Abstract: Floating-point arithmetic is a typical numerical solution model for high-performance computing. Mixed-precision optimization enhances performance and reduces energy consumption by decreas- ing the precision of floating-point variables in programs. However, existing automatic mixed-precision optimization techniques are limited by low robustness, meaning that the optimized programs fail to meet the result accuracy constraints for given inputs. To address this issue, a method for improving the robustness of mixed-precision optimization based on floating-point error analysis is proposed. Firstly, inputs that can trigger imprecise calculations in the program are identified through floating-point error analysis. Then, based on these error-triggering inputs, the precision configurations are evaluated to guide the search for highly robust mixed-precision configurations. Experimental results show that for typical floating-point applications, this method can improve the robustness of mixed-precision optimization by an average of 62%.

Key words: floating-point arithmetic, mixed precision, robustness, error analysis