CUBA

В работе рассматриваются проблемы повышения эффективности разработки ИС, и, в частности, вопросы уменьшения сроков разработки программного комплекса ИС. Проведен анализ технологий разработки программного комплекса в жизненном цикле ИС. Структурный подход к программированию предлагал осуществлять декомпозицию программ методом пошаговой детализации. Разработка структур программы выполняется при помощи построения структур входных и выходных данных, идентификации связей обработки между этими данными, формирования структуры программы на основании структур данных и обнаруженных соответствий. Преодолеть фактор сложности можно, если отойти от прямолинейного подхода к решению поставленной задачи, состоящего в последовательном и линейном наращивании исходного текста программы оператор за оператором, в результате чего получается одна длинная и аморфная программа. Оказывается действенным принцип модульности: исходная задача разбивается на относительно независимые части; они реализуются отдельными программными модулями, которые затем связываются в единое целое на этапе компоновки. Выделены особенности технологии автоматизированного синтеза программ, а именно, технологии сборки программ из типовых программных элементов. Определены основные понятия указанной выше технологии, проведено исследование конструирования программ из блоков и выявлена проблемная область. Исходными данными для постановки и решения задач синтеза системы программных модулей являются множество информационных массивов системы, для которых определены: входные, выходные и промежуточные данные; множество альтернативных процедур обработки данных; последовательности выполнения процедур в процессе обработки; способы обмена с внешней памятью. Рассмотрен подход для выделения типовых программных элементов, удовлетворяющих определенным критериям. На основании рассмотренного подхода, а также с учетом его недостатков, был предложен усовершенствованный метод классификации типовых программных элементов и способ проектирования ПО на их основе с учетом минимизации времени и стоимости проекта.

This chapter will guide you through the stages of using the Program Development Facility (pdf) to enable you to enter a process structure, allocate conditions and operations, generate the schematic logic and finally produce executable code that the structure represents. It will usually be necessary to develop the process structure manually from the input and output data structures in the normal way, but for demonstration purposes in this tutorial session an example has been prepared. pdf does not support the creation of process structures from data structures; rather it allows the automatic production of code from a process structure that has been manually prepared.

In this paper, we propose a hybrid CPU+GPU data structure, that optimizes search operation for frequently accessed search keys. This is based on the working-set structure due to Badiu et al. [1]. The main idea is to maintain a dynamic set of most frequently accessed keys in the GPU memory and the rest of the keys in the CPU main memory. Further, search queries are processed in batches of size 1K to 16K (K = 210). We measured the query throughput of our data structure using Millions of Queries Processed per Second (MQPS) as a metric, on different key access distributions. On distributions, where some keys are accessed more frequently than others, we achieved 2x higher MQPS when compared to a highly tuned hash map provided by C++ BOOST library, and 1.5x higher MQPS against the B+ tree implementation in the Rodinia GPU benchmark. We further showed the effectiveness of our structure, when it is used to store visited vertices information in breadth-first search traversal of graphs. Here, we achieved 1.2x and 1.5x speedups when compared to the BOOST hash map and the GPU B+ trees respectively.

Interactive out-of-core isosurface visualisation in time-varying data sets

Byte addressable non-volatile (NV) memory, such as STT-RAM, MRAM, and PCM, is the next generation memory that can be used as both main memory and secondary storage. While it can persistently store data without power supply, its access speed is comparable to DRAM. While there have been the active researches on its use for either main memory or secondary storage, these researches were conducted independently. This paper presents the integration methods of the main memory and file system management for NV memory, so that it can be used as both main memory and storage. The presented methods use a file system as their basis for the NV memory management; thus, the internal data structures of a file system can have impacts upon the performance of the integration methods. We implemented the proposed methods in the Linux kernel, and performed the evaluation on a system emulator. The evaluation results show that 1) the proposed methods can perform comparably to the existing DRAM memory allocator and significantly better than the page swapping, 2) their performance is affected by the internal data structures of a file system, and 3) the data structures appropriate for traditional hard disk drives do not always work effectively for byte addressable NV memory.

“When you can measure what you are speaking about and express it in numbers, you know something about it; but when you cannot measure, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely, in your thoughts, advanced to the stage of science.” LORD WILLIAM KELVIN (1824 – 1907). During the last decade, the quantification of software engineering process has got a pace due to availability of a huge amount of software repositories. These repositories include source code, bug, communication among developers/users, changes in code, etc. Researchers are trying to find out useful information from these repositories for improving the quality of software.The absence of bugs in the software is a major factor that decides the quality of software. In the available literature, researchers have proposed and implemented a plethora of bug prediction approaches varying in terms of accuracy, complexity and input data. The code change metric based bug prediction is proven to be very useful. In the literature, decay functions have been proposed that decay the complexity of code changes over a period of time in either exponential or linear fashion but they do not fit in open source software development paradigm because in open source software development paradigm, the development team is geographical dispersed and there is an irregular fluctuation in the code changes and bug detection/fixing process. The complexity of code changes reduces over a period of time that may be less than exponential or more than linear. This paper presents the method that quantifies the code change metric and also proposed decay functions that capture the variability in the decay curves represented the complexity of code changes. The proposed decay functions model the complexity of code changes which reduces over a period of time and follows different types of decay curves. We have collected the source code change data of Mozilla components and applied simple linear regression (SLR) and support vector regression (SVR) techniques to validate the proposed method and predict the bugs yet to come in future based on the current year complexity of code changes (entropy). The performance of proposed models has been compared using different performance criteria namely R2, Adjusted R2, Variation and Root Mean Squared Prediction Error (RMSPE).

