Feedback-Driven Performance and Precision Tuning for Automatic Fixed Point Exploitation

Abstract

Precision tuning is an emerging class of techniques that leverage the trade-off between accuracy and performance in a wide range of numerical applications. We employ TAFFO, a compiler-based state-of-the-art framework that relies on fixed point representations to perform precision tuning. It converts floating-point computations into a fixed point version with comparable semantics, in order to obtain performance improvements. Usually, the process of fixed point type selection aims at the minimization of the round-off error introduced by the precision reduction. However, this approach introduces a large number of type cast operations, generating an overhead that may overcome the performance improvements of the conversion to fixed point formats. We propose a control loop architecture that exploits the static analyses provided by TAFFO to reduce the number of type cast operations while keeping the error under a given threshold. We evaluate our approach on three benchmarks of the AXBENCH suite, and we show that in all cases we are able to achieve performance improvements while keeping the introduced numerical error below the given tolerance threshold.