aspnes, Faith Ellen, Rati Gelashvili, and Leqi Zhu. “Why Extension-Based Proofs Fail.”https://doi.org/10.1145/3313276.3316407__In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, 986–96. ACM Press, 2019.https://doi.org/10.1145/3313276.3316407 .

Alistarh, Dan-Adrian, James Aspnes, Valerie King, and Jared Saia. “Communication-Efficient Randomized Consensus.”https://doi.org/10.1007/s00446-017-0315-1__Distributed Computing. Springer, 2018.https://doi.org/10.1007/s00446-017-0315-1 .

Grubic, Demjan, Leo Tam, Dan-Adrian Alistarh, and Ce Zhang. “Synchronous Multi-GPU Training for Deep Learning with Low-Precision Communications: An Empirical Study.”https://doi.org/10.5441/002/EDBT.2018.14__In Proceedings of the 21st International Conference on Extending Database Technology, 145–56. OpenProceedings, 2018.https://doi.org/10.5441/002/EDBT.2018.14 .

Alistarh, Dan-Adrian, William Leiserson, Alexander Matveev, and Nir Shavit. “ThreadScan: Automatic and Scalable Memory Reclamation.”https://doi.org/10.1145/3201897__ACM Transactions on Parallel Computing. Association for Computing Machinery, 2018.https://doi.org/10.1145/3201897 .

Polino, Antonio, Razvan Pascanu, and Dan-Adrian Alistarh. “Model Compression via Distillation and Quantization.” In 6th International Conference on Learning Representations, 2018.

Alistarh, Dan-Adrian, Christopher De Sa, and Nikola H Konstantinov. “The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory.”https://doi.org/10.1145/3212734.3212763__In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 169–78. ACM Press, 2018.https://doi.org/10.1145/3212734.3212763 .

Alistarh, Dan-Adrian. “A Brief Tutorial on Distributed and Concurrent Machine Learning.”https://doi.org/10.1145/3212734.3212798__In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 487–88. ACM Press, 2018.https://doi.org/10.1145/3212734.3212798 .

Alistarh, Dan-Adrian, Trevor A Brown, Justin Kopinsky, and Giorgi Nadiradze. “Relaxed Schedulers Can Efficiently Parallelize Iterative Algorithms.”https://doi.org/10.1145/3212734.3212756__In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 377–86. ACM Press, 2018.https://doi.org/10.1145/3212734.3212756 .

Alistarh, Dan-Adrian, Trevor A Brown, Justin Kopinsky, Jerry Z. Li, and Giorgi Nadiradze. “Distributionally Linearizable Data Structures.”https://doi.org/10.1145/3210377.3210411__In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, 133–42. ACM Press, 2018.https://doi.org/10.1145/3210377.3210411 .

Alistarh, Dan-Adrian, Syed Kamran Haider, Raphael Kübler, and Giorgi Nadiradze. “The Transactional Conflict Problem.”https://doi.org/10.1145/3210377.3210406__In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, 383–92. ACM Press, 2018.https://doi.org/10.1145/3210377.3210406 .

Aksenov, Vitaly, Dan-Adrian Alistarh, and Petr Kuznetsov. “Brief Announcement: Performance Prediction for Coarse-Grained Locking.”https://doi.org/10.1145/3212734.3212785__In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 411–13. ACM Press, 2018.https://doi.org/10.1145/3212734.3212785 .

Stojanov, Alen, Tyler Michael Smith, Dan-Adrian Alistarh, and Markus Puschel. “Fast Quantized Arithmetic on : Trading Compute for Data Movement.”https://doi.org/10.1109/SiPS.2018.8598402__In 2018 IEEE International Workshop on Signal Processing Systems, Vol. 2018–October. IEEE, 2018.https://doi.org/10.1109/SiPS.2018.8598402 .

Alistarh, Dan-Adrian, James Aspnes, and Rati Gelashvili. “Space-Optimal Majority in Population Protocols.”https://doi.org/10.1137/1.9781611975031.144__In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, 2221–39. ACM, 2018.https://doi.org/10.1137/1.9781611975031.144 .

