The Atomic Kinetic Monte Carlo (AKMC) method provides insights into the macroscopic behavior of materials through atomistic-level simulations and finds broad applications in materials science innovation. Improving simulation scale and performance remains a consistent focus in the development of parallel AKMC software. We port the AKMC software to GPU clusters. To alleviate the memory pressure in large-scale complex system simulations, we redesign the data layout and propose the Lattice Data Compression and Vacancy Data Decompression algorithms. Additionally, We propose a multi-level pipeline scheme combined with an on-demand communication forwarding and merging strategy to reduce data transfer and communication overhead. Compared to state-of-the-art KMC software, MISA-AKMC achieves a 10.41-fold improvement in computational throughput and a 52.07-fold expansion in simulation scale. We implement the first true micrometer-scale AKMC simulation involving 20 quadrillion atoms on GPU clusters. MISA-AKMC achieves 96.03% parallel efficiency in weak scaling and 85.29% in strong scaling on 16,000 GPUs.

Sparse matrix-vector multiplication (SpMV) is a fundamental building block for various numerical computing applications. However, most existing GPU-SpMV approaches may suffer from either long preprocessing overhead, load imbalance, format conversion, bad memory access patterns. In this paper, we proposed two new SpMV algorithms:flat andline-enhance, as well as their implementations, for GPU systems to overcome the above shortcomings. Our algorithms work directly on the CSR sparse matrix format. To achieve high performance: 1) for load balance, theflat algorithm uses non-zero splitting andline-enhance uses a mix of row and non-zero splitting; 2) memory access patterns are designed for both algorithms for data loading, storing and reduction steps; and 3) an adaptive approach is proposed to select appropriate algorithm and parameters based on matrix characteristics.

The limitation of simulation scales leads to a gap between simulation results and physical phenomena. This paper reports our efforts on increasing the scalability of metal material microscopic damage simulation on the Sunway TaihuLight supercomputer. We use a multiscale modeling approach that couples Molecular Dynamics (MD) with Kinetic Monte Carlo (KMC). According to the characteristics of metal materials, we design a dedicated data structure to record the neighbor atoms for MD, which significantly reduces the memory consumption. Data compaction and double buffer are used to reduce the data transfer overhead between the main memory and the local store. We propose an on-demand communication strategy for KMC to remarkably reduce the communication overhead. We simulate 4 * 1012 atoms on 6,656,000 master+slave cores using MD with 85% parallel efficiency. Using the coupled MD-KMC approach, we simulate 3.2 * 1010 atoms in 19.2 days temporal scale on 6,240,000 master+slave cores with runtime of 8.6 hours.

This paper provide a brief survey of semantic similarity including semantic similarity between concepts and semantic textual similarity. We classify methods of semantic similarity between into four categories based on background information resource used and classify methods of semantic textual similarity into four categories too. As a basic methodology of text related research and applications, semantic similarity measure is often used in online social network analysis. This survey introduces how similarity computation methods assist in many aspects of social network analysis.

In this paper, we propose a dynamic audit service for verifying the integrity of an untrusted and outsourced storage. Our audit service is constructed based on the techniques, fragment structure, random sampling, and index-hash table, supporting provable updates to outsourced data and timely anomaly detection. In addition, we propose a method based on probabilistic query and periodic verification for improving the performance of audit services. Our experimental results not only validate the effectiveness of our approaches, but also show our audit system verifies the integrity with lower computation overhead and requiring less extra storage for audit metadata.

With the development of e-science, more and more attention is drawn to the visualization and interoperation of scientific data. Integrating Data Grid technology and Geographical Information System (GIS) offers a well solution to implement e-science applications. Utilizing Data Grid technology could take advantages to share heterogeneous and distributed data sources. GIS provides visualization environment for spatial data and non-spatial data. In this paper we brought forward spatial information grid (SIG) architecture for gas network safety evaluation application. After discussing the key technologies of gas network SIG, such as spatial metadata modeling and spatial metadata searching, one prototype system was designed and then implemented. Through the SIG system, gas network safety evaluation results can be shown to users in an intuitive and convenient way.

