Search Results for "linux cpu"
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Showing 40 open source projects for "linux cpu"
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1
WeakAuras
World of Warcraft addon that provides a powerful framework
.... Progress bars and textures that show the exact duration of auras. Displays based on auras, health, power (mana, rage, soul shards, holy power, etc.), cooldowns, combat events, runes, totems, items, and many other triggers. Preset and user-defined animations. Custom side-effects such as chat announcements or sounds. Grouping, which allows multiple displays to be positioned and configured at the same time. CPU optimizations such as conditional loading/unloading of displays, modularity, etc. -
2
Unicorn Engine
Unicorn CPU emulator framework (ARM, AArch64, M68K, Mips, Sparc
Unicorn is a lightweight multi-platform, multi-architecture CPU emulator framework. Highlight features. Multi-architectures: ARM, ARM64 (ARMv8), m68k, MIPS, PowerPC, RISC-V, S390x (SystemZ), SPARC, TriCore & x86 (include x86_64). Clean/simple/lightweight/intuitive architecture-neutral API. Implemented in pure C language, with bindings for Pharo, Crystal, Clojure, Visual Basic, Perl, Rust, Haskell, Ruby, Python, Java, Go, D, Lua, JavaScript, .NET, Delphi/Pascal & MSVC available. Native support... -
3
fgprof
fgprof is a sampling Go profiler that allows you to analyze On-CPU
fgprof is a sampling Go profiler that allows you to analyze On-CPU as well as Off-CPU (e.g. I/O) time together. Go's builtin sampling CPU profiler can only show On-CPU time, but it's better than fgprof at that. Go also includes tracing profilers that can analyze I/O, but they can't be combined with the CPU profiler. fgprof is designed for analyzing applications with mixed I/O and CPU workloads. This kind of profiling is also known as wall-clock profiling. If this is the first time you hear... -
4
Play Framework
A high velocity web framework
... for modern web & mobile applications. Play is based on a lightweight, stateless, web-friendly architecture. Built on Akka, Play provides predictable and minimal resource consumption (CPU, memory, threads) for highly-scalable applications. Make your changes and simply hit refresh! All you need is a browser and a text editor. Underneath the covers Play uses a fully asynchronous model built on top of Akka. -
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5
automaxprocs
Automatically set GOMAXPROCS to match Linux container CPU quota
Automatically set GOMAXPROCS to match Linux container CPU quota. All APIs are finalized, and no breaking changes will be made in the 1.x series of releases. Users of semver-aware dependency management systems should pin automaxprocs to ^1. When GOMAXPROCS is increased above the CPU quota, we see P50 decrease slightly but see significant increases to P99. We also see that the total RPS handled also decreases. Data was measured from Uber's internal load balancer. We ran the load balancer with 200... -
6
MNN
MNN is a blazing fast, lightweight deep learning framework
... distribution, security risk control. In addition, MNN is also used on embedded devices, such as IoT. MNN Workbench could be downloaded from MNN's homepage, which provides pretrained models, visualized training tools, and one-click deployment of models to devices. Android platform, core so size is about 400KB, OpenCL so is about 400KB, Vulkan so is about 400KB. Supports hybrid computing on multiple devices. Currently supports CPU and GPU. -
7
tvm
Open deep learning compiler stack for cpu, gpu, etc.
Apache TVM is an open source machine learning compiler framework for CPUs, GPUs, and machine learning accelerators. It aims to enable machine learning engineers to optimize and run computations efficiently on any hardware backend. The vision of the Apache TVM 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... -
8
MegEngine
Easy-to-use deep learning framework with 3 key features
.... Gain the lowest memory usage when inferencing a model by leveraging our unique pushdown memory planner. NOTE: MegEngine now supports Python installation on Linux-64bit/Windows-64bit/MacOS(CPU-Only)-10.14+/Android 7+(CPU-Only) platforms with Python from 3.5 to 3.8. On Windows 10 you can either install the Linux distribution through Windows Subsystem for Linux (WSL) or install the Windows distribution directly. Many other platforms are supported for inference. -
9
EaseProbe
A simple, standalone, and lightweight tool
... command on a remote host and check the CPU, Memory, and Disk usage. MySQL. Connect to a MySQL server and run the SHOW STATUS SQL. Redis. Connect to a Redis server and run the PING command. Memcache. Connect to a Memcache server and run the version command or validate a given key/value pair. MongoDB. Connect to a MongoDB server and perform a ping. Kafka. Connect to a Kafka server and perform a list of all topics. -
Global anycast DNS network. | IBM NS1 ConnectIBM NS1 Connect provides fast, secure connections to users anywhere in the world with premium DNS and advanced, customizable traffic steering. NS1 Connect’s always-on, API-first architecture enables your IT teams to more efficiently monitor networks, deploy changes and conduct routine maintenance.
