Search Results for "cpu performance meter" - Page 13
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Showing 405 open source projects for "cpu performance meter"
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pg_flame
A flamegraph generator for Postgres EXPLAIN ANALYZE output
...The pg_flame program reads a JSON query plan from standard input and writes the flamegraph HTML to standard output. Therefore you can pipe and direct input and output however you desire. Flamegraphs were invented by Brendan Gregg to visualize CPU consumption per code-path of profiled software. They are useful visualization tools in many types of performance investigations. Flamegraphs have been used to visualize Oracle database query plans and query executions, proving useful for debugging slow database queries. Pg_flame is an extension of that work for Postgres query plans. ... -
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Cuberite
A lightweight, fast and extensible game server for Minecraft
Cuberite is a lightweight, open-source Minecraft-compatible multiplayer server implementation written primarily in C++. The project was created as an alternative to the official Java-based server software with the goal of improving performance, efficiency, and flexibility. By using C++ instead of Java, Cuberite reduces memory consumption and CPU overhead while still allowing players using the standard Minecraft Java Edition client to connect and play. The software recreates core gameplay systems such as world generation, multiplayer networking, and commands while allowing extensive customization through a plugin system. ... -
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Appmetrics
Node Application Metrics provides a foundational infrastructure
AppMetrics is a monitoring library for Node.js and Java applications, providing insights into application performance and resource usage. -
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Microjob
Turn Node.js worker threads into easy-to-use routines
A lightweight Node.js library for running CPU-bound tasks in worker threads, enabling better parallel execution. -
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AutoBench
This program is a benchmark site data extraction util program
This program is a program that extracts the latest CPU, GPU, Drive and RAM performance scores and rankings from benchmark sites. The Output Data is saved as a csv, xlsx and xls file. CPU information is written by model name and score. GPU information is written by model name and score. Drive information is written by model name and score. RAM information is written by model name and score. -
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XMRig AMD
Monero AMD (OpenCL) miner
XMRig is a high-performance Monero (XMR) OpenCL miner, with the official full Windows support. -
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node-in-debugging
Node.js Debugging Guide
Node.js Debugging Guide is a Chinese-language technical book and repository about diagnosing performance, memory, code, and production problems in Node.js. Its CPU section introduces Linux perf, FlameGraph, V8 profiling, and the Tick Processor. Memory chapters cover core dumps, llnode, heap snapshots, leak detection, and CPU or memory monitoring. Code-level material examines promises, async and await, stack traces, the event loop, native Rust add-ons, and uncaught exceptions. ... -
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GPUImage 2
Framework for GPU-accelerated video and image processing
...The objective of the framework is to make it as easy as possible to set up and perform realtime video processing or machine vision against image or video sources. By relying on the GPU to run these operations, performance improvements of 100X or more over CPU-bound code can be realized. This is particularly noticeable in mobile or embedded devices. On an iPhone 4S, this framework can easily process 1080p video at over 60 FPS. On a Raspberry Pi 3, it can perform Sobel edge detection on live 720p video at over 20 FPS. -
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Zucc Kornel
A Kernel for Android
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cpuplot
Command Line profiler of CPU usage
cpuplot Command Line Profiler of CPU Usage and Load Average. Synopsis: BASH script that shows the history of CPU usage, and what procces uses the most CPU at a given time. Bar graphs represent the percentage of Total CPU Usage, and the percentage of Total 1 Minute Load Average. Default wait interval between system readings is 5secs. But a different one can be set in the command line with the "-i" argument. Default output is formatted for the standard console width of 80... -
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Simd
High performance image processing library in C++
The Simd Library is a free open source image processing library, designed for C and C++ programmers. It provides many useful high performance algorithms for image processing such as: pixel format conversion, image scaling and filtration, extraction of statistic information from images, motion detection, object detection (HAAR and LBP classifier cascades) and classification, neural network. The algorithms are optimized with using of different SIMD CPU extensions. -
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face-recognition.js
Simple Node.js package for robust face detection and face recognition
...Synchronous and Promise-based asynchronous methods support different application designs, while OpenCV integration enables additional image processing. OpenBLAS can improve CPU performance when the native dependency is compiled with it. The project is now considered obsolete by its author, who recommends face-api.js for equivalent browser and Node.js functionality. -
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PumpedUp
Performance monitoring and logging software for Linux and Windows.
