Search Results for "cpu-g"
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Showing 57 open source projects for "cpu-g"
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CPU Info
CPU Info provides information about device hardware and software
CPU Info provides the main information about the hardware and software of your device. -
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Java Client for Google Maps Services
Java client library for Google Maps API Web Services
This library brings the Google Maps API Web Services to your server-side Java application. Each Google Maps Web Service request requires an API key. API keys are generated in the 'Credentials' page of the 'APIs & Services' tab of Google Cloud console. The Java Client for Google Maps Services is designed for use in both server and Android applications. In either case, it is important to add API key restrictions to improve the security of your API key. Additional security measures, such as... -
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Tracy Profiler
Frame profiler
A real-time, nanosecond resolution, remote telemetry, hybrid frame, and sampling profiler for games and other applications. Tracy supports profiling CPU (Direct support is provided for C, C++, Lua and Python integration. At the same time, third-party bindings to many other languages exist on the internet, such as Rust, Zig, C#, OCaml, Odin, etc.), GPU (All major graphic APIs: OpenGL, Vulkan, Direct3D 11/12, OpenCL.), memory allocations, locks, context switches, automatically attribute... -
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ncnn
High-performance neural network inference framework for mobile
ncnn is a high-performance neural network inference computing framework designed specifically for mobile platforms. It brings artificial intelligence right at your fingertips with no third-party dependencies, and speeds faster than all other known open source frameworks for mobile phone cpu. ncnn allows developers to easily deploy deep learning algorithm models to the mobile platform and create intelligent APPs. It is cross-platform and supports most commonly used CNN networks, including... -
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VulkanSceneGraph
Vulkan & C++17 based Scene Graph Project
VulkanSceneGraph (VSG), is a modern, cross-platform, high-performance scene graph library built upon Vulkan graphics/compute API. The software is written in C++17 and follows the CppCoreGuidelines and FOSS Best Practices. The source code is published under the MIT License, with the exception of vulkan.h, used for Vulkan extensions, which is under Apache License 2.0. This repository contains C++ headers and source and CMake build scripts to build the libvsg library. Additional support... -
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ArrayFire
ArrayFire, a general purpose GPU library
... interested and able to write top performing tensor functions. Together we can fulfill The ArrayFire Mission under an excellent Code of Conduct that promotes a respectful and friendly building experience. Rigorous benchmarks and tests ensuring top performance and numerical accuracy. Cross-platform compatibility with support for CUDA, OpenCL, and native CPU on Windows, Mac, and Linux. Built-in visualization functions through Forge. -
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Face Alignment
2D and 3D Face alignment library build using pytorch
Detect facial landmarks from Python using the world's most accurate face alignment network, capable of detecting points in both 2D and 3D coordinates. Build using FAN's state-of-the-art deep learning-based face alignment method. For numerical evaluations, it is highly recommended to use the lua version which uses identical models with the ones evaluated in the paper. More models will be added soon. By default, the package will use the SFD face detector. However, the users can alternatively... -
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Lightweight ring buffer manager
Lightweight generic ring buffer manager library
The library provides generic FIFO ring buffer implementation. -
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OpenBLAS
Optimized BLAS library based on GotoBLAS2 1.13 BSD version
OpenBLAS is an optimized BLAS library based on GotoBLAS2 1.13 BSD version. The BLAS (Basic Linear Algebra Subprograms) are routines that provide standard building blocks for performing basic vector and matrix operations. The Level 1 BLAS performs scalar, vector and vector-vector operations, the Level 2 BLAS performs matrix-vector operations, and the Level 3 BLAS performs matrix-matrix operations. Because the BLAS are efficient, portable, and widely available, they are commonly used in the... -
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Fast Android Networking
A Complete Fast Android Networking Library that also supports HTTP/2
... any type of file, uploading file, loading images from the network in ImageView, etc. There are some libraries but they are outdated. No other library provides a simple interface for doing all types of things in networking like setting priority, canceling, etc. As it uses Okio , No more GC overhead in android applications. Okio is made to handle GC overhead while allocating memory. Okio does some clever things to save CPU and memory. -
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DGL
Python package built to ease deep learning on graph
... want to make it easy to implement graph neural networks model family. We also want to make the combination of graph based modules and tensor based modules (PyTorch or MXNet) as smooth as possible. DGL provides a powerful graph object that can reside on either CPU or GPU. It bundles structural data as well as features for a better control. We provide a variety of functions for computing with graph objects including efficient and customizable message passing primitives for Graph Neural Networks. -
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Tiny CUDA Neural Networks
Lightning fast C++/CUDA neural network framework
This is a small, self-contained framework for training and querying neural networks. Most notably, it contains a lightning-fast "fully fused" multi-layer perceptron (technical paper), a versatile multiresolution hash encoding (technical paper), as well as support for various other input encodings, losses, and optimizers. We provide a sample application where an image function (x,y) -> (R,G,B) is learned. The fully fused MLP component of this framework requires a very large amount of shared... -
