The radiation damage effect of key structural materials is one of the main research subjects of the numerical reactor. From the perspective of experimental safety and feasibility, Molecular Dynamics (MD) in the materials field is an ideal method for simulating the radiation damage of structural materials. The Crystal-MD represents a massive parallel MD simulation software based on the key material characteristics of reactors. Compared with the Large-scale Atomic/Molecurlar Massively Parallel Simulator (LAMMPS) and ITAP Molecular Dynamics (IMD) software, the Crystal-MD reduces the memory required for software operation to a certain extent, but it is very time-consuming. Moreover, the calculation results of the Crystal-MD have large deviations, and there are also some problems, such as memory limitation and frequent communication during its migration and optimization. In this paper, in order to solve the above problems, the memory access mode of the Crystal-MD software is studied. Based on the memory access mode, a memory access optimization strategy is proposed for a unique architecture of China’s supercomputer Sunway TaihuLight. The proposed optimization strategy is verified by the experiments, and experimental results show that the running speed of the Crystal-MD is increased significantly by using the proposed optimization strategy.

With the development of Chinese international trade, real-time processing systems based on ship trajectory have been used to cluster trajectory in real-time, so that the hot zone information of a sea ship can be discovered in real-time. This technology has great research value for the future planning of maritime traffic. However, ship navigation characteristics cannot be found in real-time with a ship Automatic Identification System (AIS) positioning system, and the clustering effect based on the density grid fixed-time-interval algorithm cannot resolve the shortcomings of real-time clustering. This study proposes an adaptive time interval clustering algorithm based on density grid (called DAC-Stream). This algorithm can perform adaptive time-interval clustering according to the size of the real-time ship trajectory data stream, so that a ship’s hot zone information can be found efficiently and in real-time. Experimental results show that the DAC-Stream algorithm improves the clustering effect and accelerates data processing compared with the fixed-time-interval clustering algorithm based on density grid (called DC-Stream).

MapReduce is currently the most popular programming model for big data processing, and Hadoop is a well-known MapReduce implementation platform. However, Hadoop jobs suffer from imbalanced workloads during the reduce phase and inefficiently utilize the available computing and network resources. In some cases, these problems lead to serious performance degradation in MapReduce jobs. To resolve these problems, in this paper, we propose two algorithms, the Locality-Based Balanced Schedule (LBBS) and Overlapping-Based Resource Utilization (OBRU), that optimize the Locality-Enhanced Load Balance (LELB) and the Map, Local reduce, Shuffle, and final Reduce (MLSR) phases. The LBBS collects partition information from input data during the map phase and generates balanced schedule plans for the reduce phase. OBRU is responsible for using computing and network resources efficiently by overlapping the local reduce, shuffle, and final reduce phases. Experimental results show that the LBBS and OBRU algorithms yield significant improvements in load balancing. When LBBS and OBRU are applied, job performance increases by 15% from that of models using LELB and MLSR.

Kinetic Monte Carlo (KMC) is a widely used method for studying the evolution of materials at themicrocosmic level. At present, while there are many simulation software programs based on this algorithm, most focus on the verification of a certain phenomenon and have no analog-scale requirement, so many are serial in nature. The dynamic Monte Carlo algorithm is implemented using a parallel framework called SPPARKS, but it does not support the Embedded Atom Method (EAM) potential, which is commonly used in the dynamic simulation of metal materials. Metal material — the preferred material for most containers and components — plays an important role in many fields, including construction engineering and transportation. In this paper, we propose and describe the development of a parallel software program called Crystal-KMC, which is specifically used to simulate the lattice dynamics of metallic materials. This software uses MPI to achieve a parallel multiprocessing mode, which avoid the limitations of serial software in the analog scale. Finally, we describe the use of the parallel-KMC simulation software Crystal-KMC in simulating the diffusion of vacancies in iron, and analyze the experimental results. In addition, we tested the performance of Crystal-KMC in “meta -Era” supercomputing clusters, and the results show the Crystal-KMC parallel software to have good parallel speedup and scalability.

With the rise of various cloud services, the problem of redundant data is more prominent in the cloud storage systems. How to assign a set of documents to a distributed file system, which can not only reduce storage space, but also ensure the access efficiency as much as possible, is an urgent problem which needs to be solved. Space-efficiency mainly uses data de-duplication technologies, while access-efficiency requires gathering the files with high similarity on a server. Based on the study of other data de-duplication technologies, especially the Similarity-Aware Partitioning (SAP) algorithm, this paper proposes the Frequency and Similarity-Aware Partitioning (FSAP) algorithm for cloud storage. The FSAP algorithm is a more reasonable data partitioning algorithm than the SAP algorithm. Meanwhile, this paper proposes the Space-Time Utility Maximization Model (STUMM), which is useful in balancing the relationship between space-efficiency and access-efficiency. Finally, this paper uses 100 web files downloaded from CNN for testing, and the results show that, relative to using the algorithms associated with the SAP algorithm (including the SAP-Space-Delta algorithm and the SAP-Space-Dedup algorithm), the FSAP algorithm based on STUMM reaches higher compression ratio and a more balanced distribution of data blocks.