This paper presents a fine-tuned implementation of the quicksort algorithm for highly parallel multicore NVIDIA graphics processors. The described approach focuses on algorith-mic and implementation-level improvements to achieve enhanced performance. Several fine-tuning techniques are explored to identify the best combination of improvements for the quicksort algorithm on GPUs. The results show that this approach leads to a significant reduction in execution time and an improvement in algorithmic operations, such as the number of iterations of the algorithm and the number of operations performed compared to its predecessors. The experiments are conducted on an NVIDIA graphics card, taking into account several distributions of input data. The findings suggest that this fine-tuning approach can enable efficient and fast sorting on GPUs for a wide range of applications.

In this article, an upgraded version of CUDA-Quicksort - an iterative implementation of the quicksort algorithm suitable for highly parallel multicore graphics processors, is described and evaluated. Three key changes which lead to improved performance are proposed. The main goal was to provide an implementation with increased scalability with the size of data sets and number of cores with modern GPU architectures, which was successfully achieved. The proposed changes also lead to significant reduction in execution time. The execution times were measured on an NVIDIA graphics card, taking into account the possible distributions of the input data.

Education and society always lag behind technical state of the art achievements. General computer literacy needed decades to become the part of public acceptance after computers become available. Smart phones enters our life and becomes an extension of the human body yet we still do not know how to properly apply them in education. Artificial intelligence is an exciting technology that adapts educational experiences to different learning groups, teachers and tutors. Intelligent Management Systems (IMS) are not a novelty in education though. There have been many experiments, but they have all somehow stalled due to immature technology or misinterpretation. We can now see a new impetus for AI in education, and its impact will soon be very noticeable. In education, AI can: personalize learning, connect and create innovative learning content, perform tutoring in intelligent tutoring systems, is used to help pupils with special needs, help teachers assess, give students access to learning content, and translate educational content from different languages, removing language barriers. This article will explore the different possibilities of using AI in education and its use in education.

In this paper, three variants of the Floyd-Warshall (FW) All Pairs Shortest Path (APSP) algorithm are presented and compared - the sequential implementation, the parallel implementation using the Nvidia CUDA API, and the blocked parallel version of the FW algorithm. A performance analysis between these three approaches, as well as between the individual phases of the parallel algorithm is provided. The performance of these algorithms has been measured on regular as well as on embedded GPU hardware, and a significant speedup has been achieved. Additionally, this paper shows that a blocked data access results in significant energy savings of up to 72% on embedded hardware.