Tree-Based Overlay Networks for Scalable Applications

Abstract

The increasing availability of high-performance computing systems with thousands, tens of thousands, and even hundreds of thousands of computational nodes is driving the demand for programming models and infrastructures that allow effective use of such large-scale environments. Tree-based Overlay Networks (TBŌNs) have proven to provide such a model for distributed tools like performance profilers, parallel debuggers, system monitors and system administration tools. We demonstrate that the extensibility and flexibility of the TBŌN distributed computing model, along with its performance characteristics, make it surprisingly general, particularly for applications outside the tool domain. We describe many interesting applications and commonly-used algorithms for which TBŌNs are well-suited and provide a new (non-tool) case study, a distributed implementation of the mean-shift algorithm commonly used in computer vision to delineate arbitrarily shaped clusters in complex, multi-modal feature spaces. 1