Comparability Graph Coloring for Optimizing Utilization of Software-Managed Stream Register Files for Stream Processors

Abstract

The stream processors represent a promising alternative to traditional cache-based general-purpose processors in achieving high performance in stream applications (media and some scientific applications). In a stream programming model for stream processors, an application is decomposed into a sequence of kernels operating on streams of data. During the execution of a kernel on a stream processor, all streams accessed must be communicated through a nonbypassing software-managed on-chip memory, the SRF (Stream Register File). Optimizing utilization of the scarce on-chip memory is crucial for good performance. The key insight is that the interference graphs (IGs) formed by the streams in stream applications tend to be comparability graphs or decomposable into a set of comparability graphs. We present a compiler algorithm for finding optimal or near-optimal colorings, that is, SRF allocations in stream IGs, by computing a maximum spanning forest of the sub-IG formed by long live ranges, if necessary. Our experimental results validate the optimality and near-optimality of our algorithm by comparing it with an ILP solver, and show that our algorithm yields improved SRF utilization over the First-Fit bin-packing algorithm, the best in the literature.