distributed computing

EF-BV: distributed optimization with compressed communication

Laurent Condat, Senior Research Scientist, Computer Science

Oct 10, 12:00 - 13:00

B9 L2 R2322 H1

big data distributed computing Federated learning

In the big data era, it is necessary to rely on distributed computing. For distributed optimization and learning tasks, in particular in the modern paradigm of federated learning, specific challenges arise, such as decentralized data storage. Communication between the parallel machines and the orchestrating distant server is necessary but slow. To address this main bottleneck, a natural strategy is to compress the communicated vectors. I will present EF-BV, a new algorithm which converges linearly to an exact solution, with a large class of deterministic or random, biased or unbiased compressors.

Mojtaba Alshams

Ph.D. Student, Electrical and Computer Engineering

distributed computing Deep learning Computer Vision edge compute paradigms distributed deep learning edge devices energy flexible and efficient inference

Ali Charara

Ph.D., Computer Science

High Performance Computing algorithms distributed computing Linux GPU Computing OpenMP parallel computing

Ali Charara obtained both, his MS Degree and PhD Degree from Computer Science at KAUST in 2011 and 2018 respectively. His master thesis supervisor was Alyn Rockwood and his PhD thesis supervisor was David Keyes. Ali's research interests were in High Performance Computing with a focus on parallel computing and algorithms using multicore and manycore Computer Graphics Units. Education Profile MS Degree, Computer Science, KAUST (2011) PhD Degree, Computer Science, KAUST (2018)