High-Throughput Computing on High-Performance Platforms: A Case Study
This addresses the scalability problem for high-energy physics experiments like ATLAS, though it is an incremental integration of existing platforms.
The paper tackles the challenge of meeting future computing demands for LHC experiments by integrating the Titan supercomputer with traditional distributed high-throughput computing, achieving sustained production scales of approximately 52 million core-hours per year.
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.