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10
ServiceTalk
A networking framework that evolves with your application
... knowledge of EventLoop threading model. Executing CPU-intensive or "blocking" code requires manual thread hops. Subtle out-of-order execution of tasks when code executes both on and off the EventLoop thread. APIs are not tailored towards common application use cases (e.g. request/response, RPC, etc..) The asynchronous programming paradigm presents a barrier to entry in scenarios where vertical scalability is not a primary concern. -
11
Netty-socketio
Socket.IO server implemented on Java
...-featured Java Profiler. YourKit, LLC is the creator of innovative and intelligent tools for profiling Java and .NET applications. CentOS, 1 CPU, 4GB RAM runned on VM, CPU 10%, Memory 15%, 6000 xhr-long polling sessions or 15000 websockets sessions, 4000 messages per second. -
12
TorchQuantum
A PyTorch-based framework for Quantum Classical Simulation
A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers. Researchers on quantum algorithm design, parameterized quantum circuit training, quantum optimal control, quantum machine learning, and quantum neural networks. Dynamic computation graph, automatic gradient computation, fast GPU support, batch model terrorized processing. -
13
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... -
14
AutomatedLab
Framework that lets you deploy complex labs on HyperV and Azure
AutomatedLab is a provisioning solution and framework that lets you deploy complex labs on HyperV and Azure with simple PowerShell scripts. It supports all Windows operating systems from 2008 R2 to 2019, some Linux distributions and various products like AD, Exchange, PKI, IIS, etc. AutomatedLab (AL) enables you to setup test and lab environments on Hyper-v or Azure with multiple products or just a single VM in a very short time. There are only two requirements you need to make sure: You need... -
15
Axe
Logger-agnostic wrapper that normalizes logs regardless of arg style
Logger-agnostic wrapper that normalizes logs regardless of arg style. Great for large dev teams, old/new projects, and works w/Pino, Bunyan, Winston, console, and more. It is lightweight, performant, highly-configurable, and automatically adds OS, CPU, and Git information to your logs. Hooks, dot-notation remap, omit, and pick of metadata. Axe was built to provide consistency among development teams when it comes to logging. You not only have to worry about your development team using the same... -
16
MetalPetal
A GPU accelerated image and video processing framework built on Metal
MetalPetal is an image processing framework based on Metal designed to provide real-time processing for still images and video with easy-to-use programming interfaces. This chapter covers the key concepts of MetalPetal, and will help you to get a better understanding of its design, implementation, performance implications, and best practices. A MTIImage object is a representation of an image to be processed or produced. It does directly represent image bitmap data instead it has all the... -
17
Execute JavaScript from Go
Execute JavaScript from Go
Execute JavaScript from Go. For scripts that are large or are repeatedly run in different contexts, it is beneficial to compile the script once and used the cached data from that compilation to avoid recompiling every time you want to run it. The v8go library would welcome contributions from anyone able to get an external windows build of the V8 library linking with v8go, using the version of V8 checked out in the deps/v8 git submodule, and documentation of the process involved. This process... -
18
neo.mjs
The application worker driven frontend framework
... it comes to big Apps or complex Components. The problem is, that most Apps today run within a single thread (CPU), while each computer or mobile device has several CPUs available. Some Apps move expensive tasks into separate threads, but this is by far not enough. Using neo.mjs, most parts of the framework as well as the Apps which you create with it, run inside a separate thread. This architecture leaves the main thread mostly idle, so it can fully concentrate on its purpose. -
19
SocketCluster
Highly scalable realtime pub/sub and RPC framework
Highly scalable pub/sub and RPC toolkit optimized for async/await. Pub/sub-channels are very cheap. You can have millions of unique channels without worrying about memory or CPU usage. Channels are automatically cleaned up when they are no longer used. The socket cluster CLI tool exposes kubectl (Kubernetes) shortcut commands to make deploying your app to any Kubernetes cluster really easy. All necessary Kubernetes .yaml files are provided. After deploying your app to a Kubernetes cluster, you... -
20
ToolGood.Words
A high-performance sensitive word
A high-performance sensitive word (illegal word/dirty word) detection and filtering component, with a traditional and simplified exchange, supports full-width half-width exchange, Chinese characters to pinyin, fuzzy search, and other functions. C#Language, using StringSearchEx2.Replacefiltering, on a 48k sensitive thesaurus at over 300 million characters per second. (cpu i7 8750h). C#'s own regular is very slow, which StringSearchEx2.ContainsAnyis Regex.IsMatchmore than 88,000 times... -
21
Jittor
Jittor is a high-performance deep learning framework
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement... -
22
Deep Java Library (DJL)
An engine-agnostic deep learning framework in Java
... favorite IDE to build, train, and deploy your models. DJL makes it easy to integrate these models with your Java applications. Because DJL is deep learning engine agnostic, you don't have to make a choice between engines when creating your projects. You can switch engines at any point. To ensure the best performance, DJL also provides automatic CPU/GPU choice based on hardware configuration. -
23
Ray
A unified framework for scalable computing
Modern workloads like deep learning and hyperparameter tuning are compute-intensive and require distributed or parallel execution. Ray makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes. Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. Accelerate your hyperparameter search workloads with Ray Tune. Find the best... -
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AWS IoT Device Defender Library
Client library for using AWS IoT Defender service on embedded devices
The Device Defender library enables you to send device metrics to the AWS IoT Device Defender Service. This library also supports custom metrics, a feature that helps you monitor operational health metrics that are unique to your fleet or use case. For example, you can define a new metric to monitor the memory usage or CPU usage on your devices. This library has no dependencies on any additional libraries other than the standard C library, and therefore, can be used with any MQTT client library... -
25
TensorFlow Model Garden
Models and examples built with TensorFlow
... are suitable. A flexible and lightweight library that users can easily use or fork when writing customized training loop code in TensorFlow 2.x. It seamlessly integrates with tf.distribute and supports running on different device types (CPU, GPU, and TPU).