International audience

To study the constituents of matter, CERN mainly relies on the Worldwide LHC Computing Grid (WLCG), which processes petabytes of data coming from the Large Hadron Collider (LHC). LHC experiments have adopted the Pilot-Job paradigm, and deliver tools to supply grid resources with Pilot-Jobs, to efficiently leverage the computing power offered by WLCG. This sole approach will be insufficient and will need to be complemented to meet future computing needs – of the High-Luminosity LHC – and the rise of data generated over time: national science programs are consolidating computing resources and encourage using cloud and High-Performance Computing systems. Yet, even though they have started to integrate their workflows on such infrastructures, LHC experiments still largely depend on WLCG resources. This paper lays out an approach to increase the throughput of the jobs, on grid resources, by improving the performance of the Pilot-Job provisioning tools through a case study: the LHCb-specific solution, known as “DIRAC Site Director”. We propose: (i) a complete analysis of the capabilities and limitations of the DIRAC Site Director; (ii) several methods to speed up its execution, including parallel processing as well as bulk operations; (iii) a comprehensive analysis of a group of Site Directors in the LHCb production environment during 12 months. With our approach, we recorded an increase of 40.86% of the number of jobs processed simultaneously per second, enabling the simultaneous management of 80,300 LHCb jobs, while only 57,000 of them could be managed before our improvements.

International audience

Summary Complete backtranslation is the step of generating all possible nucleic acid sequences from a protein sequence. This is a time‐consuming task that can provide unreasonable quantities of data. Complete backtranslation was recently used to initiate probe design for functional DNA microarrays from conserved peptidic regions, in order to assess the full microbial gene diversity present in complex environments. In this article, we present an efficient parallelization method to compute a complete backtranslation of short peptides to select probes for functional microarrays. We implemented a software that uses meta‐programming and a model‐driven engineering approach to automatically generate source codes to perform complete backtranslation on different architectures: PCs, Symmetric Multiprocessors servers, computing clusters, or a computing grid. Our software is filtering the generated oligonucleotides with usual selection criteria for the design of microarray probes. It uses load balancing and can be easily integrated in probe design software for functional microarrays. We present its performance on both simulated and real biological datasets. The obtained results show a significant computing speedup on different platforms and an important gain of about 40% of disk space when filtering oligonucleotides. Copyright © 2014 John Wiley & Sons, Ltd.

Summary Several software efforts have been produced over the past few years in various programming languages to help developers handle pseudorandom streams partitioning. Parallel and distributed stochastic simulations can obviously benefit from this kind of high‒level tools. The latest release of the Java Development Kit (JDK 7) tries to tackle this problem by providing facilities to partition a pseudorandom stream across various threads thanks to the new classThreadLocalRandom. Meanwhile, Java 7 offers a framework to split a problem in a divide and conquer way through the new class calledForkJoinPool. As any other Java thread pool, ForkJoin exploits threads as workers and manipulates the tasks that will be run on the workers. InThreadLocalRandom, pseudorandom number generation is handled at a thread level. As a consequence, a scientific application taking advantage of a Java thread pool to parallelize its computation may suffer from a bad pseudorandom stream partitioning due to the behavior ofThreadLocalRandom. The present work introduces TaskLocalRandom, a task‒level alternative toThreadLocalRandomthat solves this partitioning problem and assigns an independent pseudorandom stream to each task run in the thread pool. TaskLocalRandom is compatible with existing Java thread pools such as Executors or ForkJoin. Copyright © 2014 John Wiley & Sons, Ltd.

The 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.

For the sake of software compatibility, simulations are often parallelized without much code rewriting. Performances can be further improved by optimizing codes so that to use the maximum power offered by parallel architectures. While this approach can provide some speed-up, performance of parallelized codes can be strongly limited a priori because traditional algorithms have been designed for sequential technologies. Thus, additional increase of performance should ultimately rely on some redesign of algorithms.

