High-Throughput Computing on High-Performance Platforms: A Case Study

The computing systems used by LHC experiments has historically consisted of the federation of hundreds to thousands of distributed resources, ranging from small to mid-size resource. In spite of the impressive scale of the existing distributed computing solutions, the federation of small to mid-size resources will be insufficient to meet projected future demands. This paper is a case study of how the ATLAS experiment has embraced Titan---a DOE leadership facility in conjunction with traditional distributed high- throughput computing to reach sustained production scales of approximately 52M core-hours a years. The three main contributions of this paper are: (i) a critical evaluation of design and operational considerations to support the sustained, scalable and production usage of Titan; (ii) a preliminary characterization of a next generation executor for PanDA to support new workloads and advanced execution modes; and (iii) early lessons for how current and future experimental and observational systems can be integrated with production supercomputers and other platforms in a general and extensible manner

A clinical system for acquistion of aver-ced sincle-trial evokedpotentials

TEZ2234Tez (Yüksek Lisans) -- Çukurova Üniversitesi, Adana, 1996.Kaynakça (s. 63-65) var.ix, 65 s. ; 30 cm.

Understanding Lustre Internals

Lustre was initiated and funded, almost a decade ago, by the U.S. Department of Energy (DoE) Office of Science and National Nuclear Security Administration laboratories to address the need for an open source, highly-scalable, high-performance parallel filesystem on by then present and future supercomputing platforms. Throughout the last decade, it was deployed over numerous medium-to-large-scale supercomputing platforms and clusters, and it performed and met the expectations of the Lustre user community. As it stands at the time of writing this document, according to the Top500 list, 15 of the top 30 supercomputers in the world use Lustre filesystem. This report aims to present a streamlined overview on how Lustre works internally at reasonable details including relevant data structures, APIs, protocols and algorithms involved for Lustre version 1.6 source code base. More importantly, it tries to explain how various components interconnect with each other and function as a system. Portions of this report are based on discussions with Oak Ridge National Laboratory Lustre Center of Excellence team members and portions of it are based on our own understanding of how the code works. We, as the authors team bare all responsibilities for all errors and omissions in this document. We can only hope it helps current and future Lustre users and Lustre code developers as much as it helped us understanding the Lustre source code and its internal workings