Zobrazeno 1 - 10
of 376
pro vyhledávání: '"Nadiradze, A."'
Determining the degree of inherent parallelism in classical sequential algorithms and leveraging it for fast parallel execution is a key topic in parallel computing, and detailed analyses are known for a wide range of classical algorithms. In this pa
Externí odkaz:
http://arxiv.org/abs/2304.09331
Distributed optimization is the standard way of speeding up machine learning training, and most of the research in the area focuses on distributed first-order, gradient-based methods. Yet, there are settings where some computationally-bounded nodes m
Externí odkaz:
http://arxiv.org/abs/2210.07703
Federated Learning (FL) enables large-scale distributed training of machine learning models, while still allowing individual nodes to maintain data locally. However, executing FL at scale comes with inherent practical challenges: 1) heterogeneity of
Externí odkaz:
http://arxiv.org/abs/2206.10032
Publikováno v:
In European Journal of Paediatric Neurology July 2024 51:125-131
Designing and implementing efficient parallel priority schedulers is an active research area. An intriguing proposed design is the Multi-Queue: given $n$ threads and $m\ge n$ distinct priority queues, task insertions are performed uniformly at random
Externí odkaz:
http://arxiv.org/abs/2109.00657
This paper gives tight logarithmic lower bounds on the solo step complexity of leader election in an asynchronous shared-memory model with single-writer multi-reader (SWMR) registers, for randomized obstruction-free algorithms. The approach extends t
Externí odkaz:
http://arxiv.org/abs/2108.02802
Autor:
Li, Shigang, Ben-Nun, Tal, Nadiradze, Giorgi, Di Girolamo, Salvatore, Dryden, Nikoli, Alistarh, Dan, Hoefler, Torsten
Publikováno v:
in IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 7, pp. 1725-1739, 1 July 2021
Deep learning at scale is dominated by communication time. Distributing samples across nodes usually yields the best performance, but poses scaling challenges due to global information dissemination and load imbalance across uneven sample lengths. St
Externí odkaz:
http://arxiv.org/abs/2005.00124
Several classic problems in graph processing and computational geometry are solved via incremental algorithms, which split computation into a series of small tasks acting on shared state, which gets updated progressively. While the sequential variant
Externí odkaz:
http://arxiv.org/abs/2003.09363
We consider the following dynamic load-balancing process: given an underlying graph $G$ with $n$ nodes, in each step $t\geq 0$, one unit of load is created, and placed at a randomly chosen graph node. In the same step, the chosen node picks a random
Externí odkaz:
http://arxiv.org/abs/2003.09297
Machine learning has made tremendous progress in recent years, with models matching or even surpassing humans on a series of specialized tasks. One key element behind the progress of machine learning in recent years has been the ability to train mach
Externí odkaz:
http://arxiv.org/abs/2001.05918