Search Results for "multi-thread cpu" - Page 2
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Showing 192 open source projects for "multi-thread cpu"
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xManager
Ad-Free, New Features & Freedom
...Android devices have CPUs. Merged is a combination of Armeabi-v7a and Arm64-v8a architecture. Armeabi-v7a is the modern arch that supports floating point operations, and a multi-threading safe is used on multi-cpu devices. It is also 32-bit. If you are determined to run this on a non-android device then fire up an Android emulator. -
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HunyuanVideo-I2V
A Customizable Image-to-Video Model based on HunyuanVideo
...The repository includes pretrained weights, inference and sampling scripts, training code for LoRA effects, and support for parallel inference via xDiT. Resolution, video length, stability mode, flow shift, seed, CPU offload etc. Parallel inference support using xDiT for multi-GPU speedups. LoRA training / fine-tuning support to add special effects or customize generation. -
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DALI
A GPU-accelerated library containing highly optimized building blocks
...It can be used as a portable drop-in replacement for built-in data loaders and data iterators in popular deep learning frameworks. Deep learning applications require complex, multi-stage data processing pipelines that include loading, decoding, cropping, resizing, and many other augmentations. These data processing pipelines, which are currently executed on the CPU, have become a bottleneck, limiting the performance and scalability of training and inference. DALI addresses the problem of the CPU bottleneck by offloading data preprocessing to the GPU. ... -
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Netty-socketio
Socket.IO server implemented on Java
...Supports 0.7...0.9.16 (netty-socketio 1.6.6) and 1.0+ (netty-socketio latest version) version of Socket.IO-client. Supports distributed broadcast across netty-socketio nodes (Redisson, Hazelcast). Supports OSGi, supports Spring, lock-free and thread-safe implementation, and declarative handler configuration via annotations. 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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SkyEmu
Game Boy Advance, Game Boy, Game Boy Color, and DS Emulator
SkyEmu is a modern, multi-system handheld emulator that supports Game Boy, Game Boy Color, Game Boy Advance, and experimental Nintendo DS emulation, with a strong focus on balancing accuracy, performance, and usability. It is designed as a low-level emulator that accurately reproduces hardware behavior while still offering a smooth and accessible user experience across a wide range of platforms. The emulator is highly portable, supporting desktop operating systems, mobile devices, and even... -
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ChatGLM3
ChatGLM3 series: Open Bilingual Chat LLMs | Open Source Bilingual Chat
ChatGLM3 is ZhipuAI & Tsinghua KEG’s third-gen conversational model suite centered on the 6B-parameter ChatGLM3-6B. It keeps the series’ smooth dialog and low deployment cost while adding native tool use (function calling), a built-in code interpreter, and agent-style workflows. The family includes base and long-context variants (8K/32K/128K). The repo ships Python APIs, CLI and web demos (Gradio/Streamlit), an OpenAI-format API server, and a compact fine-tuning kit. Quantization (4/8-bit),... -
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LMCache
Supercharge Your LLM with the Fastest KV Cache Layer
LMCache is an extension layer for LLM serving engines that accelerates inference, especially with long contexts, by storing and reusing key-value (KV) attention caches across requests. Instead of rebuilding KV states for repeated or shared text segments, LMCache persists and retrieves them from multiple tiers—GPU memory, CPU DRAM, and local disk—then injects them into subsequent requests to reduce TTFT and increase throughput. Its design supports reuse beyond strict prefix matching and enables sharing across serving instances, improving efficiency under real multi-tenant traffic. The broader project includes examples, tests, a server component, and public posts describing cross-engine sharing and inter-GPU KV transfers. ... -
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ImplicitGlobalGrid.jl
Distributed parallelization of stencil-based GPU and CPU applications
ImplicitGlobalGrid is an outcome of a collaboration of the Swiss National Supercomputing Centre, ETH Zurich (Dr. Samuel Omlin) with Stanford University (Dr. Ludovic Räss) and the Swiss Geocomputing Centre (Prof. Yuri Podladchikov). It renders the distributed parallelization of stencil-based GPU and CPU applications on a regular staggered grid almost trivial and enables close to ideal weak scaling of real-world applications on thousands of GPUs [1, 2, 3]. ImplicitGlobalGrid relies on the... -
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VibeVoice ComfyUI
ComfyUI integration for Microsoft's VibeVoice text-to-speech model
