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Strong–strong beam–beam simulations for circular colliders are very time-consuming. Thus far, such simulations have predominantly utilized a linear transfer matrix to represent the full nonlinear lattice. Otherwise, when nonlinear lattices were included, beam–beam interactions were usually studied by using weak–strong or quasi-strong–strong numerical models. The next-generation circular colliders, including FCC-ee at CERN, CEPC in China, and the Super Tau-Charm Factories in China and Russia, will leverage the crab waist collision scheme with a large Piwinski angle. This scheme has already been employed in the two currently operational colliders, DAFNE and SuperKEKB. Accurate predictions of the performance of such colliders call for strong–strong beam–beam simulations, taking into account the interplay of the beam–beam interaction with lattice nonlinearities, impedances, and other collective effects. To answer this call, a novel code APES-T that allows integrating the direct strong–strong beam–beam simulations with element-by-element nonlinear tracking has been developed. To overcome the limitations in computing time, APES-T employs parallelization techniques. Specifically, the beam–beam module is parallelized using MPI programming, while the element-by-element tracking modules are parallelized using GPU programming. In this paper, we present the development of the APES-T code, its benchmark against the SAD code, and its application to the CEPC in investigating the interplay between beam–beam interaction, beamstrahlung, and lattices. [ABSTRACT FROM AUTHOR]

Nowadays, the world is fighting a dangerous form of Coronavirus that represents an emerging pandemic. Since its early appearance in China Wuhan city, many countries undertook several strict regulations including lockdowns and social distancing measures. Unfortunately, these procedures have badly impacted the world economy. Detecting and isolating positive/probable virus infected cases using a tree tracking mechanism constitutes a backbone for containing and resisting such fast spreading disease. For helping this hard effort, this research presents an innovative case study based on big data processing techniques to build a complete tracking system able to identify the central areas of infected/suspected people, and the new suspected cases using health records integration with mobile stations spatio-temporal data logs. The main idea is to identify the positive cases historical movements by tracking their phone location for the last 14 days (i.e., the virus incubation period). Then, by acquiring the citizen's mobile phone locations for the same period, the system will be able to measure the Euclidean distances between positive case locations and other nearby people to identify the incontact suspected-cases using parallel clustering and classification techniques. Moreover, the daily change of the clusters size and its centroids will be used to predict new regions of infection, as well as, new cases. Moreover, this approach will support infection avoidance by alerting people approaching areas of high probability of infection using their mobile GPS location. This case study has been developed as a simulation system consisting of three components; positive cases/citizens movement's data generation subsystem, big data processing platform including CPU/GPU tasks, and data visualization/map geotagging subsystem. The processing of such a big data system requires intensive computing tasks. Therefore, GPU tasks carried out to achieve high performance and accelerate the data processing. According to the simulated system results, data partitioning and processing speed up measures have been examined. [ABSTRACT FROM AUTHOR]

Hydrodynamic models have been widely used in urban flood modelling. Due to the prohibitive computational cost, most of urban flood simulations have been currently carried out at low spatial resolution or in small localised domains, leading to unreliable predictions. With the recent advance in high-performance computing technologies, GPU-accelerated hydrodynamic models are now capable of performing high-resolution simulations at a city scale. This paper presents a multi-GPU hydrodynamic model applied to reproduce a flood event in a 267.4 km2 urbanised domain in Fuzhou, Fujian Province, China. At 2 m resolution, the simulation is completed in nearly real time, demonstrating the efficiency and robustness of the model for high-resolution flood modelling. The model is used to further investigate the effects of varying spatial resolution and using localised domains on the simulation results. It is recommended that urban flood simulations should be performed at resolutions higher than 5 m and localised simulations may introduce unacceptable numerical errors. [ABSTRACT FROM AUTHOR]

The material point method (MPM) simulation of large-scale landslides can provide useful information to response to landslide hazards in mountainous regions. However, the structured background meshes commonly adopted in MPM fail to accurately capture the complex terrain above which the sliding mass moves. In this work, a novel terrain contact algorithm is proposed and integrated into a GPU-accelerated 3D MPM to model large-scale landslides. The complex terrain is modelled by a fixed triangular surface mesh generated according to the digital elevation model (DEM) and the interaction between the sliding mass and the basal surface is modelled by a contact algorithm between the triangular surface mesh and the material points. Furthermore, a contact detection algorithm based on level-set functions is developed to improve the contact detection efficiency. The detailed computational formulations and numerical implementation based on GPU parallel computing are presented. After the correctness and the efficiency being verified by two numerical examples, the proposed method is applied to simulate the catastrophic Baige landslide that occurred in China in 2018. The results indicate that the present method is both accurate and efficient and can provide a useful tool for numerical study on landslides. [ABSTRACT FROM AUTHOR]

