Reducing Energy Consumption of Dense Linear Algebra Operations on Hybrid CPU-GPU Platforms - Alonso, P.; Dolz, M. F.; Igual, F. D.; Mayo, R.; Quintana-Orti, E. S. (2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications, 2012)
We investigate the balance between the time-to-solution and the energy consumption of a task-parallel execution of the Cholesky and LU factorizations on a hybrid platform, equipped with a multi-core processor and several GPUs. To improve energy efficiency, we incorporate two energy-saving techniques in the runtime in charge of scheduling the computations, to block idle threads and enable the transition to a more energy-friendly state of the general-purpose cores. Experiments on an Intel Xeon-based platform connected to an NVIDIA Tesla server report an average reduction of the energy consumption close to 9% (38% when only the consumption associated with the application is considered), for a minor increase in the execution time of the algorithm.