An Evaluation of POP Performance for Tuning Numerical Programs in Floating-Point Arithmetic

Most of today's numerical computations are performed using floating-point data operations for representing real numbers. The precision of the related data types should be adapted in order to guarantee a desired overall rounding error and to strengthen the performance of programs. This adaptation lies to the precision tuning technique, which consists of finding the least floating-point formats enabling a program to compute some results with an accuracy requirement. Precision tuning is important for several applications from a wide variety of domains including embedded systems, Internet of Things (IoT), etc. In this work, we evaluate the performance of POP, our static analysis tool, in several manners. First, we compare two ways of optimizing programs in POP throughout the definition of different cost functions given to the solver. Second, we measure the runtime errors between the exact results given by an execution in multiple precision and the results of tuned programs by POP and show that the measured error is always less than the accuracy requirements given by the user for several examples. Third, we provide a detailed comparison of POP versus the prior state-of-the-art tool, Precimonious.