Scaling Distributed Machine Learning with In-Network Aggregation
KAUST: King Abdullah University of Science and Technology –
Training complex machine learning models in parallel is an increasingly important workload. We accelerate distributed parallel training by designing a communication primitive that uses a programmable switch dataplane to execute a key step of the training process. Our approach reduces the volume of exchanged data by aggregating the model updates from multiple workers in the network. We co-design the switch processing with the end-host protocols and ML frameworks to provide a robust, efficient solution that speeds up training by up to 310%, and at least by 20% in most cases for a number of real-world benchmark models.
Marco does not know what the next big thing will be. But he’s sure that our next-gen computing and networking infrastructure must be a viable platform for it and avoid stifling innovation. Marco’s research area is cloud computing, distributed systems and networking. His current interest is in designing better systems support for AI/ML and provide practical implementations deployable in the real-world.
Marco is an associate professor in Computer Science at KAUST. Marco obtained his Ph.D. in computer science and engineering from the University of Genoa in 2009 after spending the last year as a visiting student at the University of Cambridge, Computer Laboratory. He was a postdoctoral researcher at EPFL from 2009 to 2012 and after that a senior research scientist for one year at Deutsche Telekom Innovation Labs & TU Berlin. Before joining KAUST, he was an assistant professor at the UCLouvain. He also held positions at Intel, Microsoft and Google.
Date: 2019-Nov-15 Time: 15:00:00 Room: 020
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Workshop Metabolism and mathematical models: Two for a tango
Dates: November 18-19, 2021
Location: This workshop will be held in a virtual way
The topic of this workshop is metabolism in general, with a special focus, although not exclusive, on parasitology. Besides an exploration of the biological, biochemical and biomedical aspects, the workshop will also aim at presenting some of the mathematical modelling, algorithmic theory and software development that have become crucial to explore such aspects.
This workshop is being organised in the context of two projects, both with the Inria European Team Erable. One of the projects involves a partnership with the University of São Paulo (USP), in São Paulo, Brazil, more specifically the Institute of Mathematics and Statistics (IME) and the Institute of Biomedical Sciences – Inria Associated Team Capoeira – and the other involves the Inesc-ID/IST in Portugal, ETH in Zürich and EMBL in Heidelberg – H2020 Twinning Project Olissipo.
The workshop is open to all members of these two projects but also, importantly, to the community in general.