Search Results for "parallel-sudoku-solver" - Page 27
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Showing 1929 open source projects for "parallel-sudoku-solver"
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Bzip2
Parallel implementation of Bzip2 for multi-core machines written in Go
Parallel implementation of Bzip2 for modern multi-core machines written in Go. This project is licensed under the ISC License. Copyright © 2020-2021 ALBANESE Research Lab Source code: https://github.com/pedroalbanese/bzip2 Visit: http://albanese.atwebpages.com -
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Gzip
Parallel implementation of Gzip for multi-core machines written in Go
Parallel implementation of Gzip for modern multi-core machines written in Go. This project is licensed under the ISC License. Copyright © 2020-2021 ALBANESE Research Lab Source code: https://github.com/pedroalbanese/gzip Visit: http://albanese.atwebpages.com -
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XZ
Multi-core, cross-platform XZ compression tool written in Go
Parallel implementation of XZ for modern multi-core machines written in Go. This project is licensed under the ISC License. Copyright © 2020-2021 ALBANESE Research Lab Source code: https://github.com/pedroalbanese/xz-2 Visit: http://albanese.atwebpages.com -
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pymp - Python music player Start flags: -l --loop -v --volume <volume> -p --parallel <file1> <file2>
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5
The Practice of Parallel Programming
A book on parallel programming and a C++ library
An online edition of the book "The Practice of Parallel Programming" and examples from it. The examples include a parallel programming framework of production quality, that has been used in another project, Triceps. -
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SoundHelix
Versatile algorithmic random music generation software written in Java
SoundHelix is a free versatile Java framework for algorithmic random music composition. SoundHelix can generate random songs, play them (for example, on any number of MIDI devices or MIDI-capable software synthesizers) and is highly customizable using XML configuration. SoundHelix also allows writing the generated music as MIDI files. SoundHelix produces music that is actually enjoyable to listen to. -
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PeMSyn
Matlab/Scilab-FEMM based GUI to Assist the Design of Permanent Magnet
Release: PeMSyn 2.4 - 03.Sep.21 Requirements: - Matlab 2018a or later (with Parallel Computing Toolbox ) - Scilab 6.1.0 or later - FEMM 4.2 (21Apr2019 or later) Matlab/Scilab-FEMM based GUI to Assist the Design of Permanent Magnet Machines: - Surface Mounted PMSM: Inner Rotor / Outer Rotor - Inset Mounted PMSM: Inner Rotor / Outer Rotor - Spoke-Type PMSM: Inner Rotor - Coreless Machine - Different Slot types available - Permeance Coefficient Assistant --- No-Load and On-Load estimation for PC during the design process - Winding Wizard Assistant - Performance Simulator (FEMM Based) --- No-Load and On-Load parameters assessment considering armature reaction and saturation effects --- Torque Curve Capability assessment --- Parallel Processing (Matlab only) TUTORIAL included! ... -
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GoSpider
Gospider - Fast web spider written in Go
...Find subdomains from the response source. Get URLs from Wayback Machine, Common Crawl, Virus Total, Alien Vault. Format output easy to Grep. Support Burp input. Crawl multiple sites in parallel. -
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distmake is a distributed, parallel, GNU make-compatible make program. It reads a Makefile and distributes builds targets over a set of build servers on a network
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10
scikit-opt
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing
scikit-opt is a Python library for solving optimization problems with evolutionary and swarm-intelligence algorithms. It includes genetic algorithms, particle swarm optimization, differential evolution, simulated annealing, ant colony optimization, immune algorithms, and artificial fish swarms. The package can address continuous objectives, constrained problems, and combinatorial tasks such as the traveling salesman problem. A consistent workflow lets users define an objective, configure an... -
11
VITS
Conditional Variational Autoencoder with Adversarial Learning
...Unlike traditional two-stage systems that separately train an acoustic model and a vocoder, VITS trains an end-to-end model that maps text directly to waveform using a conditional variational autoencoder combined with normalizing flows and adversarial training. This architecture enables parallel generation (fast inference) while achieving speech quality that rivals or surpasses many two-stage systems. The repository provides training and inference pipelines for common datasets such as LJ Speech (single-speaker) and VCTK (multi-speaker), including filelists, configs, and preprocessing scripts. It also includes monotonic alignment search code and g2p preprocessing, which are crucial components for aligning text and speech in an end-to-end setup. -
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streamsupport
Backport of Java 8/9 java.util.stream API to Java 6/7 and Android
This project has moved to GitHub: https://github.com/stefan-zobel/streamsupport streamsupport is a backport of the Java 8 java.util.function (functional interfaces) and java.util.stream (streams) API for users of Java 6 or 7 supplemented with selected additions from java.util.concurrent which didn't exist back in Java 6. Due to the lack of default interface methods and static interface methods in pre-Java 8 the API had to be slightly adjusted in these areas but still covers the full... -
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College Time Table (CTT)
A small and powerful utility to generate school/college time table.
