To CMP or not to CMP: analyzing packet classification on modern and traditional parallel architectures

Abstract

Packet classification is central to modern network functionality, yet satisfactory memory usage and performance remains elusive at the highest speeds. The recent emergence of low-cost, highly parallel architectures provides a promising platform on which to realize increased classification performance. We analyze two classic algorithms (ABV and HiCuts) in multiple parallel contexts. Our results show that performance depends strongly on many factors, including algorithm choice, hardware platform, and parallelization scheme. We find that there is no clear "best solution," but in the best cases hardware constraints are mitigated by the parallelization scheme and vice versa, yielding near-linear speedups as the degree of parallelization increases.