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.


On this site:

Team


Current Projects

Distributed machine learning | Concurrent data structures and applications | Molecular computation


Publications

Alistarh D-A, Aspnes J, Ellen F, Gelashvili R, Zhu L. 2020. Brief Announcement: Why Extension-Based Proofs Fail. Proceedings of the 39th Symposium on Principles of Distributed Computing. PODC: Principles of Distributed Computing 54–56. View

Gurel NM, Kara K, Stojanov A, Smith T, Lemmin T, Alistarh D-A, Puschel M, Zhang C. 2020. Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications. IEEE Transactions on Signal Processing. View

Alistarh D-A, Brown TA, Singhal N. 2020. Memory tagging: Minimalist synchronization for scalable concurrent data structures. Annual ACM Symposium on Parallelism in Algorithms and Architectures. SPAA: Symposium on Parallelism in Algorithms and Architectures 37–49. View

Czumaj A, Davies P, Parter M. 2020. Graph sparsification for derandomizing massively parallel computation with low space. Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2020). SPAA: Symposium on Parallelism in Algorithms and Architectures 175–185. View

Czumaj A, Davies P, Parter M. 2020. Simple, deterministic, constant-round coloring in the congested clique. Proceedings of the 2020 ACM Symposium on Principles of Distributed Computing. PODC: Symposium on Principles of Distributed Computing 309–318. View

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Career

since 2017 Assistant Professor, IST Austria
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


Additional Information

Dan Alistarh’s website



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