Search Results for "cpu speed"
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Showing 20 open source projects for "cpu speed"
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Hot
Hot is macOS menu bar application that displays the CPU speed limit
Hot is macOS menu bar application that displays the CPU speed limit due to thermal issues. CPU throttling is a power management technique in computer architecture whereby the frequency of a microprocessor can be automatically adjusted "on the fly" depending on the actual need, to conserve power and reduce the amount of heat generated by the chip. -
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Stats
macOS system monitor in your menu bar
Stats currently supported on macOS 10.13 (High Sierra) and higher. Stats is an application that allows you to monitor your macOS system. CPU utilization, GPU utilization, memory usage, disk utilization, sensors information (Temperature/Voltage/Power), battery level, network usage, fans speed, fan control, and Bluetooth devices. Supports many languages, such as English, Polski, Українська, Русский, and many more. You can help by adding a new language or improve existing translation. -
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CEmu emulator
Third-party TI-84 Plus CE / TI-83 Premium CE emulator
Developer-oriented emulator of the eZ80-based TI-84 Plus CE / TI-83 Premium CE calculators. CEmu is a third-party TI-84 Plus CE / TI-83 Premium CE calculator emulator, focused on developer features. The core is programmed in C and the GUI in C++ with Qt, for performance and portability reasons. CEmu works natively on Windows, macOS, and Linux! Easy setup - get running by doing a one-time-only connection of your calculator! Accurate and fast emulation. Customizable speed/throttling. Resizable... -
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go-web-framework-benchmark
Go web framework benchmark
go-web-framework-benchmark is a benchmark suite for comparing the performance of Go web frameworks. It was created to measure full HTTP request processing instead of only route matching speed. The project runs small real HTTP servers for many stable frameworks and tests a simple /hello endpoint. It can add configurable handler delay to simulate business work such as database calls, cache access, disk writes, socket activity, or microservice calls. The benchmark uses wrk and scripts to collect throughput, latency, allocation, concurrency, pipelining, and CPU-bound results. ... -
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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Binaryen
Compiler infrastructure and toolchain library for WebAssembly
...It accepts input in WebAssembly-like form but also accepts a general control flow graph for compilers that prefer that. Binaryen's internal IR uses compact data structures and is designed for completely parallel codegen and optimization, using all available CPU cores. Binaryen's IR also compiles down to WebAssembly extremely easily and quickly because it is essentially a subset of WebAssembly. Binaryen's optimizer has many passes (see an overview later down) that can improve code size and speed. These optimizations aim to make Binaryen powerful enough to be used as a compiler backend by itself. -
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Shumai
Fast Differentiable Tensor Library in JavaScript & TypeScript with Bun
...It allows seamless integration of machine learning, deep learning, and custom differentiable programs into web-based or server-side environments without relying on Python frameworks. The library supports matrix operations, gradient computation, and tensor conversions with intuitive APIs and near-native speed, thanks to Bun’s low-overhead FFI bindings. It can automatically leverage GPU acceleration on Linux (via CUDA) and CPU computation on macOS. -
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PyTorch Lightning
The lightweight PyTorch wrapper for high-performance AI research
Scale your models, not your boilerplate with PyTorch Lightning! PyTorch Lightning is the ultimate PyTorch research framework that allows you to focus on the research while it takes care of everything else. It's designed to decouple the science from the engineering in your PyTorch code, simplifying complex network coding and giving you maximum flexibility. PyTorch Lightning can be used for just about any type of research, and was built for the fast inference needed in AI research and... -
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3D Box rotation
Simple example of draw and rotate 3D box
Simple source .java file; .bat for fast re-compile and run; and pre-compiled .jar Java program with example from scratch writed in Notepad++ without Eclipse, etc., How to draw and rotate 3D box most simple way. Rotation speed regulated in simple Loop with 10 ms sleep. Use Java version 8 (OpenJDK 8, OracleJDK 8, OracleJRE 8, ..). Higher versions have an anti-aliasing error in the BufferedImage ( Windows 10 ). Python version with tkinter and math imports. Including calculated faces, moving lights and shadows only with CPU. -
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MegEngine
Easy-to-use deep learning framework with 3 key features
...You can represent quantization/dynamic shape/image pre-processing and even derivation in one model. After training, just put everything into your model and inference it on any platform at ease. Speed and precision problems won't bother you anymore due to the same core inside. In training, GPU memory usage could go down to one-third at the cost of only one additional line, which enables the DTR algorithm. Gain the lowest memory usage when inferencing a model by leveraging our unique pushdown memory planner. NOTE: MegEngine now supports Python installation on Linux-64bit/Windows-64bit/MacOS(CPU-Only)-10.14+/Android 7+(CPU-Only) platforms with Python from 3.5 to 3.8. ... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Bandicoot