VibeVoice ComfyUI is a comprehensive wrapper that integrates Microsoft’s VibeVoice text-to-speech models directly into ComfyUI workflows. It exposes VibeVoice as a set of custom nodes so you can build single-speaker and multi-speaker voice generation pipelines visually, combining TTS with other audio or generative components. The integration supports high-quality single-speaker synthesis as well as scripted multi-speaker conversations, with optional voice cloning from audio samples for each... -
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OpenCV
Open Source Computer Vision Library
OpenCV (Open Source Computer Vision Library) is a comprehensive open-source library for computer vision, machine learning, and image processing. It enables developers to build real-time vision applications ranging from facial recognition to object tracking. OpenCV supports a wide range of programming languages including C++, Python, and Java, and is optimized for both CPU and GPU operations. -
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llmfit
157 models, 30 providers, one command to find what runs on hardware
llmfit is a terminal-based utility that helps developers determine which large language models can realistically run on their local hardware by analyzing system resources and model requirements. The tool automatically detects CPU, RAM, GPU, and VRAM specifications, then ranks available models based on performance factors such as speed, quality, and memory fit. It provides both an interactive terminal user interface and a traditional CLI mode, enabling flexible workflows for different user preferences. llmfit also supports advanced configurations including multi-GPU setups, mixture-of-experts architectures, and dynamic quantization recommendations. ... -
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Multimodal
TorchMultimodal is a PyTorch library
...The repository also includes example scripts and datasets for common multimodal tasks (e.g. retrieval, visual question answering, grounding) so you can test and compare models end to end. Installation supports both CPU and CUDA, and the codebase is versioned, tested, and maintained. -
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PDFRip
A multi-threaded PDF password cracking utility
A multi-threaded PDF password cracking utility equipped with commonly encountered password format builders and dictionary attacks. pdfrip is a fast multithreaded PDF password cracking utility written in Rust with support for wordlist-based dictionary attacks, date and number range bruteforcing, and a custom query builder for password formats. You can write your own queries like STRING{69-420} with the -q option which would generate a wordlist with the full number range. You can pass in an... -
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HeavyDB
HeavyDB (formerly MapD/OmniSciDB)
HeavyDB is an open-source GPU-accelerated analytical database designed to perform extremely fast queries on large datasets. The system is built as a SQL-based relational columnar database engine that leverages modern hardware parallelism, including GPUs and multicore CPUs. Its architecture allows users to query datasets containing billions of rows in milliseconds without requiring traditional indexing, pre-aggregation, or sampling techniques. HeavyDB was originally developed as part of the... -
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cuML
RAPIDS Machine Learning Library
cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn. For large datasets, these GPU-based implementations can complete 10-50x faster than their CPU... -
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VulkanSceneGraph
Vulkan & C++17 based Scene Graph Project
VulkanSceneGraph (VSG), is a modern, cross-platform, high-performance scene graph library built upon Vulkan graphics/compute API. The software is written in C++17 and follows the CppCoreGuidelines and FOSS Best Practices. The source code is published under the MIT License, with the exception of vulkan.h, used for Vulkan extensions, which is under Apache License 2.0. This repository contains C++ headers and source and CMake build scripts to build the libvsg library. Additional support... -
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ncnn
High-performance neural network inference framework for mobile
ncnn is a high-performance neural network inference computing framework designed specifically for mobile platforms. It brings artificial intelligence right at your fingertips with no third-party dependencies, and speeds faster than all other known open source frameworks for mobile phone cpu. ncnn allows developers to easily deploy deep learning algorithm models to the mobile platform and create intelligent APPs. It is cross-platform and supports most commonly used CNN networks, including... -
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DebugSwift
A toolkit to make debugging iOS applications easier
...Designed to operate within the iOS environment (iOS 14+ and Swift 6+), it includes network and WebSocket inspectors for HTTP requests, detailed performance metrics (CPU, memory, FPS), and mechanisms to detect memory leaks and main thread violations in real time. The toolkit also provides developers with crash analysis features, console log monitoring, device and build information displays, and customizable debugging panels, all of which make it easier to troubleshoot edge cases and unexpected behavior. ... -
