Search Results for "overclock cpu intel"
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Showing 38 open source projects for "overclock cpu intel"
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Intel PCM
Intel® Performance Counter Monitor (Intel® PCM)
Intel® Performance Counter Monitor (Intel® PCM) is an application programming interface (API) and a set of tools based on the API to monitor performance and energy metrics of Intel® Core™, Xeon®, Atom™ and Xeon Phi™ processors. PCM works on Linux, Windows, Mac OS X, FreeBSD, DragonFlyBSD and ChromeOS operating systems. -
2
FastSD CPU
Fast stable diffusion on CPU and AI PC
...The repository contains multiple interfaces including a desktop GUI for simple generation, an advanced web-based UI with support for extensions like LoRA and ControlNet, and a command-line interface for scripted usage or server deployments. With support for performance-oriented libraries such as OpenVINO and hardware acceleration on platforms like Intel AI PCs, FastSD CPU aims to shrink generation times dramatically compared with naive CPU implementations. -
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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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auto-cpufreq
Automatic CPU speed & power optimizer for Linux
Automatic CPU speed & power optimizer for Linux. Actively monitors laptop battery state, CPU usage, CPU temperature, and system load, ultimately allowing you to improve battery life without making any compromises. -
A privacy-first API that predicts global consumer preferencesThrough our API, we provide contextualized personalization and insights based on a deep understanding of consumer behavior and more than 575 million people, places, and things.
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5
Likwid
Performance monitoring and benchmarking suite
Likwid is a simple to install and use toolsuite of command line applications and a library for performance oriented programmers. It works for Intel, AMD, ARMv8 and POWER9 processors on the Linux operating system. There is additional support for Nvidia and AMD GPUs. There is support for ARMv7 and POWER8/9 but there is currently no test machine in our hands to test them properly. -
6
GameMode
Optimise Linux system performance on demand
GameMode is a daemon/lib combo for Linux that allows games to request a set of optimizations be temporarily applied to the host OS and/or a game process. GameMode was designed primarily as a stop-gap solution to problems with the Intel and AMD CPU power save or on-demand governors but is now host to a range of optimization features and configurations. -
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CTranslate2
Fast inference engine for Transformer models
...The project supports x86-64 and AArch64/ARM64 processors and integrates multiple backends that are optimized for these platforms: Intel MKL, oneDNN, OpenBLAS, Ruy, and Apple Accelerate. -
8
XiaoMi Pro Hackintosh
XiaoMi NoteBook Pro Hackintosh
XiaoMi NoteBook Pro Hackintosh. If you are using XiaoMi-Pro with 8th Gen CPU, then it's a KBL (Kaby Lake) machine. (Actually Kaby Lake Refresh) If you are using XiaoMi-Pro with 10th Gen CPU, then it's a CML (Comet Lake) machine. -
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OpenVINO
OpenVINO™ Toolkit repository
...Boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common tasks. Use models trained with popular frameworks like TensorFlow, PyTorch and more. Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud. This open-source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi. -
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AWS Deep Learning Containers
A set of Docker images for training and serving models in TensorFlow
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well. ... -
11
OpenFace Face Recognition
Face recognition with deep neural networks
OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google. Torch allows the network to be executed on a CPU or with CUDA. This research was supported by the National Science Foundation (NSF) under grant number CNS-1518865. Additional support was provided by the Intel Corporation, Google, Vodafone, NVIDIA, and the Conklin Kistler family fund. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and should not be attributed to their employers or funding sources. ... -
12
Prime number ( primenumbers )
Benchmark for 50 000 000 prime numbers as single and multicore
Simple source files and compiled JAR Java programs, for benchmark 50 000 000 cycle finding prime numbers. On Intel(R) Core(TM) i5-8600K CPU, Windows 10 20H2, i have 39 second on single core and 7,6 second on multi core. (PS: C++ multicore 6 second). Added C files for gcc compiler in Windows 10 and for Xcode C command line project in MacOS ( tested on Mac mini M2 with single core 16 to 25 sec and multicore 2,3 to 5 second by compiler -O switch). -
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This is a freedom software and it obeys opensource initiative. The main purpose of this project is to bring extra updates to the existing Octave OCL project with more supporting PC devices like Intel GPU, AMD APUs, and Apple Silicon GPU as well as mobile devices like Qualcomm Adreno and ARM Mali GPUs. Due to the fact tremendous time and energy has been spent on its working on many devices, the project name is changing to AnyArray to reflect it is really working on many devices in 9 March...
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... ⚡ Key Features: • Super lightweight ISO (263MB) • Fast boot and low memory footprint • Clean, minimal user interface • Pre-installed basic tools: file manager, text editor, terminal, and more • Perfect for reviving old PCs or running in lightweight virtual environments 🖥️ Minimum System Requirements: • CPU: Intel Pentium II / AMD K6-2 or newer (32-bit) • RAM: 256 MB minimum • Storage: 400 MB free disk space Website: xxri.rf.gd Maintained by: xxri OS Team
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LDPartEditor
Create and edit LDraw™ parts quick and easy.
