The ATLAS detector at CERN's Large Hadron Collider presents data handling requirements on an unprecedented scale. From 2008 on the ATLAS distributed data management system, Don Quijote2 (DQ2), must manage tens of ...
The ATLAS detector at CERN's Large Hadron Collider presents data handling requirements on an unprecedented scale. From 2008 on the ATLAS distributed data management system, Don Quijote2 (DQ2), must manage tens of petabytes of experiment data per year, distributed globally via the LCG, OSG and NDGF computing grids, now commonly known as the WLCG. Since its inception in 2005 DQ2 has continuously managed all experiment data for the ATLAS collaboration, which now comprises over 3000 scientists participating from more than 150 universities and laboratories in 34 countries. Fulfilling its primary requirement of providing a highly distributed, fault-tolerant and scalable architecture DQ2 was successfully upgraded from managing data on a terabyte-scale to managing data on a petabyte-scale. We present improvements and enhancements to DQ2 based on the increasing demands for ATLAS data management. We describe performance issues, architectural changes and implementation decisions, the current state of deployment in test and production as well as anticipated future improvements. Test results presented here show that DQ2 is capable of handling data up to and beyond the requirements of full-scale data-taking.
The term Research software Engineer, or RSE, emerged a little over 10 years ago as a way to represent individuals working in the research community but focusing on software development. The term has been widely adopte...
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This paper describes the implementation of transmission-line matrix (TLM) method algorithms on a massively parallel computer (DECmpp 12000), the technique of distributed computing in the UNIX environment, and the comb...
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This paper describes the implementation of transmission-line matrix (TLM) method algorithms on a massively parallel computer (DECmpp 12000), the technique of distributed computing in the UNIX environment, and the combination of TLM analysis with Prony's method as well as with autoregressive moving average (ARMA) digital signal processing for electromagnetic field modelling. By combining these advanced computation techniques, typical electromagnetic field modelling of microwave structures by TLM analysis can be accelerated by a few orders of magnitude.
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