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Gemma
Gemma open-weight LLM library, from Google DeepMind
Gemma, developed by Google DeepMind, is a family of open-weights large language models (LLMs) built upon the research and technology behind Gemini. This repository provides the official implementation of the Gemma PyPI package, a JAX-based library that enables users to load, interact with, and fine-tune Gemma models. The framework supports both text and multi-modal input, allowing natural language conversations that incorporate visual content such as images. It includes APIs for... -
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Dozzle
Realtime log viewer for containers. Supports Docker, Swarm and K8s
...The interface includes practical quality-of-life features like fuzzy searching for containers, regex log search, split-screen viewing for multiple logs, and live stats such as CPU and memory usage. It supports more advanced analysis through an in-browser SQL query engine for querying logs, which helps when you need structured filtering without exporting data elsewhere. Dozzle also supports multi-user authentication and can integrate with proxy-forward authorization setups for deployments behind gateways. For larger environments, it can monitor multiple hosts via an agent mode and supports orchestrators like Docker Swarm and Kubernetes. -
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kcptun
A stable and secure tunnel based on KCP with N:M multiplexing and FEC
...Available for ARM, MIPS, 386 and AMD64. kcptun is shipped with builtin packet encryption powered by various block encryption algorithms and works in Cipher Feedback Mode, for each packet to be sent, the encryption process will start from encrypting a nonce from the system entropy, so encryption to same plaintexts never leads to a same ciphertexts thereafter. kcptun made use of ReedSolomon-Codes to recover lost packets, which requires massive amount of computation, a low-end ARM device cannot satisfy kcptun well. To unleash the full potential of kcptun, a multi-core x86 homeserver CPU like AMD Opteron is recommended. If you insist on running under some ARM routers, you'd better turn off FEC and use salsa20 as the encryption method. -
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mGBA
mGBA Game Boy Advance Emulator
...The project was created with the goal of improving both accuracy and speed compared to earlier emulators, achieving a balance that allows games to run reliably even on lower-end systems. It supports not only Game Boy Advance titles but also Game Boy and Game Boy Color games, making it a versatile multi-generation emulator. The architecture is written in C and focuses on precise hardware emulation, including CPU timing, graphics rendering, and audio output, which are essential for compatibility with a wide range of games. It includes multiple frontends such as Qt for a full graphical interface and SDL for lightweight usage, giving users flexibility depending on their environment. -
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Dockhand
Docker management you will like
Dockhand is a modern, self-hosted Docker management application that provides a graphical interface for handling container operations, Docker Compose stacks, and multi-environment orchestration without relying solely on terminal commands. Designed for homelab enthusiasts, developers, and growing teams, Dockhand offers real-time container lifecycle controls, visual editors for stacks, and a dashboard that shows system metrics like CPU and memory usage. The platform supports Git integration for deploying and syncing Compose stacks directly from repositories, interactive log streaming, and shell access into containers. ... -
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PicoClaw
Ultra-Efficient AI Assistant in Go
PicoClaw is an ultra-lightweight, open-source personal AI assistant written in Go, architected from the ground up to operate with extremely low memory usage (under 10 MB) and fast boot times, making it suitable for inexpensive hardware platforms and embedded devices. Inspired by earlier AI assistant projects like “nanobot,” it was refactored to emphasize resource efficiency while still supporting meaningful AI-driven interactions such as conversational workflows, planning tasks, and... -
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FlexLLMGen
Running large language models on a single GPU
...The system focuses on high-throughput generation workloads where large batches of text must be processed quickly, such as large-scale data extraction or document analysis tasks. Instead of requiring expensive multi-GPU systems, the framework uses techniques such as memory offloading, compression, and optimized batching to run large models on commodity hardware. The architecture distributes computation and memory usage across the GPU, CPU, and disk in order to maximize the number of tokens processed during inference. This design allows organizations to deploy powerful language models for high-volume tasks without the infrastructure costs typically associated with large-scale AI systems. ...