Different from the traditional topic detection and tracking (TDT) technologies primarily focus on detecting topic in local network domain, this paper proposes a cooperative alert topic detection model in distributed environment (named CATDM). The model abstracts the alert topic and represents it as the local alert case by analyzing the alert of campus network culture in depth. The model not only discovers new alert topic of local network domain, but also cooperatively schedules the information of alert case knowledge base (ACKB) between different alert monitor nodes. CATDM discovers new alert topic of each monitor node and optimizes the local ACKB periodically. Through cooperatively scheduling the information of ACKB between different alert monitor nodes, the model enables some alert monitor nodes to obtain the ability of detecting new alert topic and strengthens the detection ability of burst alert topic. To validate the performance of CATDM, we present two comparison experiments on the data corpus about ¿campus network culture¿. The experiment results validate the feasibility and practicality of CATDM, and demonstrate that CATDM can effectively improve the detecting ability of burst alert topics of local network domain in distributed environment.

Aiming at the deficiencies of traditional blog hotness evaluation methods, the paper presents a blog hotness evaluation model based on text opinion analysis (named BHEM-TOA). The model not only considers the number of reviews, comments and publication time of the blog topic, but also focuses on the comment opinion. BHEM-TOA emphasizes subjective opinions of reviewers about the blog topic. It utilizes the text opinion analysis method based on Chinese characters to extract opinioned comments, gets supportive and oppositive circumstances about the blog topic, then combines with the number of reviews, comments and publication time to realize blog hotness evaluation. To validate the performance of BHEM-TOA, the experiment constructs two data corpuses called TOAC and BHEC, and the experimental results demonstrate that BHEM-TOA could more precisely and comprehensively evaluate the hotness of the blog than traditional methods.

We present a new cache-efficient parallel multilayer Gauss-Seidel algorithm to solve 2D diffusion equations on distributed memory machines, by focusing on improving its cache behaviour and parallelism simultaneously. The novelty of our parallel multi-layer algorithm lies in performing Gauss-Seidel in two alternating sweeping directions (with multiple layers, i.e., iterations per direction) and applying alternating tiling strategies in two opposite sweeping directions to the subdomain allocated to every processor. As a result, its efficiency comes from a significant reduction in two sources of overhead: data cache misses and communication costs. In comparison with two commonly used parallel Gauss-Seidel algorithms, our algorithm has good performance and scalability in a cluster computing environment.

Web service composition is an emerging paradigm for enabling application integration within and across organizational boundaries. Since most business processes exist in temporal context in real world, and the candidate partners in Web service compositions have complex interactions, timing constraints satisfiability verification for Web service compositions becomes increasingly important to build efficient and effective business processes based on Web services. In this paper, we present an approach to specify and verify timing constraints satisfiability for the compositions of Web services workflow based onBPEL4WS(Business Process Execution Language for Web Services). By using timing constraints annotation layers, a BPEL4WS model can be extended with timing constraint information. The pair of an underlying BPEL4WS model and a timing constraint annotation layer can be transformed to corresponding TPPN (Timed Predicate Petri-net) model to verify automatically timing constraint satisfiability for a business process. The method allows us to add conveniently timing constraints information to a business process model,and find whether a process is schedulable and which activities in the process are not schedulable under the consideration of timing constraints without actual execution based on its specification. The paper describes how to specify timing constraints for compositions of Web services workflow, and how to determine whether the specification satisfies its timing constraints.

Existing software for oil-drilling engineering may be developed by different people using different languages and technologies, run on different hardware platforms, use different operating systems, and provide very different functionalities. Therefore, it is of practical significance and great value to find out a modeling technique for integrating the software. In this paper, a goal-tree based domain-specific model for oil-drilling engineering is suggested, which consists of objects wrapping business goal and business-logic. It offers the benefits of extensibility, modularity, and reusability. Furthermore, an abstract service model with reference to Service Component Architecture is proposed to mapping domain-specific model into real system. The specialties of Service Component Architecture contribute to the integration of heterogeneous modules and keep the consistency of business modules at various phases of development.