Alistarh, Dan-Adrian, Zeyuan Allen-Zhu, and Jerry Li. “Byzantine Stochastic Gradient Descent.” In Advances in Neural Information Processing Systems, 2018:4613–23. Neural Information Processing Systems Foundation, 2018.

Alistarh, Dan-Adrian, Torsten Hoefler, Mikael Johansson, Nikola H Konstantinov, Sarit Khirirat, and Cedric Renggli. “The Convergence of Sparsified Gradient Methods.” In Advances in Neural Information Processing Systems 31, Volume 2018:5973–83. Neural Information Processing Systems Foundation, 2018.

Baig, Ghufran, Bozidar Radunovic, Dan-Adrian Alistarh, Matthew Balkwill, Thomas Karagiannis, and Lili Qiu. “Towards Unlicensed Cellular Networks in TV White Spaces.”https://doi.org/10.1145/3143361.3143367__In Proceedings of the 2017 13th International Conference on Emerging Networking EXperiments and Technologies, 2–14. ACM, 2017.https://doi.org/10.1145/3143361.3143367 .

Alistarh, Dan-Adrian, Bartomiej Dudek, Adrian Kosowski, David Soloveichik, and Przemysaw Uznaski. “Robust Detection in Leak-Prone Population Protocols,”https://doi.org/10.1007/978-3-319-66799-7_11__10467 LNCS:155–71. Springer, 2017.https://doi.org/10.1007/978-3-319-66799-7_11 .

Alistarh, Dan-Adrian, James Aspnes, David Eisenstat, Ronald Rivest, and Rati Gelashvili. “Time-Space Trade-Offs in Population Protocols,”https://doi.org/doi.org/10.1137/1.9781611974782.169__2560–79. SIAM, 2017.https://doi.org/doi.org/10.1137/1.9781611974782.169 .

Alistarh, Dan-Adrian, William Leiserson, Alexander Matveev, and Nir Shavit. “Forkscan: Conservative Memory Reclamation for Modern Operating Systems,”https://doi.org/10.1145/3064176.3064214__483–98. ACM, 2017.https://doi.org/10.1145/3064176.3064214 .

Kara, Kaan, Dan-Adrian Alistarh, Gustavo Alonso, Onur Mutlu, and Ce Zhang. “FPGA-Accelerated Dense Linear Machine Learning: A Precision-Convergence Trade-Off,”https://doi.org/10.1109/FCCM.2017.39__160–67. IEEE, 2017.https://doi.org/10.1109/FCCM.2017.39 .

Alistarh, Dan-Adrian, Justin Kopinsky, Jerry Li, and Giorgi Nadiradze. “The Power of Choice in Priority Scheduling.”https://doi.org/10.1145/3087801.3087810__In Proceedings of the ACM Symposium on Principles of Distributed Computing, Part 9314:283–92. ACM, 2017.https://doi.org/10.1145/3087801.3087810 .

Alistarh, Dan-Adrian, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovi. “QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding,” 2017:1710–21. Neural Information Processing Systems Foundation, 2017.

Zhang, Hantian, Jerry Li, Kaan Kara, Dan-Adrian Alistarh, Ji Liu, and Ce Zhang. “ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning.” In Proceedings of Machine Learning Research, 70:4035–43. ML Research Press, 2017.

Alistarh, Dan-Adrian, Keren Censor Hillel, and Nir Shavit. “Are Lock Free Concurrent Algorithms Practically Wait Free .”https://doi.org/10.1145/2903136__Journal of the ACM. ACM, 2016.https://doi.org/10.1145/2903136 .

Haider, Syed, William Hasenplaugh, and Dan-Adrian Alistarh. “Lease/Release: Architectural Support for Scaling Contended Data Structures,”https://doi.org/10.1145/2851141.2851155__Vol. 12-16-March-2016. ACM, 2016.https://doi.org/10.1145/2851141.2851155 .

Alistarh, Dan-Adrian, Justin Kopinsky, Jerry Li, and Nir Shavit. “The SprayList: A Scalable Relaxed Priority Queue,”https://doi.org/10.1145/2688500.2688523__2015–January:11–20. ACM, 2015.https://doi.org/10.1145/2688500.2688523 .

