Showing 4 open source projects for "cpu"

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  • 1
    Hot

    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.
    Downloads: 14 This Week
    Last Update:
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  • 2
    Stats

    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.
    Downloads: 8 This Week
    Last Update:
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  • 3
    SDWebImageSwiftUI

    SDWebImageSwiftUI

    SwiftUI Image loading and Animation framework powered by SDWebImage

    SDWebImageSwiftUI is a SwiftUI image-loading framework, which is based on SDWebImage. It brings all your favorite features from SDWebImage, like async image loading, memory/disk caching, animated image playback and performances. The framework provide the different View structs, which API match the SwiftUI framework guideline. If you're familiar with Image, you'll find it easy to use WebImage and AnimatedImage. Since SDWebImageSwiftUI is built on top of SDWebImage, it provide both the...
    Downloads: 0 This Week
    Last Update:
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  • 4
    GPUImage 2

    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.
    Downloads: 0 This Week
    Last Update:
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