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Performance Enhancement and Evaluation of AES Cryptography using OpenCL on Embedded GPGPU
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 Title & Authors
Performance Enhancement and Evaluation of AES Cryptography using OpenCL on Embedded GPGPU
Lee, Minhak; Kang, Woochul;
 
 Abstract
Recently, an increasing number of embedded processors such as ARM Mali begin to support GPGPU programming frameworks, such as OpenCL. Thus, GPGPU technologies that have been used in PC and server environments are beginning to be applied to the embedded systems. However, many embedded systems have different architectural characteristics compare to traditional PCs and low-power consumption and real-time performance are also important performance metrics in these systems. In this paper, we implement a parallel AES cryptographic algorithm for a modern embedded GPU using OpenCL, a standard parallel computing framework, and compare performance against various baselines. Experimental results show that the parallel GPU AES implementation can reduce the response time by about 1/150 and the energy consumption by approximately 1/290 compare to OpenMP implementation when 1000KB input data is applied. Furthermore, an additional 100 % performance improvement of the parallel AES algorithm was achieved by exploiting the characteristics of embedded GPUs such as removing copying data between GPU and host memory. Our results also demonstrate that higher performance improvement can be achieved with larger size of input data.
 Keywords
embedded;GPGPU;OpenCL;cryptography;AES;low power;
 Language
Korean
 Cited by
1.
통합메모리를 이용한 임베디드 환경에서의 딥러닝 프레임워크 성능 개선과 평가,이민학;강우철;

정보과학회 컴퓨팅의 실제 논문지, 2017. vol.23. 7, pp.417-423 crossref(new window)
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