TX: Algorithmic Energy Saving for Distributed Dense Matrix Factorizations

The pressing demands of improving energy efficiency for high performance scientific computing have motivated a large body of solutions using Dynamic Voltage and Frequency Scaling (DVFS) that strategically switch processors to low-power states, if the peak processor performance is unnecessary. Although OS level solutions have demonstrated the effectiveness of saving energy in a black-box fashion, for applications with variable execution patterns, the optimal energy efficiency can be blundered away due to defective prediction mechanism and untapped load imbalance. In this paper, we propose TX, a library level race-tohalt DVFS scheduling approach that analyzes Task Dependency Set of each task in distributed Cholesky/LU/QR factorizations to achieve substantial energy savings OS level solutions cannot fulfill. Partially giving up the generality of OS level solutions per requiring library level source modification, TX leverages algorithmic characteristics of the applications to gain greater energy savings. Experimental results on two clusters indicate that TX can save up to 17.8% more energy than state-of-the-art OS level solutions with negligible 3.5% on average performance loss.