#java #guide #java_8 #lambda_expressions #learning #parallel_streams #stream #tutorial
https://github.com/winterbe/java8-tutorial
https://github.com/winterbe/java8-tutorial
GitHub
GitHub - winterbe/java8-tutorial: Modern Java - A Guide to Java 8
Modern Java - A Guide to Java 8. Contribute to winterbe/java8-tutorial development by creating an account on GitHub.
#rust #hacktoberfest #javascript #javascript_linter #linter #parallel #parser
https://github.com/RDambrosio016/RSLint
https://github.com/RDambrosio016/RSLint
GitHub
GitHub - rslint/rslint: A (WIP) Extremely fast JavaScript and TypeScript linter and Rust crate
A (WIP) Extremely fast JavaScript and TypeScript linter and Rust crate - GitHub - rslint/rslint: A (WIP) Extremely fast JavaScript and TypeScript linter and Rust crate
#python #callcenter #conformer #ctc_decode #deepspeech #fastspeech2 #language_model #mandarin_language #ngram #parallel_wavegan #punctuation_restoration #speech_alignment #speech_recognition #speech_to_text #speech_translation #streaming_asr #text_frontend #text_to_speech #transformer
https://github.com/PaddlePaddle/PaddleSpeech
https://github.com/PaddlePaddle/PaddleSpeech
GitHub
GitHub - PaddlePaddle/PaddleSpeech: Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with…
Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text frontend, Speaker Verification System, End-to-End Speech Translatio...
#cplusplus #compiler #gpu_programming #high_performance #llvm #parallel_programming #python
https://github.com/exaloop/codon
https://github.com/exaloop/codon
GitHub
GitHub - exaloop/codon: A high-performance, zero-overhead, extensible Python compiler with built-in NumPy support
A high-performance, zero-overhead, extensible Python compiler with built-in NumPy support - exaloop/codon
#cplusplus #abstraction #c_plus_plus #high_performance_computing #hpsf #kokkos #parallel_computing #programming_model
Kokkos is a tool that helps you write programs that run fast on many different computer systems. It works with various programming models like CUDA, HIP, and OpenMP, making it easy to use on different hardware. This means your programs can be efficient and work well on complex computer architectures. To get started, you can watch video lectures, read the programming guide, and look at examples. You can download Kokkos from GitHub or install it using tools like Spack. The benefit is that your programs will be faster and more flexible, working well on various high-performance computing platforms.
https://github.com/kokkos/kokkos
Kokkos is a tool that helps you write programs that run fast on many different computer systems. It works with various programming models like CUDA, HIP, and OpenMP, making it easy to use on different hardware. This means your programs can be efficient and work well on complex computer architectures. To get started, you can watch video lectures, read the programming guide, and look at examples. You can download Kokkos from GitHub or install it using tools like Spack. The benefit is that your programs will be faster and more flexible, working well on various high-performance computing platforms.
https://github.com/kokkos/kokkos
GitHub
GitHub - kokkos/kokkos: Kokkos C++ Performance Portability Programming Ecosystem: The Programming Model - Parallel Execution and…
Kokkos C++ Performance Portability Programming Ecosystem: The Programming Model - Parallel Execution and Memory Abstraction - kokkos/kokkos
#typescript #android #appium #appium_device_farm #appium_plugin #device_farm #driver_session #ios_simulators #parallel_testing #plugin #polling
Appium Device-farm is a tool that helps developers and testers automate testing on many different devices, like Android and iOS phones. It allows teams to test apps remotely without needing physical devices, which saves time and money. The tool also lets you run tests automatically across multiple devices at once, making it faster to find and fix problems. Additionally, it integrates well with continuous integration workflows, helping catch bugs early in development. This makes the testing process more efficient and reliable, ensuring apps work well on various devices before they are released.
https://github.com/AppiumTestDistribution/appium-device-farm
Appium Device-farm is a tool that helps developers and testers automate testing on many different devices, like Android and iOS phones. It allows teams to test apps remotely without needing physical devices, which saves time and money. The tool also lets you run tests automatically across multiple devices at once, making it faster to find and fix problems. Additionally, it integrates well with continuous integration workflows, helping catch bugs early in development. This makes the testing process more efficient and reliable, ensuring apps work well on various devices before they are released.
https://github.com/AppiumTestDistribution/appium-device-farm
GitHub
GitHub - AppiumTestDistribution/appium-device-farm: This is an Appium 2.0 plugin designed to manage and create driver sessions…
This is an Appium 2.0 plugin designed to manage and create driver sessions on available devices. - AppiumTestDistribution/appium-device-farm
#cplusplus #high_performance #interior_point_method #linear_optimization #mixed_integer_programming #parallel #quadratic_programming #simplex
HiGHS is a free, high-performance software that solves large and complex optimization problems like linear, quadratic, and mixed-integer programming. It works fast on many computers, including Linux, MacOS, and Windows, without needing extra software. You can use it through various programming languages like Python, C, C#, and Fortran, making it easy to integrate into your projects. HiGHS supports both serial and parallel computing, and it is advancing GPU acceleration for even faster solutions. This helps you efficiently find the best solutions for planning, scheduling, and decision-making problems in science, engineering, and business. Installation is straightforward, and detailed documentation is available to guide you[1][2][3][4].
https://github.com/ERGO-Code/HiGHS
HiGHS is a free, high-performance software that solves large and complex optimization problems like linear, quadratic, and mixed-integer programming. It works fast on many computers, including Linux, MacOS, and Windows, without needing extra software. You can use it through various programming languages like Python, C, C#, and Fortran, making it easy to integrate into your projects. HiGHS supports both serial and parallel computing, and it is advancing GPU acceleration for even faster solutions. This helps you efficiently find the best solutions for planning, scheduling, and decision-making problems in science, engineering, and business. Installation is straightforward, and detailed documentation is available to guide you[1][2][3][4].
https://github.com/ERGO-Code/HiGHS
GitHub
GitHub - ERGO-Code/HiGHS: Linear optimization software
Linear optimization software. Contribute to ERGO-Code/HiGHS development by creating an account on GitHub.