The University of Colorado (CU) and the National Center for Atmospheric Research (NCAR) have been deploying complimentary and federated resources supporting computational science in the Western United States since 2004. This activity has expanded to include other partners in the area, forming the basis for a broader Front Range Computing Consortium (FRCC). This paper describes the development of the Consortium's current architecture for federated high-performance resources, including a new 184 teraflop/s (TF) computational system at CU and prototype data-centric computing resources at NCAR. CU's new Dellbased computational plant is housed in a co-designed prefabricated data center facility that allowed the university to install a top-tier academic resource without major capital facility investments or renovations. We describe integration of features such as virtualization, dynamic configuration of high-throughput networks, and Grid and cloud technologies, into an architecture that supports collaboration among regional computational science participants.M. Oberg ( ) · M. Woitaszek · H.M. Tufo

Abstract-While much high-performance computing is performed using massively parallel MPI applications, many workflows execute jobs with a mix of processor counts. At the extreme end of the scale, some workloads consist of large quantities of single-processor jobs. These types of workflows lead to inefficient usage of massively parallel architectures such as the IBM Blue Gene/L (BG/L) because of allocation constraints forced by its unique system design. Recently, IBM introduced the ability to schedule individual processors on BG/L -a feature named High Throughput Computing (HTC) -creating an opportunity to exploit the system's power efficiency for other classes of computing.In this paper, we present a Grid-enabled interface supporting HTC on BG/L. This interface accepts single-processor tasks using Globus GRAM, aggregates HTC tasks into BG/L partitions, and requests partition execution using the underlying system scheduler. By separating HTC task aggregation from scheduling, we provide the ability for workflows constructed using standard Grid middleware to run both parallel and serial jobs on the BG/L. We examine the startup latency and performance of running large quantities of HTC jobs. Finally, we deploy Daymet, a component of a coupled climate model, on a BG/L system using our HTC interface.

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