Hash functions like SHA-1 or MD5 are one of the most important cryptographic primitives, especially in the field of information integrity. Considering the fact that increasing methods have been proposed to break these hash algorithms, a competition for a new family of hash functions was held by the US National Institute of Standards and Technology. Keccak was the winner and selected to be the next generation of hash function standard, named SHA-3. We aim to implement and optimize Batch mode based Keccak algorithms on NVIDIA GPU platform. Our work consider the case of processing multiple hash tasks at once and implement the case on CPU and GPU respectively. Our experimental results show that GPU performance is significantly higher than CPU is the case of processing large batches of small hash tasks.
, @cite_2 implements a high speed hash function Keccak (SHA3-512) using the integrated development environment CUDA for GPU is proposed. In addition, the safety level of Keccak is also discussed at the point of Pre-Image Resistance especially. In order to implement a high speed hash function for password cracking, the special program is also developed for passwords up to 71 characters. Moreover, the throughput of 2-time hash is also evaluated in their work.
@cite_2
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In an effort to provide security and data integrity, hashing algorithms have been designed to consume an input of any length to produce a fixed length output. KECCAK was selected by NIST to become the next Secure Hashing Algorithm (SHA-3) after nearly five years of competition. In addition to providing a sequential operating mode, there is also a tree mode that allows large input messages to be hashed in parallel. This work focuses on the exploration and analysis of the KECCAK tree hashing mode on a GPU platform. Based on the implementation, there are core features of the GPU that could be used to accelerate the time it takes to complete a hash due to the massively parallel architecture of the device. In addition to analyzing the speed of the algorithm, the underlying hardware is profiled to identify the bottlenecks that limited the hash speed. The results of this work show that tree hashing can hash data at rates of up to 3 GB s for the fixed size tree mode. On a 3.40 GHz CPU, this is the equivalent of 1.03 cycles per byte, more than six times faster than a sequential implementation for a very large input. For the variable size tree mode, the throughput was 500 MB s. Based on the performance analysis, modification of the input rate of the KECCAK sponge resulted in a negligible change to the overall speed. As a result of the hardware profiling, the register and L1 cache usage in the GPU was a major bottleneck to the overall throughput. In August 2015, SHA-3 Standard was published as the next generation of Secure Hashing Algorithm. Several optimized implementations of SHA-3 on a 24-core processor with software and hardware co-design are proposed in this paper. For software designs, a 4-core mapping scheme with shared-memory reduces the delay time by exploring the internal parallelism of the algorithm; the scheme of 5 cores with packet router improves flexibility; and 24-core mapping with circuit router fully explores the parallelism with higher energy efficiency. Hardware approaches: specific instructions and novel latch-based accelerators, are proposed to improve performance and energy efficiency. The three software mappings provide a speedup of 3.1, 3.6 and 19.6, respectively. Almost 2 times better performance and energy efficiency are obtained with three specific instructions. The implementation of 4 latch-based accelerators achieves a throughput of 81.9 Gbps at 850MHz with 3.7 pJ bit energy consumption per bit. A parallel implementation via CUDA of the dynamic programming method for the knapsack problem on NVIDIA GPU is presented. A GTX 260 card with 192 cores (1.4GHz) is used for computational tests and processing times obtained with the parallel code are compared to the sequential one on a CPU with an Intel Xeon 3.0GHz. The results show a speedup factor of 26 for large size problems. Furthermore, in order to limit the communication between the CPU and the GPU, a compression technique is presented which decreases significantly the memory occupancy. GPU is widely used in various applications that require huge computational power. In this paper, we contribute to the cryptography research community by presenting techniques to accelerate symmetric block ciphers (IDEA, Blowfish and Threefish) in NVIDIA GTX 690 with Kepler architecture. The results are benchmarked against implementation in OpenMP and existing GPU implementations in the literature. We are able to achieve encryption throughput of 90.3 Gbps, 50.82 Gbps and 83.71 Gbps for IDEA, Blowfish and Threefish respectively. Block ciphers can be used as pseudorandom number generator (PRNG) when it is operating under counter mode (CTR), but the speed is usually slower compare to other PRNG using lighter operations. Hence, we attempt to modify IDEA and Blowfish in order to achieve faster PRNG generation. The modified IDEA and Blowfish manage to pass all NIST Statistical Test and TestU01 Small Crush except the more stringent tests in TestU01 (Crush and BigCrush).