Accelerating the tuning process for optimizing DNN operators by ROFT model.

ABSTRACT: Deep neural networks (DNNs) are computationally intensive and optimized in different ways. Some compiler optimizations for DNNs could achieve performance almost the same as, or even better than, manual optimizations. However, the former mechanisms usually require an unbearably long optimization time in the tuning process. In this paper, we propose a new method that accelerates the tuning process significantly without performance penalties. In particular, we use a Roofline-like cost model, namely ROFT (Roofline for Fast AutoTune), to evaluate the performance of schedules. The ROFT model can be easily implemented on different microarchitectures, e.g., NVidia GPUs and Huawei Ascend NPUs. Based on the cost model, we implement a flexible two-stage search algorithm, which significantly improves

SUBMITTER: He Z 

PROVIDER: S-EPMC12534627 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature