Multiobjective Optimization of the Variability of the High-Performance Linpack Solver

Variability in the execution time of computing tasks can cause load imbalance in high-performance computing (HPC) systems. When configuring system- and application-level parameters, engineers traditionally seek configurations that will maximize the mean computational throughput. In an HPC setting, however, high-throughput configurations that do not account for performance variability could result in poor load balancing. In order to determine the effects of performance variance on computationally expensive numerical simulations, the High-Performance LINPACK solver is optimized by using multiobjective optimization to maximize the mean and minimize the standard deviation of the computational throughput on the High-Performance LINPACK benchmark. We show that specific configurations of the solver can be used to control for variability at a small sacrifice in mean throughput. We also identify configurations that result in a relatively high mean throughput, but also result in a high throughput variability.