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.

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 

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 .

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 

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 

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 

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.

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.

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.

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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.

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 

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 

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 

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.

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.

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.

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.

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.

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 

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 

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.

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.

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 

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 

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.

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.

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 

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.

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 

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 

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.

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.

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.

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 

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 

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.

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 

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 

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 

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 

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 

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 

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 

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 

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 

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.

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 

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.

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 

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 

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 

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 

Polino, A., Pascanu, R., &; Alistarh, D.-A. (2018). Model compression via distillation and quantization. In 6th International Conference on Learning Representations. Vancouver, Canada.

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 

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 

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 

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 

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 

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 

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 

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 

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.

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.

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 

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 

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 

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 

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 

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 

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.

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.

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 

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 

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 

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.

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

Alistarh, D.-A., Aspnes, J., King, V., &; Saia, J. (2014). Communication-efficient randomized consensus. In F. Kuhn (Ed.) (Vol. 8784, pp. 61–75). Presented at the DISC: Distributed Computing, Austin, USA: Springer.https://doi.org/10.1007/978-3-662-45174-8_5 

Alistarh, D.-A., Censor Hille, K., &; Shavit, N. (2014). Brief announcement: Are lock-free concurrent algorithms practically wait-free? (pp. 50–52). Presented at the PODC: Principles of Distributed Computing, ACM.https://doi.org/10.1145/2611462.2611502 

Alistarh, D.-A., Kopinsky, J., Matveev, A., &; Shavit, N. (2014). The levelarray: A fast, practical long-lived renaming algorithm (pp. 348–357). Presented at the ICDCS: International Conference on Distributed Computing Systems, IEEE.https://doi.org/10.1109/ICDCS.2014.43 

Alistarh, D.-A., Aspnes, J., Giakkoupis, G., &; Woelfel, P. (2013). Randomized loose renaming in O(loglogn) time (pp. 200–209). Presented at the PODC: Principles of Distributed Computing, ACM.https://doi.org/10.1145/2484239.2484240 

Alistarh, D.-A., Guerraoui, R., Kuznetsov, P., &; Losa, G. (2012). On the cost of composing shared-memory algorithms (pp. 298–307). Presented at the SPAA: Symposium on Parallelism in Algorithms and Architectures, ACM.https://doi.org/10.1145/2312005.2312057 

Alistarh, D.-A., Attiya, H., Guerraoui, R., &; Travers, C. (2012). Early deciding synchronous renaming in O(log f) rounds or less (Vol. 7355 LNCS, pp. 195–206). Presented at the SIROCCO: Structural Information and Communication Complexity, Springer.https://doi.org/10.1007/978-3-642-31104-8_17 

Alistarh, D.-A., Gilbert, S., Guerraoui, R., &; Travers, C. (2012). Of choices, failures and asynchrony: the many faces of set agreement. Algorithmica (New York). Springer.https://doi.org/10.1007/s00453-011-9581-7 

Alistarh, D.-A., Bender, M., Gilbert, S., &; Guerraoui, R. (2012). How to allocate tasks asynchronously (pp. 331–340). Presented at the FOCS: Foundations of Computer Science, IEEE.https://doi.org/10.1109/FOCS.2012.41 

Alistarh, D.-A., Gilbert, S., Guerraoui, R., &; Travers, C. (2012). Generating Fast Indulgent Algorithms. Theory of Computing Systems. Elsevier.https://doi.org/10.1007/s00224-012-9407-2 

Alistarh, D.-A., Gilbert, S., Guerraoui, R., &; Travers, C. (2011). Generating fast indulgent algorithms (Vol. 6522 LNCS, pp. 41–52). Presented at the ICDCN: International Conference on Distributed Computing and Networking, Springer.https://doi.org/10.1007/978-3-642-17679-1_4 

Alistarh, D.-A., Aspnes, J., Gilbert, S., &; Guerraoui, R. (2011). The complexity of renaming (pp. 718–727). Presented at the FOCS: Foundations of Computer Science, IEEE.https://doi.org/10.1109/FOCS.2011.66 

Alistarh, D.-A., Aspnes, J., Censor Hillel, K., Gilbert, S., &; Zadimoghaddam, M. (2011). Optimal-time adaptive strong renaming, with applications to counting (pp. 239–248). Presented at the PODC: Principles of Distributed Computing, ACM.https://doi.org/10.1145/1993806.1993850 

Alistarh, D.-A., &; Aspnes, J. (2011). Sub-logarithmic test-and-set against a weak adversary (Vol. 6950 LNCS, pp. 97–109). Presented at the DISC: Distributed Computing, Springer.https://doi.org/10.1007/978-3-642-24100-0_7 

Alistarh, D.-A., Attiya, H., Gilbert, S., Giurgiu, A., &; Guerraoui, R. (2010). Fast randomized test-and-set and renaming (Vol. 6343 LNCS, pp. 94–108). Presented at the DISC: Distributed Computing, Springer.https://doi.org/10.1007/978-3-642-15763-9_9 

Alistarh, D.-A., Gilbert, S., Guerraoui, R., &; Zadimoghaddam, M. (2010). How efficient can gossip be? (On the cost of resilient information exchange) (Vol. 6199 LNCS, pp. 115–126). Presented at the ICALP: International Colloquium on Automota, Languages and Programming, Springer.https://doi.org/10.1007/978-3-642-14162-1_10 

Alistarh, D.-A., Gilbert, S., Guerraoui, R., Milosevi, d., &; Newport, C. (2010). Securing every bit: Authenticated broadcast in radio networks (pp. 50–59). Presented at the SPAA: Symposium on Parallelism in Algorithms and Architectures, ACM.https://doi.org/10.1145/1810479.1810489 

Alistarh, D.-A., Gilbert, S., Guerraoui, R., &; Travers, C. (2010). Brief announcement: New bounds for partially synchronous set agreement (Vol. 6343 LNCS, pp. 404–405). Presented at the DISC: Distributed Computing, Springer.https://doi.org/10.1007/978-3-642-15763-9_40 

Alistarh, D.-A., Gilbert, S., Guerraoui, R., &; Travers, C. (2009). Of choices, failures and asynchrony: the many faces of set agreement (Vol. 5878 LNCS, pp. 943–953). Presented at the ISAAC: International Symposium on Algorithms and Computation, Springer.https://doi.org/10.1007/978-3-642-10631-6_95 

Alistarh, D.-A., Gilbert, S., Guerraoui, R., &; Travers, C. (2008). How to solve consensus in the smallest window of synchrony (Vol. 5218 LNCS, pp. 32–46). Presented at the DISC: Distributed Computing, Springer.https://doi.org/10.1007/978-3-540-87779-0_3