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JMX Exporter
A process for exposing JMX Beans via HTTP for Prometheus consumption
JMX to Prometheus exporter: a collector that can configurable scrape and expose mBeans of a JMX target. This exporter is intended to be run as a Java Agent, exposing a HTTP server and serving metrics of the local JVM. It can be also run as a standalone HTTP server and scrape remote JMX targets, but this has various disadvantages, such as being harder to configure and being unable to expose process metrics (e.g., memory and CPU usage). Running the exporter as a Java agent is strongly encouraged. -
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Android Contacts, Reborn
Android Contacts API Library written in Kotlin
This library provides a complete set of APIs to do everything you need with Contacts in Android. You no longer have to deal with the Contacts Provider, database operations, and cursors. Whether you just need to get all or some Contacts for a small part of your app (written in Kotlin or Java), or you are looking to create your own full-fledged Contacts app with the same capabilities as the AOSP Android Contacts app and Google Contacts app, this library is for you. -
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Binaryen
Compiler infrastructure and toolchain library for WebAssembly
... 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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Faiss
Library for efficient similarity search and clustering dense vectors
Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by Facebook AI Research. Faiss contains several methods for similarity search. It... -
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ChrysaLisp
Parallel OS, with GUI, Terminal, OO Assembler, Class libraries
ChrysaLisp is a 64-bit, MIMD, multi-CPU, multi-threaded, multi-core, multi-user parallel operating system with features such as a GUI, terminal, OO Assembler, class libraries, C-Script compiler, Lisp interpreter, debugger, profiler, vector font engine, and more. It supports MacOS, Windows, and Linux for x64, Riscv64, and Arm64 and eventually will move to bare metal. It also allows the modeling of various network topologies and the use of ChrysaLib hub nodes to join heterogeneous host networks... -
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Thread Affinity
Bind a java thread to a given core
Lets you bind a thread to a given core, this can improve performance (this library works best on linux). Java-Thread-Affinity will try to use JNA to provide access to native thread-handling functions. JNA should be installed on your system to get the most from this library. Java-Thread-Affinity currently depends on JNA version 4.4.0, which in turn depends on a version of GLIBC >= 2.14. If your operating system is an old one, with a version of GLIBC released before 2011, this library will not... -
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frugally-deep
A lightweight header-only library for using Keras (TensorFlow) models
Use Keras models in C++ with ease. A lightweight header-only library for using Keras (TensorFlow) models in C++. Works out-of-the-box also when compiled into a 32-bit executable. (Of course, 64 bit is fine too.) Avoids temporarily allocating (potentially large chunks of) additional RAM during convolutions (by not materializing the im2col input matrix). Utterly ignores even the most powerful GPU in your system and uses only one CPU core per prediction. Quite fast on one CPU core, and you can run... -
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CppServer
Fast and low latency asynchronous socket server & client C++ library
Ultra-fast and low latency asynchronous socket server & client C++ library with support TCP, SSL, UDP, HTTP, HTTPS, WebSocket protocols, and 10K connections problem solution. Cross platform (Linux, MacOS, Windows) Asynchronous communication. Supported CPU scalability designs: IO service per thread, thread pool. Supported transport protocols: TCP, SSL, UDP, UDP multicast. Supported Web protocols: HTTP, HTTPS, WebSocket, WebSocket secure. Supported Swagger OpenAPI iterative documentation... -
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CrossDB
Ultra High-performance Lightweight Embedded and Server OLTP RDBMS
CrossDB is a ultra high-performance, lightweight embedded and server OLTP RDBMS. It is designed for high-performance scenarios where the main memory can hold the entire database. -
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Bumblebee
Pre-trained Neural Network models in Axon
... is an optional dependency but an important one as it allows you to compile models just-in-time and run them on CPU/GPU. -
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LiteNetLib 1.0 indev
Lite reliable UDP library for Mono and .NET
Lite reliable UDP library for .NET Framework 3.5, Mono, .NET Core 2.0, .NET Standard 2.0. Small packet size overhead (1 byte for unreliable, 3 bytes for reliable packets). Helper classes for sending and reading messages. Always use library sources instead of precompiled DLL files (because there are platform-specific #ifdefs and workarounds for unity bugs). Fast packet serializer (Usage manual). Automatic small packets merging. Automatic fragmentation of reliable packets. Automatic MTU... -
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ProbabilisticCircuits.jl
Probabilistic Circuits from the Juice library
This module provides a Julia implementation of Probabilistic Circuits (PCs), tools to learn structure and parameters of PCs from data, and tools to do tractable exact inference with them. Probabilistic Circuits provides a unifying framework for several family of tractable probabilistic models. PCs are represented as computational graphs that define a joint probability distribution as recursive mixtures (sum units) and factorizations (product units) of simpler distributions (input units).... -
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AIMET
AIMET is a library that provides advanced quantization and compression
... accelerators. Quantized inference is significantly faster than floating point inference. For example, models that we’ve run on the Qualcomm® Hexagon™ DSP rather than on the Qualcomm® Kryo™ CPU have resulted in a 5x to 15x speedup. Plus, an 8-bit model also has a 4x smaller memory footprint relative to a 32-bit model. However, often when quantizing a machine learning model (e.g., from 32-bit floating point to an 8-bit fixed point value), the model accuracy is sacrificed.