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There are many scientific applications ranging from weather prediction to oil and gas exploration that requires high-performance computing. It aids industries and researchers to enrich further their advancements. With the advent of general purpose computing over GPUs, most of the applications above are shifting towards High-Performance Computing (HPC). Agent-based crowd simulation is one of the candidates that requires high-performance computing. This type of application is used to predict crowd movement in highly congested areas. One of the most crucial scenarios in which this application can be used is to mimic the movement of the multi-cultural crowd performing Hajj and Umrah in Masjid Al-Haram, Makkah. Adequate performance for an agent-based crowd system is a common problem in computer science. While the existing event planning software, specifically for Hajj and Umrah, are unable to provide the required performance. The main reason is the increasing amount of autonomous pilgrims every year. In this paper, we propose a high performance agent-based crowd simulation that represents pilgrim movement during these rituals. The performance is achieved by parallelizing an open source steering library called OpenSteer using CUDA over GPU. By using our technique, event organizers will be able to simulate large crowds and will also be able to predict whether the developed event plan is viable or not. We have also discussed the architecture and implementation of this parallel Hajj simulation.

Recently, wireless sensor network (WSN) gets more concern due to the robustness in the latest communication technology iteration such as big data, IoT and 5G. Such daily usage of these technologies includes smart home, smart farming, smart traffic control etc. Moreover, WSN becomes the best preference for mobile objects in data accumulating in a wild range area. Routing distance, signal interference and routing computational cost give a significant impact to the WSN nodes lifetime. Unsynchronized node time allocation slot and neighbor discovery are the main factors in the energy consumption issue faced by the WSN. Higher energy consumption reduces the network lifetime and WSN nodes performance. This paper discusses the optimization of energy-topology (E-T) factors for distributed time division multiplexing algorithm (TDMA) slot scheduling for high-speed data link capacity. The E-T factor is based on the influence of residual energy and topology on the time slot allocation. Both node residual energy and topology information have shown a respectable impact on the TDMA node slot allocation. Moreover, the numbers of neighbors and the network residual energy have been proved both nodes execution time and energy utilization can be reduced in the algorithm. The algorithm performance has been evaluated based on the previous experiment parameters with new high-speed data link. The experimental results have shown a significant improvement in residual energy consumption for the proposed optimized TDMA slot allocation