Search Results for "cpu"
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Showing 47 open source projects for "cpu"
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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. ... -
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WeakAuras
World of Warcraft addon that provides a powerful framework
...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. -
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MNN
MNN is a blazing fast, lightweight deep learning framework
...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. -
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MSA: Memory Sparse Attention
Trainable latent-memory framework for 100M-token contexts
...Document-wise rotary position encoding and top-k routing keep training and inference close to linear complexity. A tiered KV-cache design stores routing keys on GPU while larger content states can remain on CPU. Its Memory Parallel engine distributes scoring and transfers only selected memory back to the accelerator. Memory Interleave alternates retrieval, context expansion, and generation to improve multi-hop reasoning across distant segments. The project reports experiments extending from 16K to 100M tokens, including inference on two A800 GPUs. -
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Netty-socketio
Socket.IO server implemented on Java
...YourKit is kindly supporting this open source project with its full-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. -
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EaseProbe
A simple, standalone, and lightweight tool
...Support the bastion/jump server. Connect to a given port using TLS and (optionally) validate for revoked or expired certificates. Run an SSH 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. -
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ServiceTalk
A networking framework that evolves with your application
...Netty is a great low-level networking framework, but when used for service-to-service communication it presents a few opportunities for improvement. Fully asynchronous and requires 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. -
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Piscina
A fast, efficient Node.js Worker Thread Pool implementation
Piscina is a fast, efficient Node.js worker thread pool implementation, designed to perform heavy CPU-bound tasks in parallel, thereby improving application performance. -
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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... -
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neo.mjs
The application worker driven frontend framework
...While current libraries/frameworks like Angular, React or Vue provide reasonable performance for small or mostly static Apps, they lack when 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. -
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Phalcon
High performance, full-stack PHP framework delivered as a C extension
Its innovative architecture makes Phalcon the fastest PHP framework ever built! Developers do not need to know C to use Phalcon. Its functionality is exposed as PHP classes and interfaces under the Phalcon namespace, ready to be used. Zephir/C extensions are loaded together with PHP one time on the web server's daemon start process. Classes and functions provided by the extension are ready to use for any application. The code is compiled and isn't interpreted because it's already compiled to... -
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Play Framework
A high velocity web framework
...By being RESTful by default, including assets compilers, JSON & WebSocket support, Play is a perfect fit 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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Chipyard
An Agile RISC-V SoC Design Framework with in-order cores
Chipyard is a framework and generator for constructing custom RISC‑V SoC hardware. Built at UC Berkeley, it leverages Chisel/FIRRTL to generate full-stack systems—from CPU cores to peripherals—and includes simulators, FPGA deployment tools, and integration with Rocket Chip and other RISC‑V ecosystems. -
14
bitnet.cpp
Official inference framework for 1-bit LLMs
bitnet.cpp is the official open-source inference framework and ecosystem designed to enable ultra-efficient execution of 1-bit large language models (LLMs), which quantize most model parameters to ternary values (-1, 0, +1) while maintaining competitive performance with full-precision counterparts. At its core is bitnet.cpp, a highly optimized C++ backend that supports fast, low-memory inference on both CPUs and GPUs, enabling models such as BitNet b1.58 to run without requiring enormous... -
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Deep Java Library (DJL)
An engine-agnostic deep learning framework in Java
...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. -
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go-web-framework-benchmark
Go web framework benchmark
...It can add configurable handler delay to simulate business work such as database calls, cache access, disk writes, socket activity, or microservice calls. The benchmark uses wrk and scripts to collect throughput, latency, allocation, concurrency, pipelining, and CPU-bound results. It is useful for developers who want repeatable framework comparisons that better reflect end-to-end web request behavior. -
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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... -
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automaxprocs
Automatically set GOMAXPROCS to match Linux container CPU quota
...Data was measured from Uber's internal load balancer. We ran the load balancer with 200% CPU quota (i.e., 2 cores). -
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Axe
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 approach to writing logs and debugging applications, but you also have to consider that open-source maintainers implement logging differently in their packages. ... -
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TensorFlow Model Garden
Models and examples built with TensorFlow
...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). -
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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... -
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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 model and reduce training costs by using the latest optimization algorithms. ... -
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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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AWS IoT Device Defender Library
Client library for using AWS IoT Defender service on embedded devices
...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. This library is distributed under the MIT Open Source License. This library has gone through code quality checks including verification that no function has a GNU Complexity score over 8, and checks against deviations from mandatory rules in the MISRA coding standard. ... -
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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. ...
