Events

May 26 - Jun 2, 2024

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  Compressed Communication in Distributed Deep Learning and Generative Language Models

  Hang Xu, Ph.D. Student, Computer Science

  May 28, 15:00 - 17:00

  B2 L5 R5209

  Deep Learning and generative Artificial Intelligence has grown rapidly during the past few years due to the advancement of computing powers and parallel distributed training algorithms. As a result, it has been a common practice to use hundreds or thousands of machines to train very large Deep Neural Networks.

Oct 13 - Oct 20, 2019

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  Use Mathematics and get a SPARQL Query Engine (almost) for Free! It’s MAGiQ!

  Panagiotis Kalnis, Professor, Computer Science

  Oct 14, 12:00 - 13:00

  B9 L2 H1 R2322

  sparse matrix algebra RDF engine MAGIQ SPARQL queries RDF graphs optimization CPU GPU

  Existing RDF engines are designed for specific hardware architectures; porting to a different architecture (e.g., GPUs) entails enormous implementation effort. We explore sparse matrix algebra as an alternative for designing a portable, scalable and efficient RDF engine.

Sep 22 - Sep 29, 2019

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  Exploring FPGAs for Virtualized In-Network Acceleration

  Dr. Suhaib Fahmy, Associate Professor, Computer Engineering, University of Warwick, UK

  Sep 24, 12:00 - 13:00

  B2 B3 A0215

  FPGA data centers streaming applications accelerators big data cloud computing

  Abstract With increasing connectivity and reliance on machine intelligence to process ever-growing amounts of data, the question of how to arrange the required connectivity and computation arises. Traditional cloud computing approaches that centralize compute capability in a data center do not scale well to large scale distributed data sources that must then transmit data over constrained networks. Similarly, computing at the very edge of the network is often constrained by limited computational capacity and a lack of access to shared data. In-network computing has been proposed as a way of

Mar 3 - Mar 10, 2019

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  Mining Large Data Series Collections: Motif, Discord and Anomaly Discovery

  Speaker: Michele Linardi, Ph.D. candidate at Paris Descartes University

  Mar 5, 17:00 - 18:00

  B3 L5 R5209

  infoclouds

  In the last fifteen years, data series motif and discord discovery have emerged as two useful primitives for data series mining, with applications to many domains, including robotics, entomology, seismology, medicine, and climatology.

Mar 18 - Mar 25, 2018

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  Computational and Statistical Interface to Big Data

  Xin Gao, Program Chair, Computer Science

  Mar 19, 08:00 - Mar 21, 17:00

  B9 L2 H2

  We are now in the fourth paradigm of science: Data Science. The massive amount of structured and unstructured data has posed new challenges and opportunities to the fields of computer science and statistics. Traditional computational and statistical methods for data storage, curation, sharing, querying, updating, visualization, analysis, and privacy have been shown to fail in the big data scenario due to the unprecedented volume, velocity, variety, veracity and value of the big data. This conference will bring together a number of prominent researchers in Computer Science and Statistics with common interests and active research in big data, as well as the researchers at KAUST who regularly generate or face big data, such as those in bioscience and red sea research.