The logistic map is a class of chaotic maps. It is still in use in image cryptography. The logistic map cryptosystem has two stages, namely permutation, and diffusion. These two stages being computationally intensive, the permutation relocates the pixels, whereas the diffusion rescales them. The research on refining the logistic map is progressing to make the encryption more secure. Now there is a need to improve its efficiency to enable such models to fit for high-speed applications. The new invention of accelerators offers efficiency. But the inherent data dependencies hinder the use of accelerators. This paper discusses the novelty of identifying independent data-parallel tasks in a logistic map, handing them over to the accelerators, and improving their efficiency. Among the two accelerator models proposed, the first one achieves peak efficiency using coalesced memory access. The other cryptosystem further improves performance at the cost of more execution resources. In this investigation, it is noteworthy that the parallelly accelerated logistic map achieved a significant speedup to the larger grayscale image used. The objective security estimates proved that the two stages of the proposed systems progressively ensure security.
Improving Uintah's Scalability Through the Use of Portable Kokkos-Based Data Parallel Tasks
