On bottleneck analysis in stochastic stream processing

Abstract

Past improvements in clock frequencies have traditionally been obtained through technology scaling, but most recent technology nodes do not offer such benefits. Instead, parallelism has emerged as the key driver of chip-performance growth. Unfortunately, efficient simultaneous use of on-chip resources is hampered by sequential dependencies, as illustrated by Amdahl's law. Quantifying achievable parallelism in terms of provable mathematical results can help prevent futile programming efforts and guide innovation in computer architecture toward the most significant challenges. To complement Amdahl's law, we focus on stream processing and quantify performance losses due to stochastic runtimes. Using spectral theory of random matrices, we derive new analytical results and validate them by numerical simulations. These results allow us to explore unique benefits of stochasticity and show how and when they outweigh the costs for software streams.