Development Of Information Visualization Methods For Use In Multimedia Applications

The aim of the article is development of a technique for visualizing information for use in multimedia applications. In this study, to visualize information, it is proposed first to compile a list of key terms of the subject area and create data tables. Based on the structuring of fragments of the subject area, a visual display of key terms in the form of pictograms, a visual display of key terms in the form of images, and a visual display of data tables are performed. The types of visual structures that should be used to visualize information for further use in multimedia applications are considered. The analysis of existing visual structures in desktop publishing systems and word processors is performed.To build a mechanism for visualizing information about the task as a presentation, a multimedia application is developed using Microsoft Visual Studio software, the C# programming language by using the Windows Forms application programming interface. An algorithm is proposed for separating pieces of information text that have key terms. Tabular data was visualized using the “parametric ruler” metaphorical visualization method, based on the metaphor of a slide rule.The use of the parametric ruler method on the example of data visualization for the font design of children's publications is proposed. Interaction of using the method is ensured due to the fact that the user will enter the size of the size that interests for it and will see the ratio of the values of other parameters. The practical result of the work is the creation of a multimedia application “Visualization of Publishing Standards” for the visualization of information for the font design of publications for children. The result of the software implementation is the finished multimedia applications, which, according to the standardization visualization technique in terms of prepress preparation of publications, is the final product of the third stage of the presentation of the visual for

Extending Yioop! With Geographical Location Local Search

It is often useful when doing an internet search to get results based on our current location. For example, we might want such results when we search on restaurants, car service center, or hospitals. Current open source search engines like those based on Nutch do not provide this facility. Commercial engines like Google and Yahoo! provide this facility so it would be useful to incorporate it in an open source alternative. The goal of this project is to include location aware search in Yioop!(Pollett, 2012) by using geographical data from OpenStreetMap(“Open Street map wiki”, 2012) and hostip.info (“DMOZ”, n.d.) database to geolocate IP addresses

Mixing multi-core CPUs and GPUs for scientific simulation software

Recent technological and economic developments have led to widespread availability of multi-core CPUs and specialist accelerator processors such as graphical processing units (GPUs). The accelerated computational performance possible from these devices can be very high for some applications paradigms. Software languages and systems such as NVIDIA's CUDA and Khronos consortium's open compute language (OpenCL) support a number of individual parallel application programming paradigms. To scale up the performance of some complex systems simulations, a hybrid of multi-core CPUs for coarse-grained parallelism and very many core GPUs for data parallelism is necessary. We describe our use of hybrid applica- tions using threading approaches and multi-core CPUs to control independent GPU devices. We present speed-up data and discuss multi-threading software issues for the applications level programmer and o er some suggested areas for language development and integration between coarse-grained and ne-grained multi-thread systems. We discuss results from three common simulation algorithmic areas including: partial di erential equations; graph cluster metric calculations and random number generation. We report on programming experiences and selected performance for these algorithms on: single and multiple GPUs; multi-core CPUs; a CellBE; and using OpenCL. We discuss programmer usability issues and the outlook and trends in multi-core programming for scienti c applications developers

A New Multi-threaded and Interleaving Approach to Enhance String Matching for Intrusion Detection Systems

String matching algorithms are computationally intensive operations in computer science. The algorithms find the occurrences of one or more strings patterns in a larger string or text. String matching algorithms are important for network security, biomedical applications, Web search, and social networks. Nowadays, the high network speeds and large storage capacity put a high requirement on string matching methods to perform the task in a short time. Traditionally, Aho-Corasick algorithm, which is used to find the string matches, is executed sequentially. In this paper, a new multi-threaded and interleaving approach of Aho-Corasick using graphics processing units (GPUs) is designed and implemented to achieve high-speed string matching. Compute Unified Device Architecture (CUDA) programming language is used to implement the proposed parallel version. Experimental results show that our approach achieves more than 5X speedup over the sequential and other parallel implementations. Hence, a wide range of applications can benefit from our solution to perform string matching faster than ever before

