High throughput grid computing with an IBM Blue Gene/L

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 systempsilas 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.