Mixed-precision optimization is a floating-point computation optimization method that im-proves program performance by using a combination of floating-point variables and basic functions with different precisions.Compared to floating-point variables,basic functions have more variations in precision,which greatly expands the mixed-precision optimization space,but also increases the diffi-culty of searching for optimal solutions.In response to the above problem,a mixed-precision optimiza-tion method based on multi-precision basic functions is proposed,which achieves fast mixed-precision optimization at the function level.Firstly,multi-precision basic functions for a specified interval are au-tomatically generated through interval reduction and polynomial approximation by this method.Sec-ondly,based on the Pareto optimal strategy,a fast search for the optimal mixed-precision solution is implemented for expressions containing basic functions.Experimental results show that,under the con-dition that the error threshold is twice the average error of the double-precision version,the optimal mixed-precision solutions for all cases with errors not exceeding the error threshold can be quickly found by this method.The average performance improvement of this method compared to the double precision version is 1.16.
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