5 - Data Structures and Algorithms for Moving Objects Types

In this paper we propose the use of the compact data structure k2-treap to process data cubes of Data Warehouses (DWs) into main memory. Compact data structures are data structures that allow compacting the data without losing the capacity of querying them in their compact form. A DW is a data repository to store historical data for decision support, and consists of dimensions and facts. The former are an abstract concept that groups data with a similar meaning, they are modelled as hierarchies of levels, which contain elements. The latter are quantitative data associated to dimensions. A data cube is a typical way to retrieve facts at different levels of granularity (through navigation on dimensions hierarchies). A DW can store terabytes of data, thus the efficient processing of data cubes is key in OLAP (On-line Analytical Processing). We show that by using a compact representation of data cubes and bitmaps to represent dimensions we are able to improve the use of space in main memory, and achieve better performance for query processing.

Scalable and queryable compressed storage structure for raster data

We prove new cell-probe lower bounds for dynamic data structures that maintain a subset of {1, 2, ..., n}, and compute various statistics of the set. The data structure is said to handle insertions non-adaptively if the locations of memory accessed depend only on the element being inserted, and not on the contents of the memory. For any such data structure that can compute the median of the set, we prove that:[EQUATION]where tins is the number of memory locations accessed during insertions, tmed is the number of memory locations accessed to compute the median, and w is the number of bits stored in each memory location. When the data structure is able to perform deletions non-adaptively and compute the minimum non-adaptively, we prove[EQUATION]where tmin is the number of locations accessed to compute the minimum, and tdel is the number of locations accessed to perform deletions. For the predecessor search problem, where the data structure is required to compute the predecessor of any element in the set, we prove that if computing the predecessors can be done non-adaptively, then[EQUATION]where tpred is the number of locations accessed to compute predecessors.These bounds are nearly matched by Binary Search Trees in some range of parameters. Our results follow from using the Sunflower Lemma of Erdos and Rado [11] together with several kinds of encoding arguments.

Using the neural network architecture of back propagation applied to speech recognition, a new input data structure is developed which improves shift invariance when recognizing signal data in the form such as a formant. The preliminary development of the data structure, piecewise linear regression vectors, is reported. The new input data structure reduces the amount of data presented to the network by as much as an order of magnitude, giving a computational advantage in execution speed.

We introduce a new family of compressed data structures to efficiently store and query large string dictionaries in main memory. Our main technique is a combination of hierarchical Front-coding with ideas from longest-common-prefix computation in suffix arrays. Our data structures yield relevant space-time tradeoffs in real-world dictionaries. We focus on two domains where string dictionaries are extensively used and efficient compression is required: URL collections, a key element in Web graphs and applications such as Web mining; and collections of URIs and literals, the basic components of RDF datasets. Our experiments show that our data structures achieve better compression than the state-of-the-art alternatives while providing very competitive query times.

Optimizing computational high-order schemes in finite volume simulations using unstructured mesh and topological data structures

SummaryExtensible Markup Language (XML) documents consist of text data plus structured data (markup). XPath allows to query both text and structure. Evaluating such hybrid queries is challenging. We present a system for in‐memory evaluation ofXPath search queries, that is, queries with text and structure predicates, yet without advanced features such as backward axes, arithmetics, and joins. We show that for this query fragment, which containsForward Core XPath, our system, dubbed Succinct XML Self‐Index (‘SXSI’), outperforms existing systems by 1–3 orders of magnitude. SXSI is based on state‐of‐the‐art indexes for text and structure data. It combines two novelties. On one hand, it represents the XML data in a compact indexed form, which allows it to handle larger collections in main memory while supporting powerful search and navigation operations over the text and the structure. On the other hand, it features an execution engine that uses tree automata and cleverly chooses evaluation orders that leverage the speeds of the respective indexes. SXSI is modular and allows seamless replacement of its indexes. This is demonstrated through experiments with (1) a text index specialized for search of bio sequences, and (2) a word‐based text index specialized for natural language search. Copyright © 2013 John Wiley & Sons, Ltd.

Faster proximity searching with the distal SAT