Most of the time table applications use complex design to generate time table but as a result often lose simplicity. This software uses the most basic, simple and popular Spreadsheet design. This small desktop application can save hundreds of hours of teachers of any small school or college. It will never ask information required but collects information on the fly as you type. Start building time table instantly as you recollect names, subjects etc. Separate executables for Windows... -
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Transformer TTS
Implementation of a Transformer based neural network
...This design addresses common autoregressive issues such as repetition, skipped words, and unstable attention, and results in robust, fast synthesis where all frames are predicted in parallel. The repository ships with tooling to build datasets (especially LJSpeech) and create training data, plus scripts to train both the aligner and the TTS model, monitor training with TensorBoard, and resume or reset training runs. -
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FARM
Fast & easy transfer learning for NLP
...With FARM you can build fast proofs-of-concept for tasks like text classification, NER or question answering and transfer them easily into production. Easy fine-tuning of language models to your task and domain language. AMP optimizers (~35% faster) and parallel preprocessing (16 CPU cores => ~16x faster). Modular design of language models and prediction heads. Switch between heads or combine them for multitask learning. Full Compatibility with HuggingFace Transformers' models and model hub. Smooth upgrading to newer language models. Integration of custom datasets via Processor class. Powerful experiment tracking & execution. -
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Amadeus
Harmonious distributed data analysis in Rust
Amadeus is a high-performance, distributed data processing framework written in Rust, designed to offer an ergonomic and safe alternative to tools like Apache Spark. It provides both streaming and batch capabilities, allowing users to work with real-time and historical data at scale. Thanks to Rust’s memory safety and zero-cost abstractions, Amadeus delivers performance gains while reducing the complexity and bugs common in large-scale data pipelines. It emphasizes developer productivity... -
17
Precision_trimmer
Precision resistance value trim circuit using standard parts.
By using standard precision resistors in parallel and series with standard 1, 2, 5 multiple potentiometers, a precision, narrow range resistance trimmer can be created. Fixed resistors are more stable than potentiometers and the circuit reduces the effects of the potentiometer while also determining a precise range. For convenience, a Python script is used to assist in part selection. -
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LBRYcrd
The blockchain that provides the digital content namespace for LBRY
...The most salient feature of the LBRY blockchain is the association of a normalized string of characters (a “name”) with a structured set of metadata. This coupling is called a claim. The LBRY blockchain stores names and metadata in a parallel Merkle tree, separate from the tree used to store transaction data. This allows LBRY URLs to be trustfully resolved even without a full copy of the blockchain. -
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Hydra
Lightweight full-featured Promises, Async & Await Library in Swift
Hydra is a lightweight Swift library for structuring asynchronous code around strongly typed promises. It lets developers chain dependent operations while handling completion and errors through unified flows instead of nested callbacks. Its async and await helpers allow asynchronous tasks to be written in a more sequential style. Promises can be cancelled through invalidation tokens and executed on configurable Grand Central Dispatch queues. Operators include timeout, retry, recover,... -
20
PyBitBanger
Python controlled hardware.
With the demise of the parallel port, a simple way for a Python script to control external hardware has disappeared. The goal of this project is to use relatively simple hardware like an Arduino Nano and the Microchip MCP23S17 port expander as an extensible bit banger. Ideally, all hardware should be leaded and breadboard friendly for easy project prototyping. -
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Pushloc
A wireless push-type network simulator.
A wireless push-type network simulator that considers locality of demand for performance improvement. The network environment consists of multiple directed emitters for parallel data broadcasting. Performance depends on the mean response time of clients. -
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YOLO ROS
YOLO ROS: Real-Time Object Detection for ROS
...Darknet on the CPU is fast (approximately 1.5 seconds on an Intel Core i7-6700HQ CPU @ 2.60GHz × 8) but it's like 500 times faster on GPU! You'll have to have an Nvidia GPU and you'll have to install CUDA. The CMakeLists.txt file automatically detects if you have CUDA installed or not. CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. -
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XLM (Cross-lingual Language Model)
PyTorch original implementation of Cross-lingual Language Model
XLM (Cross-lingual Language Model) is a family of multilingual pretraining methods that align representations across languages to enable strong zero-shot transfer. It popularized objectives like Masked Language Modeling (MLM) across many languages and Translation Language Modeling (TLM) that jointly trains on parallel sentence pairs to tighten cross-lingual alignment. Using a shared subword vocabulary, XLM learns language-agnostic features that work well for classification and sequence labeling tasks such as XNLI, NER, and POS without target-language supervision. The repository provides preprocessing pipelines, training code, and fine-tuning scripts so you can reproduce benchmark results or adapt models to your own multilingual corpora. ... -
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Lightweight-Stream-API
Stream API from Java 8 rewritten on iterators for Java 7 and below
Stream API from Java 8 rewritten on iterators for Java 7 and below. -
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WRF_ELEC
Storm electrification package for the WRF-ARW
...It contains modules for the lightning discharge scheme, the screening layer parameterization, communications, and include code that calls these subroutines. Note that the required elliptic equation solver is a separate download from <http://sourceforge.net/projects/boxmg4wrf>
