Power and Performance Tradeoffs for Visualization Algorithms

One of the biggest challenges for leading-edge supercomputers is power usage. Looking forward, power is expected to become an increasingly limited resource, so it is critical to understand the runtime behaviors of applications in this constrained environment in order to use power wisely. Within this context, we explore the tradeoffs between power and performance specifically for visualization algorithms. With respect to execution behavior under a power limit, visualization algorithms differ from traditional HPC applications, like scientific simulations, because visualization is more data intensive. This data intensive characteristic lends itself to alternative strategies regarding power usage. In this study, we focus on a representative set of visualization algorithms, and explore their power and performance characteristics as a power bound is applied. The result is a study that identifies how future research efforts can exploit the execution characteristics of visualization applications in order to optimize performance under a power bound.