The loss of precision in floating-point programs is necessary to detect the loss of precision in floating-point programs because of the computational errors in floating-point operations, which can lead to the loss of precision in floating-point programs and affect the reliability of the upper-level software systems that call them. The inherent storage form of floating-point data makes it impossible to represent all real numbers fully, and the rounding error and error accumulation in floating-point operations make it difficult to detect the precision loss in floating-point programs. In this paper, we propose a dynamic analysis method to detect the precision loss of floating-point programs. First, we analyze the organization of the source code with the front-end Clang tool of LLVM to locate the floating-point data declarations and floating-point operations. Then we run the floating-point operations simultaneously with the higher precision MPFR operations with the help of the Dyninst staking framework to detect the precision loss at the statement level and record the precision loss of the floating-point. The precision loss of each step of the program is recorded. The precision loss change graph is generated to locate the floating-point precision loss. By testing the commonly used functions in the GSL library and some test cases in FPPench, the relative error of the method in this paper is improved by 43% on average compared with the randomly generated input data method to detect GSL functions. The relative error is improved by 1.8 bits on average compared with the Herbgrind tool for testing against FPPench.
KEYWORDS: Mathematical optimization, Algorithm development, Computer hardware, Logic, Internet of things, Digital signal processing, Computation time, Analog to digital converters, Design and modelling, Computer simulations
With the rapid development of the domestic Internet of Things, my country's self-designed SM2 public key cryptographic algorithm has been widely used in the integration of the Internet of Things and blockchain data of blockchain chips. However, the current SM2 public key cryptography algorithm has the problems of low calculation speed and excessive consumption of hardware resources. In view of the above problems, this article applies the Kogge-Stone adder method to solve the problem of low calculation speed; based on the characteristics of the AVR architecture, a method is proposed to optimize the inner loop operation of the Montgomery modular multiplication to reduce the consumption of hardware resources. Experiments show that the improved performance has a 26.2% performance improvement compared to before the improvement. The computing efficiency of the large integer modular multiplication operation is effectively improved, the resource consumption of hardware implementation is reduced, the balance between computing time and hardware overhead is better achieved, widely used in blockchain chips.
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