Comparing DSP Software Performance Prediction Models at Source Code Level — From Analytical to Statistical

摘要

Efficient performance prediction at source code level is essential in reducing the turnaround time of software development, particularly when the source code is subject to changes due to modification of problem specification. In this paper, we investigate and compare five performance prediction models from practical standpoint to determine the usefulness of these models. To verify the effectiveness of these models, we select a set of functions from PHY DSP Benchmark and TIC64 DSP processor for experiment. Comparing the predicted performance to the actual measured execution time, we observed that the relative prediction error generated from two of the five models are low and can thus be used for practical purposes.