Dan Alistarh
Cited by
Cited by
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, 1709-1720, 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, S Khirirat, N Konstantinov, C Renggli
arXiv preprint arXiv:1809.10505, 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
Byzantine stochastic gradient descent
D Alistarh, Z Allen-Zhu, J Li
arXiv preprint arXiv:1803.08917, 2018
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
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
Polylogarithmic-time leader election in population protocols
D Alistarh, R Gelashvili
International Colloquium on Automata, Languages, and Programming, 479-491, 2015
Space-optimal majority in population protocols
D Alistarh, J Aspnes, R Gelashvili
Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete …, 2018
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
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
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
Fast randomized test-and-set and renaming
D Alistarh, H Attiya, S Gilbert, A Giurgiu, R Guerraoui
International Symposium on Distributed Computing, 94-108, 2010
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
Are lock-free concurrent algorithms practically wait-free?
D Alistarh, K Censor-Hillel, N Shavit
Journal of the ACM (JACM) 63 (4), 1-20, 2016
Sub-logarithmic test-and-set against a weak adversary
D Alistarh, J Aspnes
International Symposium on Distributed Computing, 97-109, 2011
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
arXiv preprint arXiv:2102.00554, 2021
Optimal-time adaptive strong renaming, with applications to counting
D Alistarh, J Aspnes, K Censor-Hillel, S Gilbert, M Zadimoghaddam
Proceedings of the 30th annual ACM SIGACT-SIGOPS symposium on Principles of …, 2011
Threadscan: Automatic and scalable memory reclamation
D Alistarh, W Leiserson, A Matveev, N Shavit
ACM Transactions on Parallel Computing (TOPC) 4 (4), 1-18, 2018
The system can't perform the operation now. Try again later.
Articles 1–20