...Minimum System Requirements: - OpenGL 2.1 compatible Graphics Card - Operating System (64-bit): Windows [7 or newer], Mac OS X [>=14], Linux [e.g. Linux Mint >=22] - CPU: Multicore-Processor e.g. Intel Core 2 Duo or AMD Athlon II (>2.0Ghz) - RAM: 4 GB - Video-Memory: 1 GB - Free Disk Space: 150 MB Recommended Requirements: - OpenGL 3.3 compatible Graphics Card - Operating System (64-bit): Windows 7,8,10,11, Mac OS X [>=14], Linux [e.g. Linux Mint >=22] - CPU: Multicore-Processor with 4 cores (or more) - RAM: >4 GB - Video-Memory: >1 GB - Free Disk Space: 512 MB The sourcecode is now hosted on GitHub here: http://nilsschmidt1337.github.io/ldparteditor/ -
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PI-Based Image Encoder / Converter
Python code able to convert / compress image to PI (3.14, π) Indexes
...ZIP also include 16 MB file with 16,7 mil numbers of PI Benchmark(Single-Thread): Hardware & Environment Apple Silicon: Apple M2 (Mac mini/MacBook) x86_64 Platform: Intel Core Ultra 5 225F (Arrow Lake, 10 Cores) OS 1: Fedora 43 (GNOME) OS 2: Windows 11 Pro (23H2/24H2) Software: Python 3.14.3 + Numba JIT (latest) Results (Lower is better) Platform / OS CPU Time (Seconds) macOS (Native) Apple M2 52.151311 s (in default setup) Fedora Linux Intel Core Ultra 5 225F 58.536457 s (in default Power Management: Balanced) Windows 11 Intel Core Ultra 5 225F 59.681427 s (important! ... -
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sdcc-z80n-mingw64-binaries
A z80n-targeted repack of SDCC Win64 snapshot
This is a z80n-targeted repackaged snapshot of a Win64 build of SDCC - free open source, retargettable, optimizing ISO C compiler ( https://sdcc.sourceforge.net/ ) "Free open source, retargettable, optimizing ISO C compiler suite that targets a growing list of processors including the Intel MCS-51 based (8031, 8032, 8051, 8052, etc.), Maxim (formerly Dallas) DS80C390 variants, Freescale (formerly Motorola) HC08 based (hc08, s08), Zilog Z80 based MCUs (Z80, Z80N, Z180, SM83 (e.g. Game... -
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Throttled
Workaround for Intel throttling issues in Linux
This tool was originally developed to fix Linux CPU throttling issues affecting Lenovo T480 / T480s / X1C6. The CPU package power limit (PL1/2) is forced to a value of 44 W (29 W on battery) and the temperature trip point to 95 'C (85 'C on battery) by overriding default values in MSR and MCHBAR every 5 seconds (30 on battery) to block the Embedded Controller from resetting these values to default. On systems where the EC doesn't reset the values (ex: ASUS Zenbook UX430UNR), the power limit... -
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SageMaker MXNet Inference Toolkit
Toolkit for allowing inference and serving with MXNet in SageMaker
...AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well. -
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GCLinux
Distribuição Linux utilizada no projeto Gênio Cristão
...Como instalar o GCLinux: https://sourceforge.net/p/gclinux/blog/2021/07/como-instalar-o-gclinux-no-disco-r%C3%ADgido-ou-unidade-flash-usb/ REQUISITOS Processador: CPU i686 ou mais recente, todos os processadores Intel e os processadores AMD funcionarão Memória: 128 MB de RAM para desktop 512 MB de RAM para executar o navegador Web -
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xdp
Package xdp allows one to use XDP sockets
...TL;DR: in the same environment, sending a pre-generated DNS query using an ordinary UDP socket yield around 30 MiB/s whereas sending it using the senddnsqueries example program yields around 77 MiB/s. Connecting a PC with Intel Core i7-7700 CPU running Linux kernel 5.0.17 and igb driver to a laptop with Intel Core i7-5600U CPU running Linux kernel 5.0.9 with e1000e with a cat 5E gigabit ethernet cable. -
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YOLO ROS
YOLO ROS: Real-Time Object Detection for ROS
...Darknet on the CPU is fast (approximately 1.5 seconds on an Intel Core i7-6700HQ CPU @ 2.60GHz × 8) but it's like 500 times faster on GPU! You'll have to have an Nvidia GPU and you'll have to install CUDA. The CMakeLists.txt file automatically detects if you have CUDA installed or not. CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. -
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ThinkJava. The Professional Java IDE
Better than IntelliJ. Lightweight. Professional.
...Email me at zeanfender11@gmail.com This is an EXE app, only Windows can be used!. If you are in another OS, use a Windows emulator Requirements OS: Windows 7 SP1 or newer CPU: Intel Celeron or more powerful RAM: 1 GB Hard Disk: More than 3 MB of disk space Extensions required: .NET Framework 4.1 and Java SE Dev Kit (JDK) (this is required to debug your Java project and syntax highlighting) -
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HWSensors3
HWSensors branch based on FakeSMC-3.x
This is alternative set of kexts and applications serving the same purpose but working with other algorithms. -
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SoliditySHA3Miner
All-in-one mixed multi-GPU (nVidia, AMD, Intel) & CPU miner
All-in-one mixed multi-GPU (nVidia, AMD, Intel) & CPU miner solves proof of work to mine supported EIP918 tokens in a single instance (with API).
