Large Scale In Silico Screening on Grid Infrastructures

Large-scale grid infrastructures for in silico drug discovery open opportunities of particular interest to neglected and emerging diseases. In 2005 and 2006, we have been able to deploy large scale in silico docking within the framework of the WISDOM initiative against Malaria and Avian Flu requiring about 105 years of CPU on the EGEE, Auvergrid and TWGrid infrastructures. These achievements demonstrated the relevance of large-scale grid infrastructures for the virtual screening by molecular docking. This also allowed evaluating the performances of the grid infrastructures and to identify specific issues raised by large-scale deployment.Comment: 14 pages, 2 figures, 2 tables, The Third International Life Science Grid Workshop, LSGrid 2006, Yokohama, Japan, 13-14 october 2006, to appear in the proceeding

Virtual Screening on Large Scale Grids

PCSV, article in press in Parallel ComputingLarge scale grids for in silico drug discovery open opportunities of particular interest to neglected and emerging diseases. In 2005 and 2006, we have been able to deploy large scale virtual docking within the framework of the WISDOM initiative against malaria and avian influenza requiring about 100 years of CPU on the EGEE, Auvergrid and TWGrid infrastructures. These achievements demonstrated the relevance of large scale grids for the virtual screening by molecular docking. This also allowed evaluating the performances of the grid infrastructures and to identify specific issues raised by large scale deployment

Grid enabled virtual screening against malaria

34 pages, 5 figures, 3 tables, to appear in Journal of Grid Computing - PCSV, à paraître dans Journal of Grid ComputingWISDOM is an international initiative to enable a virtual screening pipeline on a grid infrastructure. Its first attempt was to deploy large scale in silico docking on a public grid infrastructure. Protein-ligand docking is about computing the binding energy of a protein target to a library of potential drugs using a scoring algorithm. Previous deployments were either limited to one cluster, to grids of clusters in the tightly protected environment of a pharmaceutical laboratory or to pervasive grids. The first large scale docking experiment ran on the EGEE grid production service from 11 July 2005 to 19 August 2005 against targets relevant to research on malaria and saw over 41 million compounds docked for the equivalent of 80 years of CPU time. Up to 1,700 computers were simultaneously used in 15 countries around the world. Issues related to the deployment and the monitoring of the in silico docking experiment as well as experience with grid operation and services are reported in the paper. The main problem encountered for such a large scale deployment was the grid infrastructure stability. Although the overall success rate was above 80%, a lot of monitoring and supervision was still required at the application level to resubmit the jobs that failed. But the experiment demonstrated how grid infrastructures have a tremendous capacity to mobilize very large CPU resources for well targeted goals during a significant period of time. This success leads to a second computing challenge targeting Avian Flu neuraminidase N1

Scheduling with Storage Constraints

International audienceWe are interested in this paper to study scheduling problems in systems where many users compete to perform their respective jobs on shared parallel resources. Each user has specific needs or wishes for computing his/her jobs expressed as a function to optimize (among maximum completion time, sum of completion times and sum of weighted completion times). Such problems have been mainly studied through Game Theory. In this work, we focus on solving the problem by optimizing simultaneously each user's objective function independently using classical combinatorial optimization techniques. Some results have already been proposed for two users on a single computing resource. However, no generic combinatorial method is known for many objectives. The analysis proposed in this paper concerns an arbitrarily fixed number of users and is not restricted to a single resource. We first derive inapproximability bounds; then we analyze several greedy heuristics whose approximation ratios are close to these bounds. However, they remain high since they are linear in the number of users. We provide a deeper analysis which shows that a slightly modified version of the algorithm is a constant approximation of a Pareto-optimal solution

Analysis of the ATLAS Rome production experience on the LHC computing grid

The Large Hadron Collider at CERN will start data acquisition in 2007. The ATLAS (A Toroidal LHC ApparatuS) experiment is preparing for the data handling and analysis via a series of Data Challenges and production exercises to validate its computing model and to provide useful samples of data for detector and physics studies. The last Data Challenge, begun in June 2004 and ended in early 2005, was the first performed completely in a Grid environment. Immediately afterwards, a new production activity was necessary in order to provide the event samples for the ATLAS physics workshop, taking place in June 2005 in Rome. This exercise offered a unique opportunity to estimate the reached improvements and to continue the validation of the computing model. In this paper we discuss the experience of the "Rome production" on the LHC Computing Grid infrastructure, describing the achievements, the improvements with respect to the previous Data Challenge and the problems observed, together with the lessons learned and future plans