PaViz: A Power-Adaptive Framework for Optimizing Visualization Performance

Abstract

Power consumption is widely regarded as one of the biggest challenges to reaching the next generation of high-performance computing. One strategy for achieving an exaflop given limited power is hardware overprovisioning. In this model, the theoretical peak power usage of the system is greater than the maximum allowable power usage, and a central manager keeps the aggregate power usage at the maximum by enforcing power caps on each node in the system. For this model to be effective, the central manager must be able to make informed trade-offs between power usage and performance. With this work, we introduce PaViz, a software framework designed to optimize the distribution of power for visualization algorithms, which have different characteristics than simulation codes. In this study, we focus specifically on rendering. Our strategy uses a performance model, where nodes predicted to have a small amount of work are allocated less power, and nodes predicted to have a large amount of work are allocated more power. This approach increases the likelihood of all nodes finishing at the same time, which is optimal for power efficiency. At best, our adaptive strategy achieves up to 33% speedup over the traditional strategy, while using the same total power.