AES parallel implementation method based on OpenCL

Abstract

The invention discloses an AES parallel implementation method based on OpenCL. According to the scheme, when an AES performs parallel running on an AMD GPU based on OpenCL, optimum performance can be achieved. The method includes the following steps that first, the number of plaintext / ciphertext and the number of round key groups are determined, and plaintext / ciphertext and round key data are prepared; second, an executing mode of the AES is determined, and preparation is made for writing of a KiReadyThread; third, the KiReadyThread is written; fourth, a master OpenCL program file is written, and preparation is made for execution of an OpenCL program; fifth, program running parameters are set, memory space is allocated, and the program runs; sixth, the encryption and decryption result is acquired, and resources are released. According to the method, in parallel running process of the AES, data are reasonably allocated in a memory and parallel granularity is reasonably selected so that running performance can be improved, and the method can be used for fast encryption and decryption or password cracking machines.

Description

technical field [0001] The invention relates to the technical field of cryptographic algorithms, in particular to a fast implementation method of AES on an OpenCL-based AMD GPU. Background technique [0002] GPU was originally designed specifically for processing graphics and image data, so it has a highly parallel structure. Today, after more than ten years of development, the parallel computing capability of GPU has far surpassed that of CPU, and using GPU to accelerate various calculations in parallel has become a research hotspot today. In terms of information security, an important application of GPU is to realize fast encryption and decryption through parallelization. [0003] In fact, there are currently four parallel implementation mechanisms for encryption and decryption parallelization: CPU, GPU, FPGA, and OPENCL, each of which has its own advantages and disadvantages. Although the performance of a single core of a CPU is higher than that of a GPU, it does not ha...

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Problems solved by technology

Although the performance of a single core of a CPU is higher than that of a GPU, it does not have a highly parallel structure, and its parallel capability is not as good as that of a GPU. Moreover, due to structural limitations, it is more difficult to expand the parallel scale than FPGA; although the parallel scale expansion of FPGA is easier than both CPU and GPU However, once the hardware is upgraded, a large number of codes need to be changed, which is not conducive to development and maintenance; GPU is also limited by the structure, parallel scale expansion is more difficult than FPGA, and the computing power of a single core is not as good as that of CPU. Data exchange will cause a lot of delay, and this kind of I / O consumption has become the main bottleneck of GPU parallel performance; OpenCL is specially designed for heterogeneous platforms, and can comprehensively utilize CPU, GPU and other types of processors to provide Parallel computing, and thus growing in favor

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