Download JAR files tagged by parallel with all dependencies

This module provides a very basic support for the parallel computing of tasks (Engine class) and entries of a matrix (MatrixEngine). This is basically just a wrapper around the java standard functionality for parallel computing (mainly the Standard Thread Pool and the Future interface). Additional functionality is provided by the ProgressReporter interface, which can be used as a hook to provide information on the current state of the parallel computing task to other modules or the user.

Allure Test Framework Core is responsible for: * Test report with logs and/or screenshots * Test groups/tags * Data Driven (inside test case, external file) * Test case parallel execution * BDD - Gherkin – Cucumber approach * Run on independent OperatingSystem * Externalize test environment (DEV, QA, PROD) * Adding analytics source code * Update Observers Based on Junit4.12

Stream processing like Java 8 streams, but works with Java 6 and have slightly different design. * Chicory-stream provides sequential-only streams, no parallel support. * Chicory-stream allows easy forking and reuse of streams, unlike Java 8 where stream can be used only once. * Chicory-stream automatically and transparently opens and closes resources each time stream is used, unlike Java 8 where stream should be manually closed. Part of chicory.

CTRL4TestNG provides an SLF4J Logger that can be used from any TestNG method tied to an ITestResult, namely Before, After, and Test annotations. Each separate method will have its own unique Logger and can be used with TestNG's parallel testing support making it clear which logs came from which method even when data-driven.

This package contains several example programs of the usage of the Chips-n-Salsa library. Chips-n-Salsa is a Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. Chips-n-Salsa's source code is maintained on GitHub, and the prebuilt jars of the library can be imported from Maven Central using maven or other build tools. The purpose of the package chips-n-salsa-examples is to demonstrate usage of the major functionality of the Chips-n-Salsa library. You can find out more about the Chips-n-Salsa library itself from its website: https://chips-n-salsa.cicirello.org/.

The Spark Universal Migrator is a data migration Spark application that fetches data from an Oracle database and stores it in a Hive database. It utilizes JDBC to connect to Oracle, processes and converts the data into a Hive-compatible schema, and then transfers it to Hive. The migration process employs iterators to generate queries for data retrieval and utilizes a BlockingQueue in conjunction with a ThreadPoolExecutor for parallel processing of data. Additionally, the application incorporates an Oracle connection pool to efficiently manage and reuse database connections, further optimizing performance and resource utilization during the migration process.

This small library provides an optimized solution to a problem where tasks for a particular key need to be processed sequentially as they arrive. This kind of problem can be solved by a SingleThreadExecutor, however it is not efficient. The issue is that the tasks for unrelated keys are not being processed in parallel, instead they are put into a queue common to all keys and wait for the single thread to execute them. This library allows them to be executed concurrently. Moreover this library works well in a situation where all the possible keys and their number is not known upfront.

Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. MultiBoosting can be viewed as combining AdaBoost with wagging. It is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction. Using C4.5 as the base learning algorithm, Multi-boosting is demonstrated to produce decision committees with lower error than either AdaBoost or wagging significantly more often than the reverse over a large representative cross-section of UCI data sets. It offers the further advantage over AdaBoost of suiting parallel execution. For more information, see Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).

Search methods contributed by Adrian Pino (ScatterSearchV1, TabuSearch). ScatterSearch: Performs an Scatter Search through the space of attribute subsets. Start with a population of many significants and diverses subset stops when the result is higher than a given treshold or there's not more improvement. For more information see: Felix Garcia Lopez (2004). Solving feature subset selection problem by a Parallel Scatter Search. Elsevier. Tabu Search: Abdel-Rahman Hedar, Jue Wangy, Masao Fukushima (2006). Tabu Search for Attribute Reduction in Rough Set Theory.

Stepping is a framework designed to ease the implementation of data processing solutions. In use cases where we need to implement data or data-streaming algorithms or any other processing on data, we need to first handle many different infrastructure issues. For example, we need to decide how to split the data processing logic into different steps, think about our threading policy, how to handle communication between the different steps, error handling etc. One of the most important subjects is the Threading Policy of our solution. For example, we need to think how many threads to open, have the option to distribute the processing of data to multiple 'executors' in parallel, have a thread-safe communication layer between the threads etc. On top of that we also care a lot about the performance of our solution, we want to make sure that the latency added by these infrastructures is minimal as possible. Stepping aims to handle many of these aspects so developers can spend their time on the business logic instead of solving these infrastructure and data flow issues issues over and over again.