AES Encryption Implementation on CUDA GPU and Its Analysis

Iwai, Keisuke; Kurokawa, Takakazu; Nisikawa, Naoki

doi:10.1109/ic-nc.2010.49

Cited by 42 publications

(15 citation statements)

References 4 publications

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“…A quantitative cost of reordering was not shown. This implementation looks similar to our hand optimized implementation and it refers our previous work [24]. Therefore, generated AES program by HiCrypt will have the same quality as this work by considering to the cost of reordering.…”

Section: Performance Resultsmentioning

confidence: 75%

HiCrypt: A Specialized Translator for Symmetric Block Cipher and GPGPU

Iwai

Nishikawa

Kurokawa

2013

IEICE Trans. Inf. & Syst.

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SUMMARYMany-core computer systems with GPUs are coming into mainstream use from high-end computing, including supercomputers, to embedded processors. Consequently, the implementation of cryptographic methods on GPGPU is also becoming popular because of such systems' performance. However, many factors affect the performance of GPUs. To cope with this problem, we developed a new translator, HiCrypt, which can generate an optimized GPGPU program written in both of CUDA and OpenCL from a cipher program written in standard C language with directives. Users must annotate only variables and an encoding/decoding function, which are characteristics of cipher programs, with directives. To evaluate the translator, five representative cipher programs are translated into CUDA and OpenCL programs by the translator. Generated programs perform high throughput almost identical to hand optimized programs for all five cipher programs. HiCrypt will contribute to development and evaluate of new and various symmetric block ciphers using GPGPU.

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Section: Performance Resultsmentioning

confidence: 75%

HiCrypt: A Specialized Translator for Symmetric Block Cipher and GPGPU

Iwai

Nishikawa

Kurokawa

2013

IEICE Trans. Inf. & Syst.

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“…An earlier study [10] revealed that the AES implementation on CUDA can produce higher throughput when the extended key and the substitution tables are stored in the shared memory compared with a case in which these data are stored in constant memory. Therefore, for our implementation, we also stored substitution tables in shared memory.…”

Section: Memory Allocationmentioning

confidence: 99%

“…Iwai et al implemented AES on CUDA with different granularity and various memory allocation styles [10]. For the AES encryption module, they contrived what bytes should be mapped to each thread, granularity, as one factor to increase its performance.…”

Section: Gpgpu Implementation Of Aesmentioning

confidence: 99%

“…This fact makes it difficult to detect what parameters can extract the GPU's performance. In our previous work to gain higher AES performance using GPGPU in various ways, we obtained the following two technical viewpoints: (1) 16 Bytes/Thread is the best granularity (2) extended key and a substitution table stored in shared memory is the best memory allocation style [10].…”

Section: Introductionmentioning

confidence: 99%

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High-Performance Symmetric Block Ciphers on Multicore CPU and GPUs

Nishikawa

Iwai

Kurokawa

2012

IJNC

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As the data protection with encryption becomes important day by day, the encryption processing using General Purpose computation on a Graphic Processing Unit (GPGPU) has been noticed as one of the methods to realize high-speed data protection technology. GPUs have evolved in recent years into powerful parallel computing devices, with a high cost-performance ratio. However, many factors affect GPU performance. In earlier work to gain higher AES performance using GPGPU in various ways, we obtained the following two technical viewpoints: (1) 16 Bytes/Thread is the best granularity (2) Extended key and substitution table stored in shared memory and plaintext stored in register are the best memory allocation style.However, AES is not the only cipher algorithm widely used in the real world. For this reason, this study was undertaken to test the hypothesis that these two findings are applicable to implementation of other symmetric block ciphers on two generation of GPU. In this study, we targeted five 128-bit symmetric block ciphers, AES, Camellia, CIPHERUNICORN-A, Hierocrypt-3, and SC2000, from an e-government recommended ciphers list by the CRYPTography Research and Evaluation Committees (CRYPTREC) in Japan. We evaluated the performance of these five symmetric block ciphers on the machine including a 4-core CPU and each GPU using three method: (A) throughput without data transfer, (B) throughput with data transfer and overlapping encryption processing on GPU, (C) throughput with data transfer and non-overlapping encryption processing on GPU. Results demonstrate that the throughput of implementation of SC2000 in method (A) on Tesla C2050 achieved extremely high 73.4 Gbps. Additionally, the throughput obtained using methods (B) and (C) deteriorated to 33.4 Gbps and 18.3 Gbps, respectively. Method (B) showed effective throughput with an approximately 4.7 times higher speed compared to that obtained when using 8 threads on a 4-core CPU.

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“…By using CUDA, Manavski [21] implemented AES on an NVIDIA GPU G80, achieving a speedup as high as 5.9 times, as compared to the fastest CPU at the time. Iwai et al achieved approximately a throughput of 35Gbps (Gigabits per second) on a NVIDIA Geforce GTX285 [12]. Li et al [17] achieved the highest performance, around 60Gbps throughput on a NVIDIA Tesla C2050 GPU, which runs up to 50 times faster than an Intel Core i7-920.…”

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confidence: 99%

Power Analysis Attack of an AES GPU Implementation

et al. 2018

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In the past, Graphics Processing Units (GPUs) were mainly used for graphics rendering. In the past 10 years, they have been redesigned and are used to accelerate a wide range of applications, including deep neural networks, image reconstruction, and cryptographic algorithms. Despite being the accelerator of choice in a number of important application domains, today's GPUs receive little attention on their security, especially their vulnerability to realistic and practical threats, such as sidechannel attacks. In this work, we present our study of side-channel vulnerability targeting a general purpose GPU. We propose and implement a side-channel power analysis methodology to extract all the last round key bytes of an AES (Advanced Encryption Standard) implementation on an NVIDIA TESLA GPU. We first analyze the challenges of capturing GPU power traces due to the degree of concurrency and underlying architectural features of a GPU, and propose techniques to overcome these challenges. We then construct an appropriate power model for the GPU. We describe effective methods to process the GPU power traces and launch a correlation power attack (CPA) on the processed data. We carefully consider the scalability of the attack with increasing degrees of parallelism, a key challenge on the GPU. Both our empirical and theoretical results show that parallel computing hardware systems such as a GPU are vulnerable to power analysis side-channel attacks, and need to be hardened against such threats.

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AES Encryption Implementation on CUDA GPU and Its Analysis

Cited by 42 publications

References 4 publications

HiCrypt: A Specialized Translator for Symmetric Block Cipher and GPGPU

HiCrypt: A Specialized Translator for Symmetric Block Cipher and GPGPU

High-Performance Symmetric Block Ciphers on Multicore CPU and GPUs

Power Analysis Attack of an AES GPU Implementation

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