Graph Partitioning for Reconfigurable Topology

Optical circuit switches have recently been proposed as a low-cost, low-power and high-bandwidth alternative to electronic switches for the design of high-performance compute clusters. An added advantage of these switches is that they allow for a reconfiguration of the network topology to suit the requirements of the application. To realize the full potential of a high-performance computing system with a reconfigurable interconnect, there is a need to design algorithms for computing a topology that will allow for a high-throughput load distribution, while simultaneously partitioning the computational task graph of the application for the computed topology. In this paper, we propose a new framework that exploits such reconfigurable interconnects to achieve these interdependent goals, i.e., to iteratively co-optimize the network topology configuration, application partitioning and network flow routing to maximize throughput for a given application. We also present a novel way of computing a high-throughput initial topology based on the structural properties of the application to seed our co-optimizing framework. We show the value of our approach on synthetic graphs that emulate the key characteristics of a class of stream computing applications that require high throughput. Our experiments show that the proposed technique is fast and computes high-quality partitions of such graphs for a broad range of hardware parameters that varies the bottleneck from computation to communication.