Computer animation data management: Review of evolution phases and emerging issues

The computer animation industry has been booming and prospering in recent thirty years. One of the significant changes faced by this industry is the evolution of computer-animation data and, yet, extant literature has offered very little insights into the evolution process and management issues pertinent to computer-animation data. Hence, many questions have surfaced in the extant literature of computer-animation data management. For example, to what extent has the data content expanded in terms of quantity and quality? To what extent has the information technology used to store and process the data changed? To what extent have the user and the community groups diversified in terms of their nature and number? Knowledge pertaining to these issues can provide new research directions to academics and also insights to practitioners for more effective and innovative management of computer-animation data. This conceptual paper, therefore, takes the pioneering step to address these issues by proposing four factors prudent for examining the evolution phases associated with computer-animation data management: technology, content, users, and community. Next, this paper presents a conceptual framework illustrating the inter-dependent relationships between these four factors together with associated theoretical and managerial issues. This paper, albeit limited by its conceptual nature, advances the extant literature of computer animation, information system, and open-product model

Suporte de parallel scan em OpenMP

Orientadores: Guido Costa Souza de Araújo, Marcio Machado PereiraDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Prefix Scan (ou simplesmente scan) é um operador que computa todas as somas parciais de um vetor. A operação scan retorna um vetor onde cada elemento é a soma de todos os elementos precedentes até a posição correspondente. Scan é uma operação fundamental para muitos problemas relevantes, tais como: algoritmos de ordenação, análise léxica, comparação de cadeias de caracteres, filtragem de imagens, dentre outros. Embora exis- tam bibliotecas que fornecem versões paralelizadas de scan em CUDA e OpenCL, não existe uma implementação paralela do operador scan em OpenMP. Este trabalho propõe uma nova clausula que permite o uso automático do scan paralelo. Ao usar a cláusula pro- posta, um programador pode reduzir consideravelmente a complexidade dos algoritmos, permitindo que ele concentre a atenção no problema, e não em aprender novos modelos de programação paralela ou linguagens de programação. Scan foi projetado em ACLang (www.aclang.org), um framework de código aberto baseado no compilador LLVM/Clang, que recentemente implementou o OpenMP 4.X Accelerator Programming Model . AClang converte regiões do programa de OpenMP 4.X para OpenCL. Experimentos com um con- junto de algoritmos baseados em Scan foram executados nas GPUs da NVIDIA, Intel e ARM, e mostraram que o desempenho da clausula proposta é equivalente ao alcan- çado pela biblioteca de OpenCL, mas com a vantagem de uma menor complexidade para escrever o códigoAbstract: Prefix Scan (or simply scan) is an operator that computes all the partial sums of a vec- tor. A scan operation results in a vector where each element is the sum of the preceding elements in the original vector up to the corresponding position. Scan is a key opera- tion in many relevant problems like sorting, lexical analysis, string comparison, image filtering among others. Although there are libraries that provide hand-parallelized im- plementations of the scan in CUDA and OpenCL, no automatic parallelization solution exists for this operator in OpenMP. This work proposes a new clause to OpenMP which enables the automatic synthesis of the parallel scan. By using the proposed clause a programmer can considerably reduce the complexity of designing scan based algorithms, thus allowing he/she to focus the attention on the problem and not on learning new paral- lel programming models or languages. Scan was designed in AClang (www.aclang.org), an open-source LLVM/Clang compiler framework that implements the recently released OpenMP 4.X Accelerator Programming Model. AClang automatically converts OpenMP 4.X annotated program regions to OpenCL. Experiments running a set of typical scan based algorithms on NVIDIA, Intel, and ARM GPUs reveal that the performance of the proposed OpenMP clause is equivalent to that achieved when using OpenCL library calls, with the advantage of a simpler programming complexityMestradoCiência da ComputaçãoMestre em Ciência da ComputaçãoCAPE