SUMMARY There is an increasing interest in the distribution of parallel random number streams in the high‐performance computing community particularly, with the manycore shift. Even if we have at our disposal statistically sound random number generators according to the latest and thorough testing libraries, their parallelization can still be a delicate problem. Indeed, a set of recent publications shows it still has to be mastered by the scientific community. With the arrival of multi‐core and manycore processor architectures on the scientist desktop, modelers who are non‐specialists in parallelizing stochastic simulations need help and advice in distributing rigorously their experimental plans and replications according to the state of the art in pseudo‐random numbers parallelization techniques. In this paper, we discuss the different partitioning techniques currently in use to provide independent streams with their corresponding software. In addition to the classical approaches in use to parallelize stochastic simulations on regular processors, this paper also presents recent advances in pseudo‐random number generation for general‐purpose graphical processing units. The state of the art given in this paper is written for simulation practitioners. Copyright © 2012 John Wiley & Sons, Ltd.

Intensive computing and numerical simulation are now essential tools that contribute to the success in systems designs, effectiveness of public policies such as prevention of natural hazards and taking account of climate risks, but also to security and national sovereignty. There are indications that the scientific community is technologically ready for the implementation of private HPC clouds, although a full HPC cloud solution running on virtual machines (VM) may remain application dependent. The possibility of having HPC cloud computing brings fast-compute clusters within the reach of researchers and users for whom traditional HPC facilities are not a choice. For example, with the BiG Grid HPC Cloud, users get access to a virtualized HPC Cluster that they can configure to exactly match their needs. Still, it provides self-service and dynamically scalable high performance computing facilities.

Centralized management of patient data is no more a viable solution. In many countries, patient identification restrictions due to privacy laws implies developing thorough mechanism to avoid duplicates and information loss. In this paper we present a work in progress dealing with a grid distributed medical data base. GPU based identification algorithms for disease surveillance, medical data exchange and epidemiological analyses.

The selection of oligonucleotide probes for micro arrays is still very difficult task. With the rapid growth of environmental databases (metagenomics programs coupled to next generation sequencing), the computational capacity requirements of probe design algorithms have hugely increased. The use of parallel and distributed architectures can considerably reduce the complexity and the computational time of these algorithms. In this paper we present a new efficient algorithm of oligonucleotide probe selection for an individual specific nucleic acid sequence or a group of sequences. We used a model driven engineering approach to simultaneous design of thousands of sensitive, specific, isothermal and explorative probes, on both PC, multiprocessor, cluster and grid computing with on the one hand a significant computing speedup and on the other hand an improved quality of the resulting probes when compared to equivalent software.

SUMMARY This paper presents an open source toolkit allowing a rigorous distribution of stochastic simulations. It is designed according to the state of the art in pseudo‐random numbers partitioning techniques. Based on a generic XML format for saving pseudo‐random number generator states, each state contains adapted metadata. This toolkit named DistMe is usable by modelers who are non‐specialists in parallelizing stochastic simulations, it helps in distributing the replications and in the generation of experimental plans. It automatically writes ready for runtime scripts for various parallel platforms, encapsulating the burden linked to the management of status files for different pseudo‐random generators. The automation of this task avoids many human mistakes. The toolkit has been designed based on a model driven engineering approach: the user builds a model of its simulation and the toolkit helps in distributing independent stochastic experiments. In this paper, the toolkit architecture is exposed, and two examples in life science research domains are detailed. The preliminary design of the DistMe toolkit was achieved when dealing with the distribution of a nuclear medicine application using the largest European computing grid: European Grid Initiative (EGI). Thanks to our alpha version of the software toolbox, the equivalent of 3 years of computing was achieved in a few days. Next, we present the second application in another domain to show the potential and genericity of the DistMe toolkit. A small experimental plan with 1024 distributed stochastic experiments was run on a local computing cluster to explore scenarios of an environmental application. For both applications, the proposed toolkit was able to automatically generate distribution scripts with independent pseudo‐random number streams, and it also automatically parameterized the simulation input files to follow an experimental design. The automatic generation of scripts and input files is achieved, thanks to model transformations using a model driven approach. Copyright © 2011 John Wiley & Sons, Ltd.

