Energy and Performance Prediction of CUDA Applications using Dynamic Regression Models
Shajulin Benedict, R. S. Rejitha, Suja A. Alex · 2016
Many emerging supercomputers and future exa-scale computing machines require accelerator-based GPU computing architectures for boosting their computing performances. CUDA is one of the widely applied GPGPU parallel computing platform for those architectures owing to its better performance for certain scientific applications. However, the emerging rise in the development of CUDA applications from various scientific domains, such as, bioinformatics, HEP, and so forth, has urged the need for tools that identify optimal application parameters and the other GPGPU architecture metrics, including work group size, work item, memory utilization, and so forth. In fact, the tuning process might end up with several executions of various possible code variants.