Characterizing energy efficiency of I/O intensive parallel applications on power-aware clusters

Energy efficiency and parallel I/O performance have become two critical measures in high performance computing (HPC). However, there is little empirical data that characterize the energy-performance behaviors of parallel I/O workload. In this paper, we present a methodology to profile the performance, energy, and energy efficiency of parallel I/O access patterns and report our findings on the impacting factors of parallel I/O energy efficiency. Our study shows that choosing the right buffer size can change the energy-performance efficiency by up to 30 times. High spatial and temporal spacing can also lead to significant improvement in energy-performance efficiency (about 2X). We observe CPU frequency has a more complex impact, depending on the IO operations, spatial and temporal, and memory buffer size. The presented methodology and findings are useful for evaluating the energy efficiency of I/O intensive applications and for providing a guideline to develop energy efficient parallel I/O technology.