MOTIVATION: The use of DNA microarrays allows the monitoring of the extreme microbial diversity encountered in complex samples like environmental ones as well as that of their functional capacities. However, no probe design software currently available is adapted to easily design efficient and explorative probes for functional gene arrays. RESULTS: We present a new efficient functional microarray probe design algorithm called HiSpOD (High Specific Oligo Design). This uses individual nucleic sequences or consensus sequences produced by multiple alignments to design highly specific probes. Indeed, to bypass crucial problem of cross-hybridizations, probe specificity is assessed by similarity search against a large formatted database dedicated to microbial communities containing about 10 million coding sequences (CDS). For experimental validation, a microarray targeting genes encoding enzymes involved in chlorinated solvent biodegradation was built. The results obtained from a contaminated environmental sample proved the specificity and the sensitivity of probes designed with the HiSpOD program. AVAILABILITY: http://fc.isima.fr/~g2im/hispod/.

When studying complex environments where the composing microorganisms are unknown, exploratory tools able to tackle with the biological diversity have to be proposed. DNA microarrays can be a good answer if we are in a position to propose all the probes that could target a specific enzyme. In addition, in the context of new metabolic pathways discovery, it appears that a full backtranslation of oligopeptides is also a promising approach. In both contexts it is preferable to have all the complete nucleic sequences corresponding to an enzyme of interest. In this paper, we revisit existing bioinformatics applications, which bring partial reverse translation solutions, and we propose an algorithm based on input oligopeptide degeneracy able to efficiently compute a complete backtranslation of oligopeptides. This algorithm is precious for the discovery of new organisms and we show its performance on simulated and real biological datasets.

MOTIVATION: Microbial diversity is still largely unknown in most environments, such as soils. In order to get access to this microbial 'black-box', the development of powerful tools such as microarrays are necessary. However, the reliability of this approach relies on probe efficiency, in particular sensitivity, specificity and explorative power, in order to obtain an image of the microbial communities that is close to reality. RESULTS: We propose a new probe design algorithm that is able to select microarray probes targeting SSU rRNA at any phylogenetic level. This original approach, implemented in a program called 'PhylArray', designs a combination of degenerate and non-degenerate probes for each target taxon. Comparative experimental evaluations indicate that probes designed with PhylArray yield a higher sensitivity and specificity than those designed by conventional approaches. Applying the combined PhyArray/GoArrays strategy helps to optimize the hybridization performance of short probes. Finally, hybridizations with environmental targets have shown that the use of the PhylArray strategy can draw attention to even previously unknown bacteria.

MOTIVATION: The use of oligonucleotide microarray technology requires a very detailed attention to the design of specific probes spotted on the solid phase. These problems are far from being commonplace since they refer to complex physicochemical constraints. Whereas there are more and more publicly available programs for microarray oligonucleotide design, most of them use the same algorithm or criteria to design oligos, with only little variation. RESULTS: We show that classical approaches used in oligo design software may be inefficient under certain experimental conditions, especially when dealing with complex target mixtures. Indeed, our biological model is a human obligate parasite, the microsporidia Encephalitozoon cuniculi. Targets that are extracted from biological samples are composed of a mixture of pathogen transcripts and host cell transcripts. We propose a new approach to design oligonucleotides which combines good specificity with a potentially high sensitivity. This approach is original in the biological point of view as well as in the algorithmic point of view. We also present an experimental validation of this new strategy by comparing results obtained with standard oligos and with our composite oligos. A specific E.cuniculi microarray will overcome the difficulty to discriminate the parasite mRNAs from the host cell mRNAs demonstrating the power of the microarray approach to elucidate the lifestyle of an intracellular pathogen using mix mRNAs.

Design of real-time/concurrent systems requires formal approaches in order to facilitate verification and validation at each step. Methods based on formal logic have been previously suggested but they often work only in a specific domain and are generally only possible with specialized users. In an attempt to overcome these two restrictions, this paper proposes a method based on rewriting logic with user-friendly tools to increase software reliability. Different tools are proposed to support this approach: a graphical editor for the specification of the structure and the behaviour of objects, a prototype generator, and an inference engine for rule validation based on rewriting logic decidability.