D
Dan Alistarh
Researcher at Institute of Science and Technology Austria
Publications - 213
Citations - 4887
Dan Alistarh is an academic researcher from Institute of Science and Technology Austria. The author has contributed to research in topics: Computer science & Stochastic gradient descent. The author has an hindex of 27, co-authored 175 publications receiving 3761 citations. Previous affiliations of Dan Alistarh include ETH Zurich & Microsoft.
Papers
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Proceedings ArticleDOI
Forkscan: Conservative Memory Reclamation for Modern Operating Systems
TL;DR: Empirical evaluation on a range of classical concurrent data structure microbenchmarks shows that Forkscan can preserve the scalability of the original code, while maintaining an order of magnitude lower latency than automatic garbage collection, and demonstrating competitive performance with finely crafted memory reclamation techniques.
Proceedings Article
Streaming Min-max hypergraph partitioning
TL;DR: This paper shows that a greedy assignment strategy is able to recover a hidden co-clustering of items under a natural set of recovery conditions, and demonstrates that this greedy strategy yields superior performance when compared with alternative approaches.
Proceedings ArticleDOI
A High-Radix, Low-Latency Optical Switch for Data Centers
Dan Alistarh,Hitesh Ballani,Paolo Costa,Adam Funnell,Joshua L. Benjamin,Philip M. Watts,Benn C. Thomsen +6 more
TL;DR: An optical switch design that can scale up to a thousand ports with high per-port bandwidth (25 Gbps+) and low switching latency (40 ns) is demonstrated, based on a passive star coupler and fast tunable transceivers.
Journal ArticleDOI
Of Choices, Failures and Asynchrony: The Many Faces of Set Agreement
TL;DR: This paper introduces a novel technique for simulating, in a fault-prone asynchronous shared memory, executions of an asynchronous and failure-prone message-passing system in which some fragments appear synchronous to some processes, and derives the size of the minimal window of synchrony needed to solve set agreement.
Posted Content
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner,Dan Alistarh,Gustavo Alonso,David G. Andersen,Peter Bailis,Sarah Bird,Nicholas Carlini,Bryan Catanzaro,Jennifer Chayes,Eric T. Chung,Bill Dally,Jeffrey Dean,Inderjit S. Dhillon,Alexandros G. Dimakis,Pradeep Dubey,Charles Elkan,Grigori Fursin,Gregory R. Ganger,Lise Getoor,Phillip B. Gibbons,Garth A. Gibson,Joseph E. Gonzalez,Justin Gottschlich,Song Han,Kim Hazelwood,Furong Huang,Martin Jaggi,Kevin Jamieson,Michael I. Jordan,Gauri Joshi,Rania Khalaf,Jason Knight,Jakub Konečný,Tim Kraska,Arun Kumar,Anastasios Kyrillidis,Aparna Lakshmiratan,Jing Li,Samuel Madden,H. Brendan McMahan,Erik Meijer,Ioannis Mitliagkas,Rajat Monga,Derek G. Murray,Kunle Olukotun,Dimitris S. Papailiopoulos,Gennady Pekhimenko,Theodoros Rekatsinas,Afshin Rostamizadeh,Christopher Ré,Christopher De Sa,Hanie Sedghi,Sercan Sen,Virginia Smith,Alexander J. Smola,Dawn Song,Evan R. Sparks,Ion Stoica,Vivienne Sze,Madeleine Udell,Joaquin Vanschoren,Shivaram Venkataraman,Rashmi Vinayak,Markus Weimer,Andrew Gordon Wilson,Eric P. Xing,Matei Zaharia,Ce Zhang,Ameet Talwalkar +68 more
TL;DR: It is proposed to foster a new systems machine learning research community at the intersection of the traditional systems and ML communities, focused on topics such as hardware systems for ML, software systems forML, and ML optimized for metrics beyond predictive accuracy.