Abstract
Conventional auto-tuning numerical software has the drawbacks of (1) fixed sampling points for the performance estimation; (2) inadequate adaptation to heterogeneous environments. To solve these drawbacks, we developed ABCLib_DRSSED, which is a parallel eigensolver with an auto-tuning facility. ABCLib_DRSSED has (1) functions based on the sampling points which are constructed with an end-user interface; (2) a load-balancer for the data to be distributed; (3) a new auto-tuning optimization timing called Before Execute-time Optimization (BEO). In our performance evaluation of the BEO, we obtained speedup factors from 10% to 90%, and 340% in the case of a failed estimation. In the evaluation of the load-balancer, the performance was 220% improved.
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