Distributed frank-wolfe under pipelined stale synchronous parallelism

We are witnessing the move towards data center operating systems (OS), where resources are unified and  processing frameworks coexist with each other. In this context it has been shown that an iteration model with relaxed consistency such as the Stale Synchronous Parallel (SSP) model, while still guaranteeing convergence, is able to cope with the straggler problem for converging iterative algorithms. In this poster we present a model for the integration of the SSP model on a pipelined processing framework. We then apply the SSP on a distributed version of the Frank-Wolfe algorithm and empirically show its convergence under stress situations similar to those encountered in a data center OS.

 

Thomas Peel, and Nam-Luc Tran, Distributed Frank-Wolfe under Pipelined Stale Synchronous Parallelism, poster at the Greed is Great ICML’15 Workshop, Lille, France, July 2015

Analysis of interbank messages for the enforcement of financial regulations

In the context of the recent policies concerning anti-money laundering and counter terrorist financing defined by the Financial Action Task Force Recommendation 16, it is the responsibility of the financial institution to monitor the quality of the information present in wire transfers. To that end we present in this paper an approach to automate the monitoring and the validation of the information contained in interbank transfer messages. The approach is backed by a solution built around an event-driven architecture where the data is processed as a stream and transformed at each stage. This architecture is in line with the latest research in data warehouses with stream data processing. We show that our approach is suitable to the requirements and the standards in the banking industry.

Nam-Luc Tran, Analysis of Interbank Messages for the Enforcement of Financial Regulations, proceedings of Journées francophones sur les Entrepôts de Données et l’Analyse en ligne, Bruxelles, Belgium, April 2015.

Click here to access the paper.

An approach for maximizing performance on heterogeneous clusters of CPU and GPU

Over the past years there has been significant enthusiasm for development of parallel computing on Graphics Processing Units (GPU) which have now become powerful and affordable hardware equipping data centers and research clusters. Our earlier research has explored the ways to exploit the parallel compute performance of the GPU along the CPU in the same cluster. We have proposed a model for processing distributed machine learning tasks leveraging both the CPU and the GPU equipped on the nodes. Still in this direction, we present in this paper our approach for optimizing the performance of the previously proposed framework. We then further present our approach for integrating this processing model into a more general dataflow graph processing framework by extending it with support for GPU tasks and resources. In addition we have developed a k-nearest neighbors implementation demonstrating all the features. We then present our model based on flow networks for the efficient scheduling on this heterogeneous framework.

Nam-Luc Tran, Sabri Skhiri, Arnaud Schils, and Egar Isaac Hiroshi Leon Saiki, An Approach for Maximizing Performance on Heterogeneous Clusters of CPU and GPU. EURA NOVA technical series.

Click here to access the paper.

Analytics-aware graph database modeling

Graphs are a fundamental structure for modeling many real world domains and applications. They have emerged in various fields such as social, informational and transportation networks. The hetero geneity and dynamicity of these networks pose challenges to traditional techniques for data modeling, storage and analysis of data.

Managing graph-structured data using native graph structures and algorithms is the key for its efficient analysis. Therefore, the graph should be modeled using nodes and edges, and explored using graph algorithms, such as pattern matching and k-neighborhood.

In this paper, we introduce a novel model for management of graph data. The aim of our model is to provide analysts with a set of simple, well-defined, and adaptable components to perform complex graph modeling and analysis tasks.

Amine Ghrab, Oscar Romero, Sabri Skhiri, and Esteban Zimanyi, Analytics-Aware Graph Database Modeling, EURA NOVA technical series.

Click here to access the paper.

EURA NOVA Master Theses, 2013-2014 Season

Similar to the previous years EURA NOVA R&D has been supervising Master students either for their internship or for their Master Thesis during the 2013-1014 academic year. This year, 5 students have had the opportunity to work in the fields of Machine Learning, GPU compute, distributed processing, metabolic pathways and social graphs. This blog post summarizes their breakthroughs.

EURA NOVA Master Theses

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Projects Portfolio Management (PPM)

EURA NOVA - Projects Portfolio Management
(c) Can Stock Photo / Venimo

For meeting its strategic objectives, a company has to move from a situation A to a situation B. To achieve this move, it uses Projects as vehicles for performing Changes.

As you know, vehicles have specific purposes and therefore different sizes, styles, performance, costs, lifespan and sometimes also priorities.

As you also know, vehicles sometimes don’t arrive in destination B as initially foreseen: some may get out of fuel, others may use unexpected directions, or take more time than planned for arriving to destination. In worst cases, they may get damaged or even collide each others.

Let’s see how Projects Portfolio Management can select the right mix of vehicles, control them and increase success for vehicles to arrive all at the desired place, time & cost and without bad surprises.

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Testing the Tesla Model S

EURA NOVA recently tested the usually praised Tesla cars (The Model S P85 in our case). While we were impressed by the engineering work behind such a vehicle, some of its aspects convinced us that the near 100k € price point can’t be justified, in particular on the Belgian market with its volatile fiscal rules. Flashback on a very fun testing session!

Tesla Model S P85
Tesla Model S P85

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Event Based Marketing for the Telecommunications Industry

What is Event Based Marketing?

Event Based Marketing (EBM) that frequently is also referred to as Event Driven Marketing (EDM) or Trigger Based Marketing, is the discipline within marketing where commercial and communication activities are based upon these relevant and identified changes in a customer’s individual needs. It tends to identify key events in the customer’s life-cycle and trigger the most appropriate actions at the right time.

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