Virginie Galtier

A Fault Tolerant and Multi-Paradigm Grid Architecture for Time Constrained Problems. Application to Option Pricing in Finance.

This paper introduces a Grid software architecture offering fault tolerance, dynamic and aggressive load balancing and two complementary parallel programming paradigms. Experiments with financial applications on a real multi-site Grid assess this solution. This architecture has been designed to run industrial and financial applications, that are frequently time constrained and CPU consuming, feature both tightly and loosely coupled parallelism requiring generic programming paradigm, and adopt client-server business architecture.

AdaBoost Parallelization on PC Clusters with Virtual Shared Memory for Fast Feature Selection

Feature selection is a key issue in many machine learning applications and the need to test lots of candidate features is real while computational time required to do so is often huge. In this paper, we introduce a parallel version of the well-known AdaBoost algorithm to speed up and size up feature selection for binary classification tasks using large training datasets and a wide range of elementary features. This parallelization is done without any modification to the AdaBoost algorithm and designed for PC clusters using Java and the JavaSpace distributed framework. JavaSpace is a memory sharing paradigm implemented on top of a virtual shared memory, that appears both efficient and easy-to-use. Results and performances on a face detection system trained with the proposed parallel AdaBoost are presented.

Implementation of the AdaBoost Algorithm for Large Scale Distributed Environments: Comparing JavaSpace and MPJ

This paper presents the parallelization of a machine learning method, called the adaboost algorithm. The parallel algorithm follows a dynamically load-balanced master-worker strategy, which is parameterized by the granularity of the tasks distributed to workers. We first show the benefits of this version with heterogeneous processors. Then, we study the application in a real, geographically distributed environment, hence adding network latencies to the execution. Performances of the application using more than a hundred processes are analyzed in both JavaSpace and P2P-MPI. We therefore present an head-to-head comparison of two parallel programming models. We study for each case the granularities yielding the best performance. We show that current network technologies enable to obtain interesting speedups in many situations for such an application, even when using a virtual shared memory paradigm in a large-scale distributed environment.

Simulation and platform tools to develop safe flock of UAVs: a CPS application-driven research

Autonomous formation flying (flocking) of UAVs requires coordination of efforts and solutions in multiple research fields (robotics, wireless sensor networks to name a few). The Airborne Embedded auTonomOUs Robust Network of Objects and Sensors (AETOURNOS) project is designed as a federative, fueling platform and global evaluation tool. It has spawned another collaborative project bridging the gap between Computer Science and Automation: 6PO (Cyber-Physical Systems and Cooperative Control of a Fleet of UAVs). We want to investigate new flocking strategies, mobility-aware communication protocols (and reciprocally: movements influenced by network QoS), and to ease the cooperation between experts, we need co-simulation solutions, and “universal” control interfaces. This article presents our first contribution to this grand scheme: a stand-alone simulation of an autonomous flocking strategy taking into account both the dynamics of the UAVs and their communications, which has allowed us to examine the relation between network unreliability and control computation. To better study the influence of the communication network, we present an architecture coupling a dedicated network simulator with the UAVs simulator, as well as with a real drone piloting interface. The lessons learned help us build a research and action plan for the years to come.

A Fault Tolerant and Multi-Paradigm Grid Architecture for Time Constrained Problems. Application to Financial Option Pricing

This paper introduces a Grid software architecture offering fault tolerance, dynamic and aggressive load balancing and two complementary parallel programming paradigms. Experiments with financial applications on a real multi-site Grid assess this solution. This architecture has been designed to run industrial and financial applications, that are frequently time constrained and CPU consuming, feature both tightly and loosely coupled parallelism requiring generic programming paradigm, and adopt client-server business architecture.

A Skeletal-Based Approach for the Development of Fault-Tolerant SPMD Applications

Distributing applications over PC clusters to speed-up or size-up the execution is now commonplace. Yet efficiently tolerating faults of these systems is a major issue. To ease the addition of checkpoint-based fault tolerance at the application level, we introduce a {\em Model for Low-Overhead Tolerance of Faults}(MoLOToF) which is based on structuring applications using {\em fault-tolerant skeletons}. MoLOToF also encourages collaborations with the programmer and the execution environment. The skeletons are adapted to specific parallelization paradigms and yield what can be called {\em fault-tolerant algorithmic skeletons}. The application of MoLOToF to the SPMD parallelization paradigm results in our proposed FT-SPMD framework. Experiments show that the complexity for developing an application is small and the use of the framework has a small impact on performance. Comparisons with existing system-level checkpoint solutions, namely LAM/MPI and DMTCP, point out that FT-SPMD has a lower runtime overhead while being more robust when a higher level of fault tolerance is required.

A Javaspace-Based Framework for Efficient Fault-Tolerant Master-Worker Distributed Applications

We propose a framework built around a Java Space to ease the development of bag-of-tasks applications. The framework may optionally and automatically tolerate transient crash failures occurring on any of the distributed elements. It relies on check pointing and underlying middleware mechanisms to do so. To further improve check pointing efficiency, both in size and frequency, the programmer can introduce intermediate user-defined checkpoint data and code within the task processing program. The framework used without fault tolerance accelerates application development, does not introduce runtime overhead and yields to expected speedup. When enabling fault tolerance, our framework allows, despite failures, correct completion of applications with limited runtime and data storage overheads. Experiments run with up to 128 workers study the impact of some user-related and implementation-related on overall performance, and reveal good performances for classical Java Space-based master-worker application profiles.