Alistarh, Dan-Adrian, Jennifer Iglesias, and Milan Vojnovi. “Streaming Min-Max Hypergraph Partitioning,” 2015–January:1900–1908. Neural Information Processing Systems, 2015.

Alistarh, Dan-Adrian, Justin Kopinsky, Petr Kuznetsov, Srivatsan Ravi, and Nir Shavit. “Inherent Limitations of Hybrid Transactional Memory,”https://doi.org/10.1007/978-3-662-48653-5_13__9363:185–99. Springer, 2015.https://doi.org/10.1007/978-3-662-48653-5_13 .

Alistarh, Dan-Adrian, Alexander Matveev, William Leiserson, and Nir Shavit. “ThreadScan: Automatic and Scalable Memory Reclamation,”https://doi.org/10.1145/2755573.2755600__2015–June:123–32. ACM, 2015.https://doi.org/10.1145/2755573.2755600 .

Alistarh, Dan-Adrian, and Rati Gelashvili. “Polylogarithmic-Time Leader Election in Population Protocols,”https://doi.org/10.1007/978-3-662-47666-6_38__9135:479–91. Springer, 2015.https://doi.org/10.1007/978-3-662-47666-6_38 .

Alistarh, Dan-Adrian, Rati Gelashvili, and Milan Vojnovi. “Fast and Exact Majority in Population Protocols,”https://doi.org/10.1145/2767386.2767429__2015–July:47–56. ACM, 2015.https://doi.org/10.1145/2767386.2767429 .

Alistarh, Dan-Adrian, Thomas Sauerwald, and Milan Vojnovi. “Lock-Free Algorithms under Stochastic Schedulers,”https://doi.org/10.1145/2767386.2767430__2015–July:251–60. ACM, 2015.https://doi.org/10.1145/2767386.2767430 .

Alistarh, Dan-Adrian, Rati Gelashvili, and Adrian Vladu. “How to Elect a Leader Faster than a Tournament,”https://doi.org/10.1145/2767386.2767420__2015–July:365–74. ACM, 2015.https://doi.org/10.1145/2767386.2767420 .

Alistarh, Dan-Adrian, Hitesh Ballani, Paolo Costa, Adam Funnell, Joshua Benjamin, Philip Watts, and Benn Thomsen. “A High-Radix, Low-Latency Optical Switch for Data Centers,”https://doi.org/10.1145/2785956.2790035__367–68. ACM, 2015.https://doi.org/10.1145/2785956.2790035 .

Alistarh, Dan-Adrian, James Aspnes, Michael Bender, Rati Gelashvili, and Seth Gilbert. “Dynamic Task Allocation in Asynchronous Shared Memory,”https://doi.org/10.1137/1.9781611973402.31__416–35. SIAM, 2014.https://doi.org/10.1137/1.9781611973402.31 .

Alistarh, Dan-Adrian, James Aspnes, Keren Censor Hillel, Seth Gilbert, and Rachid Guerraoui. “Tight Bounds for Asynchronous Renaming.”https://doi.org/10.1145/2597630__Journal of the ACM. ACM, 2014.https://doi.org/10.1145/2597630 .

Alistarh, Dan-Adrian, Patrick Eugster, Maurice Herlihy, Alexander Matveev, and Nir Shavit. “StackTrack: An Automated Transactional Approach to Concurrent Memory Reclamation.”https://doi.org/10.1145/2592798.2592808__ACM, 2014.https://doi.org/10.1145/2592798.2592808 .

Alistarh, Dan-Adrian, Oksana Denysyuk, Luís Rodrígues, and Nir Shavit. “Balls-into-Leaves: Sub-Logarithmic Renaming in Synchronous Message-Passing Systems,”https://doi.org/10.1145/2611462.2611499__232–41. ACM, 2014.https://doi.org/10.1145/2611462.2611499 .

Alistarh, Dan-Adrian, Keren Censor Hillel, and Nir Shavit. “Are Lock-Free Concurrent Algorithms Practically Wait-Free?,”https://doi.org/10.1145/2591796.2591836__714–23. ACM, 2014.https://doi.org/10.1145/2591796.2591836 .