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20172024
most citedSparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks

341 citations · 371 across the 17 of their papers we have counts for

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9 papers · 1 filter

cs.DS2022

Fast and Scalable Channels in Kotlin Coroutines

Nikita Koval, Dan Alistarh, Roman Elizarov

Asynchronous programming has gained significant popularity over the last decade: support for this programming pattern is available in many popular languages via libraries and nativ…

cs.DS2021

Multi-Queues Can Be State-of-the-Art Priority Schedulers

Anastasiia Postnikova, Nikita Koval, Giorgi Nadiradze +1

Designing and implementing efficient parallel priority schedulers is an active research area. An intriguing proposed design is the Multi-Queue: given threads and disti…

cs.DS2021

A Scalable Concurrent Algorithm for Dynamic Connectivity

Alexander Fedorov, Nikita Koval, Dan Alistarh

Dynamic Connectivity is a fundamental algorithmic graph problem, motivated by a wide range of applications to social and communication networks and used as a building block in vari…

cs.DS2020

Efficiency Guarantees for Parallel Incremental Algorithms under Relaxed Schedulers

Dan Alistarh, Nikita Koval, Giorgi Nadiradze

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…

cs.DS2020

Dynamic Averaging Load Balancing on Cycles

Dan Alistarh, Giorgi Nadiradze, Amirmojtaba Sabour

We consider the following dynamic load-balancing process: given an underlying graph with nodes, in each step , one unit of load is created, and placed at a randoml…

cs.DS20201 cited

Analysis and Evaluation of Non-Blocking Interpolation Search Trees

Aleksandar Prokopec, Trevor Brown, Dan Alistarh

We start by summarizing the recently proposed implementation of the first non-blocking concurrent interpolation search tree (C-IST) data structure. We then analyze the individual o…