PumpedUp gathers system performance data on a second by second basis and creates real time or static graphical web pages. It can quickly point out maximized resources on a system. It also has options for sending data to Redis / Logstash for analysis, or Nagios for alarming. PumpedUp runs an insecure web server on port 8080 by default and requires you to provide your own security for that port. You can disable this functionality by editing the PumpedUp configuration file. After... -
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RpiOptimisation
Performance optimization tool for Raspberry Pi.
Support : [Raspberry Pi 0-1-2-3] [EN] Performance optimization tool for Raspberry Pi (CPU,GPU,SD card read and write speed) by model. [FR] Outil d'optimisation des performances pour Raspberry Pi (CPU, GPU, vitesse de lecture et d'écriture de la carte SD) par modèle. -
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Fedora Kernel Netbook
Linux Kernel optimized for Intel Atom CPU on Fedora
Linux Kernel compiled for Netbook (Intel Atom CPU) for Fedora distribution get Performance. https://github.com/lozanotux/fedora-kernel-netbook -
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Napa.js
Napa.js, a multi-threaded JavaScript runtime
Napa.js is a multi-threaded JavaScript runtime built on V8, which was originally designed to develop highly iterative services with non-compromised performance in Bing. As it evolves, we find it useful to complement Node.js in CPU-bound tasks, with the capability of executing JavaScript in multiple V8 isolates and communicating between them. Napa.js is exposed as a Node.js module, while it can also be embedded in a host process without Node.js dependency. Multi-threaded JavaScript runtime. ... -
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TCPCopy
An online request replication tool, also a tcp stream replay tool
TCPCopy is a TCP stream replay tool to support real testing of Internet server applications. Although the real live flow is important for the test of Internet server applications, it is hard to simulate it as online environments are too complex. To support more realistic testing of Internet server applications, we develop a live flow reproduction tool - TCPCopy, which could generate the test workload that is similar to the production workload. Currently, TCPCopy has been widely used by... -
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Java app. to utilise (waste) as much CPU as it can. The application allows the user to create multiple Java threads, each performing a CPU-intensive task tasks with a selectable memory footprint. The options let you 'thrash' the aspect you want.
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Mocha.jl
Deep Learning framework for Julia
Mocha.jl is a deep learning framework for Julia, inspired by the C++ Caffe framework. It offers efficient implementations of gradient descent solvers and common neural network layers, supports optional unsupervised pre-training, and allows switching to a GPU backend for accelerated performance. The development of Mocha.jl happens in relative early days of Julia. Now that both Julia and the ecosystem has evolved significantly, and with some exciting new tech such as writing GPU kernels... -
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Bullet
JVM Bullet Physics SDK
...A high-end desktop GPU, such as an AMD Radeon 7970 or NVIDIA GTX 680 or better. We succesfully tested the software under Windows, Linux and Mac OSX. The software currently doesn't work on OpenCL CPU devices. It might run on a laptop GPU but performance will not likely be very good. Note that often an OpenCL drivers fails to compile a kernel. Some unit tests exist to track down the issue, but more work is required to cover all OpenCL kernels. -
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swarmprom
Docker Swarm instrumentation with Prometheus, Grafana, cAdvisor
...It collects host, container, and Docker daemon metrics through Node Exporter, cAdvisor, and a dedicated daemon exporter. Grafana dashboards display cluster capacity, node health, service tasks, CPU, memory, storage, network traffic, I/O, and Prometheus performance. Alertmanager distributes alerts, Unsee provides an alert dashboard, and optional Slack settings deliver notifications to a channel. Global services and Docker DNS discovery automatically cover exporter instances added with new nodes. The project is intended as a foundation that teams can extend and harden for their own production environments. -
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Simple program that displays information about CUDA-enabled devices. The program is equipped with GPU performance test.
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ptbench
CPU and RAM stressing and monitoring
This package provides you 2 C++ applications to stress and monitor your CPU and RAM under Linux. Find a lack of performance (not enough cores/threads or too little clock speed) and memory/resource leaks or a lack of RAM. -
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JPFPSStatus
Show FPS Status on StatusBar
JPFPSStatus is a simple and lightweight tool that displays the frames per second (FPS) of an iOS app in real-time. Designed for performance debugging and optimization, it provides a floating overlay that shows the current FPS, helping developers monitor app responsiveness and rendering performance. -
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NNVM
Open deep learning compiler stack for cpu, gpu
The vision of the Apache NNVM Project is to host a diverse community of experts and practitioners in machine learning, compilers, and systems architecture to build an accessible, extensible, and automated open-source framework that optimizes current and emerging machine learning models for any hardware platform. Compilation of deep learning models into minimum deployable modules. Infrastructure to automatically generates and optimize models on more backend with better performance....
