Elliptic curve multi-scalar point multiplication calculation optimization method and optimization device

Abstract

The invention discloses an elliptic curve multi-scalar point multiplication calculation optimization method and optimization device. Comprising the following steps: designing a bucket matrix to cache an intermediate variable point in a main calculation process, and avoiding the output of a Pippenger intermediate quantity; continuously running and calculating in a running water manner until all the final calculations are finished; then performing transverse reduction and longitudinal reduction, and reducing the total number of calculation times of serial calculation to one from thousands of times. Therefore, most overhead of serial-to-parallel conversion, synchronous locking and the like is eliminated, the continuous working time of an assembly line is effectively prolonged, and therefore the overall performance is improved.

Description

technical field [0001] The present application relates to the technical field of cryptography, in particular to an elliptic curve multi-scalar point product calculation optimization method and an optimization device. Background technique [0002] In related technologies, such as figure 1 As shown, it is the Pippenger algorithm, which is the algorithm used for one round of calculation in the MSM module of PipeZK. In PipeZK, a total of such calculations need to be performed times to generate points, and finally put this Points are again used as input for a calculation to obtain the final result. Among them, N is the total amount of input data, and M is the total amount of input data in a single round. The former is generally 10 6 In terms of magnitude, the latter often takes values ​​such as 1000 and 1024. [0003] exist figure 1 , for each G i , its buckets (Buckets, used to temporarily store the results of a point) have different storage contents, so different G i...

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Problems solved by technology

[0005] 1) Each round of calculation requires vertical reduction, and this calculation process can hardly be parallelized, resulting in performance loss

[0006] 2) Each round of calculation requires horizontal reduction, and this calculation process can hardly be parallelized, resulting in performance loss

[0007] 3) Each round of calculation needs to be written back to the memory. This process will bring additional logic control and bring certain difficulties in implementation.

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