Search Results for "intel cpu mining"
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Showing 14 open source projects for "intel cpu mining"
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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. -
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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. ... -
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AutomatedLab
Framework that lets you deploy complex labs on HyperV and Azure
...There are only two requirements you need to make sure: You need the DVD ISO images and a Hyper-V host or an Azure subscription. Requires Windows Management Framework 5+ (Windows). Requires Intel VT-x or AMD/V capable CPU, a decent amount of RAM, and low-latency high-throughput storage (No spinning disks please, as there are issues related to them). This solution supports setting up virtual machines with Windows 7, 2008 R2, 8 / 8.1 and 2012 / 2012 R2, 10 / 2016, 2019, and SQL Server 2008, 2008R2, 2012, 2014, 2016, 2017, 2019. -
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BitMagic Library
Compressed bit-sets, sparse bit matrices and algorithms
BitMagic - C and C++ library implementing dynamic bitvectors and bit-set algorithms with several types of on-the-fly, adaptive compression. Designed for use in databases, search systems, data-mining algorithms, scientific projects. The core of the library is C++, but it provides C-compatibility wrappers and can be compiled without C++ runtime. Optimizations for Intel SSE2, SSE4.2 and AVX2. -
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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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Flashlight library
A C++ standalone library for machine learning
...Flashlight can be broken down into several components as described above. Each component can be incrementally built by specifying the correct build options. Flashlight is most-easily built and installed with vcpkg. Both the CUDA and CPU backends are supported with vcpkg. For either backend, first, install Intel MKL. Flashlight app binaries are also built for the selected features and are installed into the vcpkg install tree's tools directory. -
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libfacedetection
Library for face detection in images
...You can compile the source code under Windows, Linux, ARM and any platform with a C++ compiler. SIMD instructions are used to speed up the detection. You can enable AVX2 if you use Intel CPU or NEON for ARM. The model file has also been provided in directory ./models/. The file examples/detect-image.cpp and examples/detect-camera.cpp show how to use the library. The library was trained by libfacedetection.train. You can copy the files in directory src/ into your project, and compile them as the other files in your project. ... -
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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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Ethereum Mining NVIDIA Graph Card Ubuntu
USB flash drive ISO image for Ethereum, Zcash and Monero mining
USB flash drive ISO image for Ethereum mining with NVIDIA graphics cards and Ubuntu GNU/Linux (64-bit Intel/AMD (x86_64)). Other cryptocurrencies, such as Monero or Zcash, can also be mined. With this ISO image, you can immediately mine Ethereum (ETH). Do not spend long time searching and researching. If you do not trust me and do not want to use the image, you will find all configuration files and scripts in the files folder. -
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MXLib is a C++ wrapper around the Intel® Integrated Performance Primitives (IPP) library and NVidia NPP CUDA library. You can use either IPP code (or a subset of functions that do not require IPP) on the CPU side, or use NPP/CUDA on the GPU side, or use both together. The function syntax is similar to that found in MatLab and the library is designed to make it easy to port your code from MatLab to C++.
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ZASM is a 2 pass assembler for Zilog's 8-bit CPU Z80. Supported platforms: Unix-style OS, e.g. MacOS X, Linux, BSD. Special support for ZX Spectrum emulator file formats. Binary or intel hex file output.
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Goal of this project is to make a working Intel 8051 simulator. It covers also some I/O devices like keypad, LCD screen and GUI in Qt.
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JSim8086 is platform-independent Assembly Language Simulator of the Intel 8086 CPU. It was designed to be a learning tool (CAL) for first year Computing students who are learning Computer Systems Organisation and Assembly Programming.
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Softx86 is a library that provides software emulation of an Intel 80x86 CPU. The library takes care of executing opcodes and handling exceptions while the host application is responsible for providing simulated RAM, I/O devices, external signals.
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