RESUMO
This paper presents a GPU-accelerated implementation of an image encryption algorithm. The algorithm uses the concepts of a modified XOR cipher to encrypt and decrypt the images, with an encryption pad, generated using the shared secret key and some initialization vectors. It uses a genetically optimized pseudo-random generator that outputs a stream of random bytes of the specified length. The proposed algorithm is subjected to a number of theoretical, experimental, and mathematical analyses, to examine its performance and security against a number of possible attacks, using the following metrics - histogram analysis, correlation analysis, information entropy analysis, NPCR and UACI. The performance analysis carried out shows an average speedup-ratio of 3.489 for encryption, and 4.055 for decryption operation, between the serial and parallel implementations using GPU. The algorithm aims to provide better performance benchmarks, which can significantly improve the experience in the relevant use-cases, like real-time media applications.
RESUMO
The multitiered iterative phasing (MTIP) algorithm is used to determine the biological structures of macromolecules from fluctuation scattering data. It is an iterative algorithm that reconstructs the electron density of the sample by matching the computed fluctuation X-ray scattering data to the external observations, and by simultaneously enforcing constraints in real and Fourier space. This paper presents the first ever MTIP algorithm acceleration efforts on contemporary graphics processing units (GPUs). The Compute Unified Device Architecture (CUDA) programming model is used to accelerate the MTIP algorithm on NVIDIA GPUs. The computational performance of the CUDA-based MTIP algorithm implementation outperforms the CPU-based version by an order of magnitude. Furthermore, the Heterogeneous-Compute Interface for Portability (HIP) runtime APIs are used to demonstrate portability by accelerating the MTIP algorithm across NVIDIA and AMD GPUs.