Improving the Performances of Machine Learning Algorithms by Using Assembly Language

Programmers will always have a dilemma: they must make a choice between Performance, Flexibility or Fast Development. In this work, an experimental study is presented regarding the advantages of using Assembly language compared to C and Rust languages from runtime, flexibility and development effort in the cases of two machine learning algorithms, linear regression and k-nearest neighbors, using public datasets. Experiments have shown that for simple ML algorithms, assembly language is an alternative that must be considered. Conclusions as well as future research directions are also included.