At present, massive Web Services are available on the Internet; discovery of relevant and required ones among these Web Services becomes a significant challenge. Because of deficiency of semantics, traditional discovery approaches are inefficient and have lower precision rate. In such a case, this paper presents a framework of Web Services description and discovery using OWL-S and domain ontology by which semantic information is extracted to help convert WSDLs into service profile files describing how service works semantically; then mappings from profiles to UDDI are built to reconstructed data structures of UDDI for supporting semantic services discovery. Meanwhile, a query template is provided to formulate the format of service requirement; after formatted, alluser queries will be submitted to discovery engine and query engine to complete the semantic discovery process. As results, the most suited services can be discovered and returned to users. The experiment shows this framework is effective and useful in services discovery.

Timing constraints verification (such as time consistency) for service flow has become indispensable since many services for e-business are distributed in different places with temporal context. Unfortunately, current researches mainly focus on the modeling, analyzing and verifying of service processes, while ignoring temporal factors. To address the challenges of temporal properties verification for service, the paper presents a method for verifying time constraints consistency of service flow. By building time ontology based on DAML_Time, OWL_S is extended with time constraints, which expresses time information of service flow clearly and roundly. Then the annotated OWL_S is transformed to some kinds of formal models and verification algorithm is presented. A prototype system MC4WST is also designed to prove feasibility of the verification process.

Many important scientific kernels compute solutions using finite difference techniques, and the most time consuming part of them is the iterative method, such as Gauss-Seidel or SOR. To improve performance, iterative method can exploit parallelism, intra-iteration data reuse, and inter-iteration data reuse. This paper describes a new parallel Gauss-Seidel method using iteration space alternate tiling technique, which is developed not only to improve parallelism, intra-iteration, and inter-iteration data locality, but also to decrease communication and synchronization cost in iterative method. Time-skewing can increase cache locality by exploiting locality in the time direction as well as spatial locality. The degree of parallelism can be improved by reordering the execution of cache blocks. Finally numerical results are presented which confirm the effectiveness of Gauss-Seidel parallelized with iteration space alternate tiling technique, specifically compared with owner-computes and red-black coloring based Gauss-Seidel methods, and show that the new method has a good parallel performance on distributed memory machines, as well as scalability.

Group communication significantly influences the performance of data parallel applications. Nevertheless, the important factor that influences the efficiency of group communication is often neglected: a larger communication idle time may occur when there is node contention and difference among message lengths during one particular communication step. Group communication scheduling has attracted more and more attentions. In previous works, researchers canpsilat completely avoid communication conflict or they only focus on some special cases. This paper is devoted to develop a universal and efficient scheduling strategy concerning with the situation where array distributions are block-cyclic. Base on the proof for the recursive theorems of communication table elements, this strategy generates a communication scheduling table so that each column is a permutation of receiving node number in each communication step. And the messages with the close size are put into a communication step as near as possible. This indicates that our strategy not only avoids inter-processor contention, but it also minimizes real communication cost in each communication step. Finally, experimental results show that our strategy has better performance than the general method and the implementation of all-to-all based scheduling, and greedy scheduling.

Resolving semantic heterogeneity and giving users a unified view to access distributed data sources is one of the focuses in scientific data management research community. For the integration and share of scientific data in materials science, a semantic model for materials scientific data is designed based on ontology technology. The semantic model covers the key concepts in materials science and their semantic relationships. With the semantic model which is considered as the global conceptual model, a semantic integration method is proposed, where mappings between the semantic model and local ontology as well as mappings between local ontology and data schema are built to make seamless semantic interoperability possible. Furthermore, the architecture and implementation details for the mappings are presented. Finally, the experimental results demonstrate the work of this paper, and show that the semantic model and integration method are useful in heterogeneous materials data environment.