Accelerating Lattice Quantum Chromodynamics Simulations with Value Prediction

Communication latency problems are universal and have become a major performance bottleneck as we scale in big data infrastructure and many-core architectures. Specifically, research institutes around the world have built specialized supercomputers with powerful computation units in order to accelerate scientific computation. However, the problem often comes from the communication side instead of the computation side. In this paper we first demonstrate the severity of communication latency problems. Then we use Lattice Quantum Chromo Dynamic (LQCD) simulations as a case study to show how value prediction techniques can reduce the communication overheads, thus leading to higher performance without adding more expensive hardware. In detail, we first implement a software value predictor on LQCD simulations: our results indicate that 22.15% of the predictions result in performance gain and only 2.65% of the predictions lead to rollbacks. Next we explore the hardware value predictor design, which results in a 20-fold reduction of the prediction latency. In addition, based on the observation that the full range of floating point accuracy may not be always needed, we propose and implement an initial design of the tolerance value predictor: as the tolerance range increases, the prediction accuracy also increases dramatically.