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The objective of this study is to evaluate the performances of Intel Xeon Phi hardware accelerators for Geant4 simulations, especially for multithreaded applications. We present the complete methodology to guide users for the compilation of their Geant4 applications on Phi processors. Then, we propose series of benchmarks to compare the performance of Xeon CPUs and Phi processors for a Geant4 example dedicated to the simulation of electron dose point kernels, the TestEm12 example. First, we compare a distributed execution of a sequential version of the Geant4 example on both architectures before evaluating the multithreaded version of the Geant4 example. If Phi processors demonstrated their ability to accelerate computing time (till a factor 3.83) when distributing sequential Geant4 simulations, we do not reach the same level of speedup when considering the multithreaded version of the Geant4 example.

Ensuring traceability of both field experimental data and laboratory sampling data for a reproducible research remains a challenge nowadays. Between the

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International audienceThe implementation of a grid network to support large-scale epidemiology analysis (based on distributed medical data sources) and medical data sharing require medical data integration and semantic alignment. In this paper, we present the GINSENG (Global Initiative for Sentinel e-Health Network on Grid) network that federates existing Electronic Health Records through a rich metamodel (FedEHR), a semantic data model (SemEHR) and distributed query toolkits. A query interface based on the VIP platform, and available through the e-ginseng.org web portal helps medical end-users in the design of epidemiological studies and the retrieval of relevant medical data sets

Phylogenetic Oligonucleotide Arrays (POAs) were recently adapted for studying the huge microbial communities in a flexible and easy-to-use way. POA coupled with the use of explorative probes to detect the unknown part is now one of the most powerful approaches for a better understanding of microbial community functioning. However, the selection of probes remains a very difficult task. The rapid growth of environmental databases has led to an exponential increase of data to be managed for an efficient design. Consequently, the use of high performance computing facilities is mandatory. In this paper, we present an efficient parallelization method to select known and explorative oligonucleotide probes at large scale using computing grids. We implemented a software that generates and monitors thousands of jobs over the European Computing Grid Infrastructure (EGI). We also developed a new algorithm for the construction of a high-quality curated phylogenetic database to avoid erroneous design due to bad sequence affiliation. We present here the performance and statistics of our method on real biological datasets based on a phylogenetic prokaryotic database at the genus level and a complete design of about 20,000 probes for 2,069 genera of prokaryotes.