Concept

Apache Spark

Publications associées (42)

Local SGD Converges Fast and Communicates Little

Mini-batch stochastic gradient descent (SGD) is state of the art in large scale distributed training. The scheme can reach a linear speedup with respect to the number of workers, but this is rarely seen in practice as the scheme often suffers from large ne ...

2019

Streaming Principal Component Analysis From Incomplete Data

Armin Eftekhari

Linear subspace models are pervasive in computational sciences and particularly used for large datasets which are often incomplete due to privacy issues or sampling constraints. Therefore, a critical problem is developing an efficient algorithm for detecti ...

MICROTOME PUBL2019

An accelerated communication-efficient primal-dual optimization framework for structured machine learning

Martin Jaggi, Chenxin Ma

Distributed optimization algorithms are essential for training machine learning models on very large-scale datasets. However, they often suffer from communication bottlenecks. Confronting this issue, a communication-efficient primal-dual coordinate ascent ...

2019

Decentralized clustering for node-variant graph filtering with graph diffusion LMS

Ali H. Sayed, Roula Nassif

In this work, we consider the problem of estimating the coefficients of linear shift-invariant FIR graph filters. We assume hybrid node-varying graph filters where the network is decomposed into clusters of nodes and, within each cluster, all nodes have th ...

IEEE2018

Don't cry over spilled records: Memory elasticity of data-parallel applications and its application to cluster scheduling

Willy Zwaenepoel, Calin Iorgulescu, Syed Mohammad Aunn Raza, Florin Dinu, Wajih Ul Hassan

Understanding the performance of data-parallel workloads when resource-constrained has significant practical importance but unfortunately has received only limited attention. This paper identifies, quantifies and demonstrates memory elasticity, an intrinsi ...

2017

Don't cry over spilled records: Memory elasticity of data-parallel applications and its application to cluster scheduling

Willy Zwaenepoel, Calin Iorgulescu, Syed Mohammad Aunn Raza, Florin Dinu, Wajih Ul Hassan

The USENIX Association2017

Distributed Time Series Analytics

Tian Guo

In recent years time series data has become ubiquitous thanks to affordable sensors and advances in embedded technology. Large amount of time-series data are continuously produced in a wide spectrum of applications, such as sensor networks, medical monitor ...

EPFL2017

MedCo: Enabling Privacy-Conscious Exploration of Distributed Clinical and Genomic Data

Jean-Pierre Hubaux, Bryan Alexander Ford, Juan Ramón Troncoso-Pastoriza, Jean Louis Raisaro, Joao André Gomes de Sá e Sousa, Mickaël Misbach, Olivier Michielin

Being able to share large amounts of sensitive clinical and genomic data across several institutions is crucial for precision medicine to scale up. Unfor- tunately, existing solutions only partially address this challenge and are still unable to provide th ...

2017

Big-Data Streaming Applications Scheduling Based on Staged Multi-armed Bandits

David Atienza Alonso, Karim Kanoun, Cem Tekin

Several techniques have been recently proposed to adapt Big-Data streaming applications to existing many core platforms. Among these techniques, online reinforcement learning methods have been proposed that learn how to adapt at run-time the throughput and ...

Institute of Electrical and Electronics Engineers2016

Big-Data Streaming Applications Scheduling Based on Staged Multi-Armed Bandits

David Atienza Alonso, Karim Kanoun, Cem Tekin

Institute of Electrical and Electronics Engineers2016

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