Akademik Veri Yönetim Sistemi
NEUROCOMPUTING, cilt.481, ss.102-120, 2022 (SCI-Expanded)
Loops' execution time and resource consumption are one of the interest points and vital issues in the field of appraising complex scientific or computational algorithms. This issue caused the proposal of Loop per-formance optimization techniques such as fusion. In the literature, loop fusion merges the loops by taking into account a set of properties associated with the loops or the system on which the resulting code will be executed. The number of these factors and their interactions on the one hand, and the high runtime of available comprehensive approaches, on the other hand, reveals the need for a new method that could be concerned for further progress in solving this NP-hard problem. For the first time, Deep Reinforcement Learning Loop Fusion (DRLLF) advanced to be an ideal solution for the challenge in this article. For the pro-posed framework, a particular matrix is configured as the inputs of a deep neural network based on the information of the problem, namely data dependencies, data reuse, loops' types, and computer system's register size. These randomly generated matrixes are used in the training phase by reinforcement learn-ing to get the imperative experience on predicting a profitable distribution over loops' various fusion orders. In the evaluations performed, the presented algorithm was able to achieve the same or better per-formance in terms of speedup rate, comparing with the methods under study, approximately averaged in 7.36 percent better results. The considerable improvement observed in the results, besides the low run time, proves the comprehensiveness and superiority of this approach. (c) 2022 Elsevier B.V. All rights reserved.