An Empirical Study of Computation-Intensive Loops for Identifying and Classifying Loop Kernels
Masatomo Hashimoto, Masaaki Terai, Toshiyuki Maeda, Kazuo Minami · 2017
The process of performance tuning is time consuming and costly even if it is carried out automatically. It is crucial to learn from the experience of experts. Our long-term goal is to construct a database of facts extracted from specific performance tuning histories of computation-intensive applications such that we can search the database for promising optimization patterns that fit a given kernel.