The Parallel Calculation of the Ground-State Correlation Energy of Helium Atom

When we use Modified Configuration Interaction method(MCI) to calculate the correlation energy of double electron systems, for obtaining the higher precision, we always need huge calculations. In order to handle this problem, which will cost much CPU time and memory room if only using a single computer to do it, we now adopt the parallel multisection recurrence algorithm. Thus we can use several CPUs to get the ground state energy of a Helium atom at the same time.

Parallelism and Optimization of Numerical Ocean Forecasting Model

According to the characteristics of Chinese marginal seas, the Marginal Sea Model of China(MSMC) has been developed independently in China. Because the model requires long simulation time, as a routine forecasting model, the parallelism of MSMC becomes necessary to be introduced to improve the performance of it. However, some methods used in MSMC, such as Successive Over Relaxation(SOR) algorithm, are not suitable for parallelism. In this paper, methods are developedto solve the parallel problem of the SOR algorithm following the steps as below. First, based on a 3D computing grid system, an automatic data partition method is implemented to dynamically divide the computing grid according to computing resources. Next, based on the characteristics of the numerical forecasting model, a parallel method is designed to solve the parallel problem of the SOR algorithm. Lastly, a communication optimization method is provided to avoid the cost of communication. In the communication optimization method, the non-blocking communication of Message Passing Interface(MPI) is used to implement the parallelism of MSMC with complex physical equations, and the process of communication is overlapped with the computations for improving the performance of parallel MSMC. The experiments show that the parallel MSMC runs 97.2 times faster than the serial MSMC, and root mean square error between the parallel MSMC and the serial MSMC is less than 0.01 for a 30-day simulation(172800 time steps), which meets the requirements of timeliness and accuracy for numerical ocean forecasting products.

A new method to retrieve aerosol optical thickness from satellite images on a parallel system

A wide variety of algorithms have been developed to monitor aerosol burden from satellite images. Still, few solutions currently allow for real-time and efficient retrieval of aerosol optical thickness （AOT）, mainly due to the extremely large volume of computation necessary for the numeric solution of atmospheric radiative transfer equations. Taking into account the efforts to exploit the SYNergy of Terra and Aqua Modis （SYNTAM, an AOT retrieval algorithm）, we present in this paper a novel method to retrieve AOT from Moderate Resolution Imaging Spectroradiometer （MODIS） satellite images, in which the strategy of block partition and collective communication was taken, thereby maximizing load balance and reducing the overhead time during inter-processor communication. Experiments were carried out to retrieve AOT at 0.44, 0.55, and 0.67μm of MODIS/Terra and MODIS/Aqua data, using the parallel SYNTAM algorithm in the IBM System Cluster 1600 deployed at China Meteorological Administration （CMA）. Results showed that parallel implementation can greatly reduce computation time, and thus ensure high parallel efficiency. AOT derived by parallel algorithm was validated against measurements from ground-based sun-photometers; in all cases, the relative error range was within 20%, which demonstrated that the parallel algorithm was suitable for applications such as air quality monitoring and climate modeling.

A parallel computing framework for big data

Abstract Big data has received great attention in research and application. However, most of the current efforts focus on system and application to handle the challenges of ＂volume＂ and ＂velocity＂, and not much has been done on the theoreti- cal foundation and to handle the challenge of ＂variety＂. Based on metric-space indexing and computationalcomplexity the- ory, we propose a parallel computing framework for big data. This framework consists of three components, i.e., universal representation of big data by abstracting various data types into metric space, partitioning of big data based on pair-wise distances in metric space, and parallel computing of big data with the NC-class computing theory.

Data Analyses and Parallel Optimization of the Tropical-Cyclone Coupled Numerical Model

Tropical cyclones(TCs)are one of the most feared and deadly weather systems in the world.An air-sea coupled numerical model offers a more accurate description of physical processes between atmospheric-ocean fluids.An operational ocean-atmosphere-wave coupled modeling system is employed to improve the prediction accuracy of tropical cyclones in the NationalMarine Environmental Forecasting Center(NMEFC).Due to the urgent need for operational timeliness,the parallel performance of the operational forecasting system has been analyzed.The parallel algorithm,parallel partitioning grids,and other optimizations were tested after system deployment on the Lenovo cluster of the NMEFC.After optimization,a well-balanced performance of the system is obtained,and computing resources are reasonably utilized,thus laying the foundation for real-time tropical cyclone forecasting.