Alistarh Group
Distributed Algorithms and Systems
Distribution has been one of the key trends in computing over the last decade: processor architectures are multi-core, while large-scale systems for machine learning and data processing can be distributed across several machines or even data centers. The Alistarh group works to enable these applications by creating algorithms that scale—that is, they improve their performance when more computational units are available.
This fundamental shift to distributed computing performed puts forward exciting open questions: How do we design algorithms to extract every last bit of performance from the current generation of architectures? How do we design future architectures to support more scalable algorithms? Are there clean abstractions to render high-performance distribution accessible to programmers? The group’s research is focused on answering these questions. In particular, they are interested in designing efficient, practical algorithms for fundamental problems in distributed computing, in understanding the inherent limitations of distributed systems, and in developing new ways to overcome these limitations. One particular area of focus over the past few years has been distributed machine learning.
Group Leader
Administrative Support
Christine Francois
Assistant to Professors
+43 2243 9000 1137
On this site:
Team
John Lazarsfeld
Predoctoral Visiting Scientist
Current Projects
Distributed machine learning | Concurrent data structures and applications | Molecular computation
Publications
Alistarh D-A, Rybicki J, Voitovych S. 2022. Near-optimal leader election in population protocols on graphs. Proceedings of the Annual ACM Symposium on Principles of Distributed Computing. PODC: Symposium on Principles of Distributed Computing, 246–256. View
Balliu A, Hirvonen J, Melnyk D, Olivetti D, Rybicki J, Suomela J. 2022. Local mending. International Colloquium on Structural Information and Communication Complexity. SIROCCO: Structural Information and Communication ComplexityLNCS vol. 13298, 1–20. View
Postnikova A, Koval N, Nadiradze G, Alistarh D-A. 2022. Multi-queues can be state-of-the-art priority schedulers. Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. PPoPP: Sympopsium on Principles and Practice of Parallel Programming, 353–367. View
Brown TA, Sigouin W, Alistarh D-A. 2022. PathCAS: An efficient middle ground for concurrent search data structures. Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. PPoPP: Sympopsium on Principles and Practice of Parallel Programming, 385–399. View
Shevchenko A, Kungurtsev V, Mondelli M. 2022. Mean-field analysis of piecewise linear solutions for wide ReLU networks. Journal of Machine Learning Research. 23(130), 1–55. View
ReX-Link: Dan Alistarh
Career
since 2017 Assistant Professor, Institute of Science and Technology Austria (ISTA)
2016 – 2017 “Ambizione Fellow”, Computer Science Department, ETH Zurich
2014 – 2016 Researcher, Microsoft Research, Cambridge, UK
2014 – 2016 Morgan Fellow, Downing College, University of Cambridge, UK
2012 – 2013 Postdoc, Massachusetts Institute of Technology, Cambridge, USA
2012 PhD, EPFL, Lausanne, Switzerland
Selected Distinctions
2018 ERC Starting Grant
2015 Awarded Swiss National Foundation “Ambizione” Fellowship
2014 Elected Morgan Fellow at Downing College, University of Cambridge
2012 Postdoctoral Fellowship of the Swiss National Foundation
2011 Best Paper Award at the International Conference on Distributed Computing and Networking
