Download JAR files tagged by threads with all dependencies

日志模块，集成log4j2, 提供工具类可静态打印日志, 业务可import通用日志配置(中间件配置和业务基础配置)开箱即用， 另外还提供http接口出入参打印和rpc提供者和消费者的出入参打印(有开关可以热启动和热关闭，同时可在线修改日志级别)， traceId在日志中进行输出以及普通文件日志之外的结构化日志( 供运维采集分析可视化)，提供全链路信息（包括tid）跨线程跨线程池的能力 Log module, integrates log4j2, provides tool classes that can print logs statically, business can import common log configurations (middleware configuration and business basic configuration) out of the box, and also provides http interface input and output parameter printing and rpc providers and consumers. Input and output parameter printing (there is a switch for hot start and hot shutdown, and the log level can be modified online), traceId is output in the log and structured logs other than ordinary file logs (for operation and maintenance collection and analysis visualization). The ability to provide full link information (including tid) across threads across thread pools.

JTransforms is the first, open source, multithreaded FFT library written in pure Java. Currently, four types of transforms are available: Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete Sine Transform (DST) and Discrete Hartley Transform (DHT). The code is derived from General Purpose FFT Package written by Takuya Ooura and from Java FFTPack written by Baoshe Zhang. This version has been modified to daemonize threads and stop any application using the library waiting after execution has finished, and is based on revision 29 of the svn version of the code from 2014-05-18.

This package contains a classifier that can be used to train a two-class kernel logistic regression model with the kernel functions that are available in WEKA. It optimises the negative log-likelihood with a quadratic penalty. Both, BFGS and conjugate gradient descent, are available as optimisation methods, but the former is normally faster. It is possible to use multiple threads, but the speed-up is generally very marginal when used with BFGS optimisation. With conjugate gradient descent optimisation, greater speed-ups can be achieved when using multiple threads. With the default kernel, the dot product kernel, this method produces results that are close to identical to those obtained using standard logistic regression in WEKA, provided a sufficiently large value for the parameter determining the size of the quadratic penalty is used in both cases.

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.

Chips-n-Salsa is a Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. The library includes implementations of several stochastic local search algorithms, including simulated annealing, hill climbers, as well as constructive search algorithms such as stochastic sampling. Chips-n-Salsa now also includes genetic algorithms as well as evolutionary algorithms more generally. The library very extensively supports simulated annealing. It includes several classes for representing solutions to a variety of optimization problems. For example, the library includes a BitVector class that implements vectors of bits, as well as classes for representing solutions to problems where we are searching for an optimal vector of integers or reals. For each of the built-in representations, the library provides the most common mutation operators for generating random neighbors of candidate solutions, as well as common crossover operators for use with evolutionary algorithms. Additionally, the library provides extensive support for permutation optimization problems, including implementations of many different mutation operators for permutations, and utilizing the efficiently implemented Permutation class of the JavaPermutationTools (JPT) library. Chips-n-Salsa is customizable, making extensive use of Java's generic types, enabling using the library to optimize other types of representations beyond what is provided in the library. It is hybridizable, providing support for integrating multiple forms of local search (e.g., using a hill climber on a solution generated by simulated annealing), creating hybrid mutation operators (e.g., local search using multiple mutation operators), as well as support for running more than one type of search for the same problem concurrently using multiple threads as a form of algorithm portfolio. Chips-n-Salsa is iterative, with support for multistart metaheuristics, including implementations of several restart schedules for varying the run lengths across the restarts. It also supports parallel execution of multiple instances of the same, or different, stochastic local search algorithms for an instance of a problem to accelerate the search process. The library supports self-adaptive search in a variety of ways, such as including implementations of adaptive annealing schedules for simulated annealing, such as the Modified Lam schedule, implementations of the simpler annealing schedules but which self-tune the initial temperature and other parameters, and restart schedules that adapt to run length.