Grid Computing in High Energy Physics

Abstract

Over the next two decades, major high energy physics (HEP) experiments, particularly at the Large Hadron Collider, will face unprecedented challenges to achieving their scientific potential. These challenges arise primarily from the rapidly increasing size and complexity of HEP datasets that will be collected and the enormous computational, storage and networking resources that will be deployed by global collaborations in order to process, distribute and analyze them.Coupling such vast information technology resources to globally distributed collaborations of several thousand physicists requires extremely capable computing infrastructures supporting several key areas: (1) computing (providing sufficient computational and storage resources for all processing, simulation and analysis tasks undertaken by the collaborations); (2) networking (deploying high speed networks to transport data quickly between institutions around the world); (3) software (supporting simple and transparent access to data and software resources, regardless of location); (4) collaboration (providing tools that allow members full and fair access to all collaboration resources and enable distributed teams to work effectively, irrespective of location); and (5) education, training and outreach (providing resources and mechanisms for training students and for communicating important information to the public).It is believed that computing infrastructures based on Data Grids and optical networks can meet these challenges and can offer data intensive enterprises in high energy physics and elsewhere a comprehensive, scalable framework for collaboration and resource sharing. A number of Data Grid projects have been underway since 1999. Interestingly, the most exciting and far ranging of these projects are led by collaborations of high energy physicists, computer scientists and scientists from other disciplines in support of experiments with massive, near‐term data needs. I review progress in this important area, outlining new research directions and practical experiences in deploying national and international scale Data Grids.