In China, fast city rebuilding poses the challenge of frequent refresh cycle of urban traffic noise mapping. Computational complexity and lack of resources are the primary bottleneck in traffic noise mapping. In this paper, we present a flexible distributed heterogeneous computing method based on GPU-CPU cooperation, which reduces the overhead, improves the efficiency of parallel computing and consistently generates good quality results for traffic noise mapping. A genetic algorithm based large-scale task partition algorithm is employed to solve load balancing problem in distributed noise mapping calculation. The methodology is evaluated by an example, whose results show that the proposed task partition method can significantly improve running efficiency. Parallel efficiency increases from 54% to 78%. In addition, test speed is further improved by 21% with the GPU-CPU collaborative computing, even with only low-end type GPUs. [ABSTRACT FROM AUTHOR]

High aggradation and degradation in a river induced by last flood event will respectively raise and decrease the risk of an upcoming flood event. However, this point is likely to be ignored or is not fully considered in some 2D fluvial flood models. To address this problem, this work develops a 2D high-resolution hydrodynamic model coupled with the sediment transport and the river bed evolution models. The modelling system is within the framework of a Godunov-type finite volume scheme. GPU technique is applied to accelerate the computation by more than 10 times, comparing to the CPU counterpart. After being validated against an experimental benchmark test, the model is applied to simulate the effects of the morphological change on flood patterns for the Bayangaole Reach of Yellow River, China. The results indicate that the effect of perturbed bed could be of significance for the fluvial flood over movable bed. It is therefore suggested to take into account when evaluating the flood risk in this case. [ABSTRACT FROM AUTHOR]

Solar energy, as a clean and renewable resource is becoming increasingly important in the global context of climate change and energy crisis. Utilization of solar energy in urban areas is of great importance in urban energy planning, environmental conservation, and sustainable development. However, available spaces for solar panel installation in cities are quite limited except for building roofs. Furthermore, complex urban 3D morphology greatly affects sunlit patterns on building roofs, especially in downtown areas, which makes the determination of roof solar energy potential a challenging task. The object of this study is to estimate the solar radiation on building roofs in an urban area in Shanghai, China, and select suitable spaces for installing solar panels that can effectively utilize solar energy. A Graphic Processing Unit (GPU)-based solar radiation model named SHORTWAVE-C simulating direct and non-direct solar radiation intensity was developed by adding the capability of considering cloud influence into the previous SHORTWAVE model. Airborne Light Detection and Ranging (LiDAR) data was used as the input of the SHORTWAVE-C model and to investigate the morphological characteristics of the study area. The results show that the SHORTWAVE-C model can accurately estimate the solar radiation intensity in a complex urban environment under cloudy conditions, and the GPU acceleration method can reduce the computation time by up to 46%. Two sites with different building densities and rooftop structures were selected to illustrate the influence of urban morphology on the solar radiation and solar illumination duration. Based on the findings, an object-based method was implemented to identify suitable places for rooftop solar panel installation that can fully utilize the solar energy potential. Our study provides useful strategic guidelines for the selection and assessment of roof solar energy potential for urban energy planning. [ABSTRACT FROM AUTHOR]

In this paper we present the programming of the Linpack benchmark on TianHe-1 system, the first petascale supercomputer system of China, and the largest GPU-accelerated heterogeneous system ever attempted before. A hybrid programming model consisting of MPI, OpenMP and streaming computing is described to explore the task parallel, thread parallel and data parallel of the Linpack. We explain how we optimized the load distribution across the CPUs and GPUs using the two-level adaptive method and describe the implementation in details. To overcome the low-bandwidth between the CPU and GPU communication, we present a software pipelining technique to hide the communication overhead. Combined with other traditional optimizations, the Linpack we developed achieved 196 :7 GFLOPS on a single compute element of TianHe-1. This result is 70 :1% of the peak compute capability, 3 :3 times faster than the result by using the vendor's library. On the full configuration of TianHe-1 our optimizations resulted in a Linpack performance of 0 :563 PFLOPS, which made TianHe-1 the 5th fastest supercomputer on the Top500 list in November, 2009. [ABSTRACT FROM AUTHOR]