High Performance Frequent Subgraph Mining on Transactional Datasets

Graph data mining has been a crucial as well as inevitable area of research. Large amounts of graph data are produced in many areas, such as Bioinformatics, Cheminformatics, Social Networks, and Web etc. Scalable graph data mining methods are getting increasingly popular and necessary due to increased graph complexities. Frequent subgraph mining is one such area where the task is to find overly recurring patterns/subgraphs. To tackle this problem, many main memory-based methods were proposed, which proved to be inefficient as the data size grew exponentially over time. In the past few years several research groups have attempted to handle the frequent subgraph mining (FSM) problem in multiple ways. Many authors have tried to achieve better performance using Graphic Processing Units (GPUs) which has multi-fold improvement over in-memory while dealing with large datasets. Later, Google\u27s MapReduce model with the Hadoop framework proved to be a major breakthrough in high performance large batch processing. Although MapReduce came with many benefits, its disk I/O and non-iterative style model could not help much for FSM domain since subgraph mining process is an iterative approach. In recent years, Spark has emerged to be the De Facto industry standard with its distributed in-memory computing capability. This is a right fit solution for iterative style of programming as well. In this work, we cover how high-performance computing has helped in improving the performance tremendously in the transactional directed and undirected aspect of graphs and performance comparisons of various FSM techniques are done based on experimental results

Novel Parallelization Techniques for Computer Graphics Applications

Increasingly complex and data-intensive algorithms in computer graphics applications require software engineers to find ways of improving performance and scalability to satisfy the requirements of customers and users. Parallelizing and tailoring each algorithm of each specific application is a time-consuming task and its implementation is domain-specific because it can not be reused outside the specific problem in which the algorithm is defined. Identifying reusable parallelization patterns that can be extrapolated and applied to other different algorithms is an essential task needed in order to provide consistent parallelization improvements and reduce the development time of evolving a sequential algorithm into a parallel one. This thesis focuses on defining general and efficient parallelization techniques and approaches that can be followed in order to parallelize complex 3D graphic algorithms. These parallelization patterns can be easily applied in order to convert most kinds of sequential complex and data-intensive algorithms to parallel ones obtaining consistent optimization results. The main idea in the thesis is to use multi-threading techniques to improve the parallelization and core utilization of 3D algorithms. Most of the 3D algorithms apply similar repetitive independent operations on a vast amount of 3D data. These application characteristics bring the opportunity of applying multi-thread parallelization techniques on such applications. The efficiency of the proposed idea is tested on two common computer graphics algorithms: hidden-line removal and collision detection. Both algorithms are data-intensive algorithms, whose conversions from a sequential to a multithread implementation introduce challenges, due to their complexities and the fact that elements in their data have different sizes and complexities, producing work-load imbalances and asymmetries between processing elements. The results show that the proposed principles and patterns can be easily applied to both algorithms, transforming their sequential to multithread implementations, obtaining consistent optimization results proportional to the number of processing elements. From the work done in this thesis, it is concluded that the suggested parallelization warrants further study and development in order to extend its usage to heterogeneous platforms such as a Graphical Processing Unit (GPU). OpenCL is the most feasible framework to explore in the future due to its interoperability among different platforms

Parallel Computing in Java

The Java programming language and environment is inspiring new research activities in many areas of computing, of which parallel computing is one of the major interests. Parallel techniques are themselves finding new uses in cluster computing systems. Although there are excellent software tools for scheduling, monitoring and message-based programming on parallel clusters, these systems are not yet well integrated and do not provide very high-level parallel programming support. This research presents a number of issues which are considered to be key to the suitability of Java for HPC (High Performance Computing) applications and then explore the support for concurrency in the current Java 1.8 specification. We further present various relatively recent parallel Java models which support HPC for both shared and distributed memory programming paradigms. Finally, we attempt to evaluate the performance of discussed Java HPC models by comparing the same with the relative traditional native C implementations, where appropriate. The analysis of the results suggest that Java can achieve near similar performance to natively compiled languages, both for sequential and parallel applications, thus making it a viable alternative for HPC programming