Add to Chrome
Add to Firefox
Add to EdgeK. Yip
D0 Data Handling Operational Experience
Jun 19, 2003
We report on the production experience of the D0 experiment at the Fermilab Tevatron, using the SAM data handling system with a variety of computing hardware configurations, batch systems, and mass storage strategies. We have stored more than 300 TB of data in the Fermilab Enstore mass storage system. We deliver data through this system at an average rate of more than 2 TB/day to analysis programs, with a substantial multiplication factor in the consumed data through intelligent cache management. We handle more than 1.7 Million files in this system and provide data delivery to user jobs at Fermilab on four types of systems: a reconstruction farm, a large SMP system, a Linux batch cluster, and a Linux desktop cluster. In addition, we import simulation data generated at 6 sites worldwide, and deliver data to jobs at many more sites. We describe the scope of the data handling deployment worldwide, the operational experience with this system, and the feedback of that experience.
* Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 6 pages. PSN MOKT002
Spatial Aggregation: Theory and Applications
Aug 01, 1996
Visual thinking plays an important role in scientific reasoning. Based on the research in automating diverse reasoning tasks about dynamical systems, nonlinear controllers, kinematic mechanisms, and fluid motion, we have identified a style of visual thinking, imagistic reasoning. Imagistic reasoning organizes computations around image-like, analogue representations so that perceptual and symbolic operations can be brought to bear to infer structure and behavior. Programs incorporating imagistic reasoning have been shown to perform at an expert level in domains that defy current analytic or numerical methods. We have developed a computational paradigm, spatial aggregation, to unify the description of a class of imagistic problem solvers. A program written in this paradigm has the following properties. It takes a continuous field and optional objective functions as input, and produces high-level descriptions of structure, behavior, or control actions. It computes a multi-layer of intermediate representations, called spatial aggregates, by forming equivalence classes and adjacency relations. It employs a small set of generic operators such as aggregation, classification, and localization to perform bidirectional mapping between the information-rich field and successively more abstract spatial aggregates. It uses a data structure, the neighborhood graph, as a common interface to modularize computations. To illustrate our theory, we describe the computational structure of three implemented problem solvers -- KAM, MAPS, and HIPAIR --- in terms of the spatial aggregation generic operators by mixing and matching a library of commonly used routines.
* See http://www.jair.org/ for any accompanying files