Flexible filters in stream programs

Abstract

The stream-processing model is a natural fit for multicore systems because it exposes the inherent locality and concurrency of a program and highlights its separable tasks for efficient parallel implementations. We present flexible filters, a load-balancing optimization technique for stream programs. Flexible filters utilize the programmability of the cores in order to improve the data-processing throughput of individual bottleneck tasks by "borrowing" resources from neighbors in the stream. Our technique is distributed and scalable because all runtime load-balancing decisions are based on point-to-point handshake signals exchanged between neighboring cores. Load balancing with flexible filters increases the system-level processing throughput of stream applications, particularly those with large dynamic variations in the computational load of their tasks. We empirically evaluate flexible filters in a homogeneous multicore environment over a suite of five real-word stream programs.