Abstract: Floating-point numbers use a finite number of digits to represent infinite real numbers for computation,so floating-point computation is inherently inaccurate,which can be measured by the maximum error.The traditional floating-point maximum error detection algorithm uses serial computing thinking combined with classical search algorithm.When the number of sampling points is small,it is easy to treat the local maximum as the global maximum,thus omitting the maximum error value.If the number of sampling points is increased on a large scale,the time of the detection program will be greatly increased and the perfor-mance will be reduced.In this paper,the parallel computing mode is used to exponentially increase the number of sampling points,and the floating-point dynamic sampling strategy is used to near the error hot spot in combination with the principle of synchronization and locality,which greatly improves the accuracy of the detection results.This method can maximize the computing power of parallel computing,which can not only improves the detection accuracy of the maximum error of floating-point number calculation,but also reduces the execution time of the detection program and improves the performance,and the acceleration ratio can reach 1 136.3.The maximum error value detected is better than the current mainstream detection tools,which provides a new detection method for measuring the floating-point number calculation index.

Key words: Floating point arithmetic, Parallel optimization, Interval sampling, Error detection, Sunway heterogeneous architecture