Modeling the scalability of acrylic stream programs

Abstract

Despite the fact that the streaming application domain is becoming increasingly widespread, few studies have focused specifically on the performance characteristics of stream programs. We introduce two models by which the scalability of stream programs can be predicted to some degree of accuracy. This is accomplished by testing a series of stream benchmarks on our numerical representations of the two models. These numbers are then compared to actual speedups obtained by running the benchmarks through the Raw machine and a Magic network. Using the metrics, we show that stateless acyclic stream programs benefit considerably from data, parallelization. In particular, programs with low communication datarates experience up to a tenfold speedup increase when parallelized to a reasonable margin. Those with high communication data rates also experience approximately a twofold speedup. We find that the model that takes synchronization communication overhead into account, in addition to a cost proportional to the communication rate of the stream, provides the highest predictive accuracy.