fast C++ library for GPU linear algebra & scientific computing
* Fast GPU linear algebra library (matrix maths) for the C++ language, aiming towards a good balance between speed and ease of use * Provides high-level syntax and functionality deliberately similar to Matlab * Provides an API that is aiming to be compatible with Armadillo for easy transition between CPU and GPU linear algebra code * Useful for algorithm development directly in C++, or quick conversion of research code into production environments * Distributed under the permissive Apache 2.0 license, useful for both open-source and proprietary (closed-source) software * Can be used for machine learning, pattern recognition, computer vision, signal processing, bioinformatics, statistics, finance, etc * Downloads: http://coot.sourceforge.io/download.html * Documentation: http://coot.sourceforge.io/docs.html * Bug reports: http://coot.sourceforge.io/faq.html * Git repo: https://gitlab.com/conradsnicta/bandicoot-code -
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HighwayHash
Fast strong hash functions: SipHash/HighwayHash
...Although not a replacement for collision-resistant digests like SHA-2/3, it strikes a pragmatic balance of speed, simplicity, and resistance to common abuse patterns seen in production backends. -
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Neural Tangents
Fast and Easy Infinite Neural Networks in Python
Neural Tangents is a high-level neural network API for specifying complex, hierarchical models at both finite and infinite width, built in Python on top of JAX and XLA. It lets researchers define architectures from familiar building blocks—convolutions, pooling, residual connections, and nonlinearities—and obtain not only the finite network but also the corresponding Gaussian Process (GP) kernel of its infinite-width limit. With a single specification, you can compute NNGP and NTK kernels,... -
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Execute JavaScript from Go
Execute JavaScript from Go
Execute JavaScript from Go. For scripts that are large or are repeatedly run in different contexts, it is beneficial to compile the script once and used the cached data from that compilation to avoid recompiling every time you want to run it. The v8go library would welcome contributions from anyone able to get an external windows build of the V8 library linking with v8go, using the version of V8 checked out in the deps/v8 git submodule, and documentation of the process involved. This process... -
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CityHash
Automatically exported from Google code CityHash
...CityHash has been rigorously tested using tools like SMHasher to ensure high-quality mixing and collision resistance across a wide range of inputs. Its speed and portability have made it a popular choice for developers needing dependable, lightweight hash functions. -
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speedtest-cli
Command line interface for testing internet bandwidth using speedtest
Command line interface for testing internet bandwidth using speedtest.net. It is not a goal of this application to be a reliable latency reporting tool. Latency reported by this tool should not be relied on as a value indicative of ICMP style latency. It is a relative value used for determining the lowest latency server for performing the actual speed test against. Speedtest CLI brings the trusted technology and global server network behind Speedtest to the command line. Measure internet... -
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TFLearn
Deep learning library featuring a higher-level API for TensorFlow
...Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs, and optimizers. Easy and beautiful graph visualization, with details about weights, gradients, activations, and more. Effortless device placement for using multiple CPU/GPU. The high-level API currently supports the most of the recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, etc. -
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Zopfli
Zopfli Compression Algorithm is a compression library
...The codebase includes both a reusable library and ready-to-use CLI tools for bulk optimization in build pipelines. It is frequently used offline—e.g., as a final step in release builds—because decode speed remains normal while files get smaller. -
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Caffe2
Caffe2 is a lightweight, modular, and scalable deep learning framework
Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind. Caffe2 is a deep learning framework that provides an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform... -
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This project is a Neural Network Training Library implemented on CUDA. It's compatible with the most used libraries but allows to exploit the full power of NVIDIA graphic cards. Experimental results show speed ups over 100 times against CPU libraries
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A C compiler that easily ports to any microprocessor. The compiler has the goals, in order of importance, of portability (in both source code and cpu target), small code size, simplicity, and execution speed. It is quite useful for microcontrollers.
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