Dan Alistarh
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QSGD: Communication-efficient SGD via gradient quantization and encoding
D Alistarh, D Grubic, J Li, R Tomioka, M Vojnovic
Advances in neural information processing systems 30, 2017
Model compression via distillation and quantization
A Polino, R Pascanu, D Alistarh
ICLR 2018, 2018
The convergence of sparsified gradient methods
D Alistarh, T Hoefler, M Johansson, N Konstantinov, S Khirirat, C Renggli
Advances in Neural Information Processing Systems 31, 2018
Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
T Hoefler, D Alistarh, T Ben-Nun, N Dryden, A Peste
The Journal of Machine Learning Research 22 (1), 10882-11005, 2021
Byzantine stochastic gradient descent
D Alistarh, Z Allen-Zhu, J Li
Advances in Neural Information Processing Systems 31, 2018
ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning
H Zhang, J Li, K Kara, D Alistarh, J Liu, C Zhang
International Conference on Machine Learning, 4035-4043, 2017
The spraylist: A scalable relaxed priority queue
D Alistarh, J Kopinsky, J Li, N Shavit
Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of …, 2015
Time-space trade-offs in population protocols
D Alistarh, J Aspnes, D Eisenstat, R Gelashvili, RL Rivest
Proceedings of the twenty-eighth annual ACM-SIAM symposium on discrete …, 2017
SparCML: High-performance sparse communication for machine learning
C Renggli, S Ashkboos, M Aghagolzadeh, D Alistarh, T Hoefler
Proceedings of the International Conference for High Performance Computing …, 2019
Gptq: Accurate post-training quantization for generative pre-trained transformers
E Frantar, S Ashkboos, T Hoefler, D Alistarh
arXiv preprint arXiv:2210.17323, 2022
Fast and exact majority in population protocols
D Alistarh, R Gelashvili, M Vojnović
Proceedings of the 2015 ACM Symposium on Principles of Distributed Computing …, 2015
Woodfisher: Efficient second-order approximation for neural network compression
SP Singh, D Alistarh
Advances in Neural Information Processing Systems 33, 18098-18109, 2020
Space-optimal majority in population protocols
D Alistarh, J Aspnes, R Gelashvili
Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete …, 2018
Inducing and exploiting activation sparsity for fast inference on deep neural networks
M Kurtz, J Kopinsky, R Gelashvili, A Matveev, J Carr, M Goin, W Leiserson, ...
International Conference on Machine Learning, 5533-5543, 2020
Polylogarithmic-time leader election in population protocols
D Alistarh, R Gelashvili
Automata, Languages, and Programming: 42nd International Colloquium, ICALP …, 2015
FPGA-accelerated dense linear machine learning: A precision-convergence trade-off
K Kara, D Alistarh, G Alonso, O Mutlu, C Zhang
2017 IEEE 25th Annual International Symposium on Field-Programmable Custom …, 2017
Tight bounds for asynchronous renaming
D Alistarh, J Aspnes, K Censor-Hillel, S Gilbert, R Guerraoui
Journal of the ACM (JACM) 61 (3), 1-51, 2014
Massive language models can be accurately pruned in one-shot
E Frantar, D Alistarh
arXiv preprint arXiv:2301.00774, 2023
Stacktrack: An automated transactional approach to concurrent memory reclamation
D Alistarh, P Eugster, M Herlihy, A Matveev, N Shavit
Proceedings of the Ninth European Conference on Computer Systems, 1-14, 2014
Fast randomized test-and-set and renaming
D Alistarh, H Attiya, S Gilbert, A Giurgiu, R Guerraoui
Distributed Computing: 24th International Symposium, DISC 2010, Cambridge …, 2010
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