Search Results for "kernel-ntfs"
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Showing 162 open source projects for "kernel-ntfs"
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1
eBPF
ebpf-go is a pure-Go library to read, modify and load eBPF programs
Package ebpf is a toolkit for working with eBPF programs. eBPF programs are small snippets of code which are executed directly in a VM in the Linux kernel, which makes them very fast and flexible. Many Linux subsystems now accept eBPF programs. This makes it possible to implement highly application-specific logic inside the kernel, without having to modify the actual kernel itself. This package is designed for long-running processes which want to use eBPF to implement part of their application logic. ... -
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Lunatik
Lunatik is a framework for scripting the Linux kernel with Lua
Lunatik is a framework for scripting the Linux kernel with Lua. It is composed by the Lua interpreter modified to run in the kernel; a device driver (written in Lua =)) and a command line tool to load and run scripts and manage runtime environments from the user space; a C API to load and run scripts and manage runtime environments from the kernel; and Lua APIs for binding kernel facilities to Lua scripts. -
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JupyterLab LSP
Coding assistance for JupyterLab (code navigation + hover suggestions
...Use the context menu entry, or Alt + 🖱️ to jump to definitions/references (you can change it to Ctrl/⌘ in settings); use Alt + o to jump back. Place your cursor on a variable, function, etc and all the usages will be highlighted. When a kernel is available the suggestions from the kernel (such as keys of a dict and columns of a DataFrame) are merged with the suggestions from the Language Server (in notebook). If the kernel is too slow to respond promptly only the Language Server suggestions will be shown (default threshold: 0.6s). You can configure the completer to not attempt to fetch the kernel completions if the kernel is busy. -
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BCC (BPF Compiler Collection)
Tools for BPF-based Linux IO analysis, networking, monitoring, etc.
BCC is a toolkit that simplifies creating efficient kernel tracing, monitoring, and manipulation programs by leveraging extended Berkeley Packet Filters (eBPF). It includes a rich set of example tools and scripting interfaces in C, Python, and Lua. BCC makes BPF programs easier to write, with kernel instrumentation in C (and includes a C wrapper around LLVM), and front-ends in Python and lua. -
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linux-wasm
WebAssembly (Wasm) arch support for the Linux kernel
linux-wasm is an experimental project that ports the Linux kernel to run directly as a WebAssembly module, enabling a full Linux environment to boot and operate entirely inside a web browser without virtualization or cloud infrastructure. Instead of emulating Linux through a virtual machine, this project compiles the actual kernel for a WebAssembly target, allowing it to execute natively within a Wasm runtime. -
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OCaml Jupyter
An OCaml kernel for Jupyter (IPython) notebook
An OCaml kernel for Jupyter Notebook. This provides an OCaml REPL with a great user interface such as markdown/HTML documentation, LaTeX formula by MathJax, and image embedding. OCaml Jupyter requires the libraries zlib, libffi, libgmp, libzmq 5+. -
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pwru
eBPF-based Linux kernel networking debugger
pwru is an eBPF-based tool for tracing network packets in the Linux kernel with advanced filtering capabilities. It allows fine-grained introspection of kernel state to facilitate debugging network connectivity issues. -
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CuPy
A NumPy-compatible array library accelerated by CUDA
...CuPy is highly compatible with NumPy, serving as a drop-in replacement in most cases. CuPy is very easy to install through pip or through precompiled binary packages called wheels for recommended environments. It also makes writing a custom CUDA kernel very easy, requiring only a small code snippet of C++. -
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NVIDIA Warp
A Python framework for accelerated simulation, data generation
NVIDIA Warp is a high-performance Python framework developed by NVIDIA for building and accelerating simulation, graphics, and physics-based workloads using GPU computing. It enables developers to write kernel-level code in Python that is automatically compiled into efficient CUDA kernels, combining ease of use with near-native performance. The framework is designed for applications such as robotics, reinforcement learning, physical simulation, and differentiable computing, where performance and flexibility are critical. Warp provides a set of primitives for working with arrays, geometry, and physics operations, allowing users to implement complex simulations without writing low-level CUDA code directly. ... -
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GoNB
GoNB, a Go Notebook Kernel for Jupyter
Go is a compiled language, but with very fast compilation, that allows one to use it in a REPL (Read-Eval-Print-Loop) fashion, by inserting a "Compile" step in the middle of the loop -- so it's a Read-Compile-Run-Print-Loop — while still feeling very interactive. GoNB leverages that compilation speed to implement a full-featured (at least it's getting there) Jupyter notebook kernel. As a side benefit it works with packages that use CGO — although it won't parse C code in the cells, so it can't be used as a C kernel. It already includes many goodies: cache between cell of results, contextual help and auto-complete (with gopls), compilation error context (by mousing over), bash command execution, images, html, etc. ... -
11
CubeCL
Multi-platform high-performance compute language extension for Rust
...CubeCL focuses on delivering predictable performance and composability by exposing explicit control over memory layouts, parallelism, and execution patterns while still maintaining a developer-friendly syntax. The framework is built to integrate tightly with modern ML stacks, enabling efficient tensor operations and custom kernel development that can outperform generic libraries in specialized workloads. By combining compiler optimizations with a domain-specific language, CubeCL allows developers to generate highly optimized code for different hardware backends while maintaining a single source of truth. -
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CUDA Python
Performance meets Productivity
...The project is designed to simplify GPU programming by offering Pythonic abstractions while still exposing the full power of CUDA for advanced users. It integrates tightly with the broader Python GPU ecosystem, including Numba for kernel compilation and CCCL for parallel primitives, allowing developers to write performant code without leaving Python. The toolkit also includes utilities for profiling, memory management, distributed computing, and numerical operations, making it suitable for scientific computing, AI, and data processing workloads. -
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Tun2Socks
tun2socks , powered by gVisor TCP/IP stack
...Full IPv6 Support: All functions work in IPv6, tunnel IPv4 connections through IPv6 proxy and vice versa. Network Stack: Powered by user-space TCP/IP stack from Google container application kernel gVisor. -
14
LiteBox
A security-focused library OS supporting kernel execution
...The design emphasizes interoperability across different integration layers, describing a separation between “North” shims (how apps or runtimes plug in) and “South” platforms (where the sandbox runs), which helps the system adapt to multiple deployment contexts. A key aspect of the project is that it targets both kernel-mode and user-mode scenarios, enabling experimentation with different trust and performance tradeoffs. The repository positions LiteBox as a foundation for building hardened execution environments where untrusted or semi-trusted components can run with reduced privileges and a minimized host interface. -
15
Perfetto
Production-grade client-side tracing, profiling, and analysis
...It’s designed around a low-overhead producer/consumer model: instrumented components (“producers”) write binary events into shared memory buffers and a collector (“service”) reliably streams them to storage. The data model spans kernel and userspace, so you can stitch together CPU scheduling, app lifecycles, binder/IPC hops, GPU work, power and thermal signals, file I/O, heap samples, and more into a single coherent timeline. Perfetto’s ecosystem includes a web-based UI that can load multi-GB traces directly in the browser and an offline “trace processor” that exposes the trace as a queryable SQL-like table schema for deep analysis and automation. ... -
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ipytest
Pytest in IPython notebooks
ipytest allows you to run Pytest in Jupyter notebooks. ipytest aims to give access to the full pytest experience and to make it easy to transfer tests out of notebooks into separate test files. -
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SocketCAN userspace utilities and tools
Linux-CAN / SocketCAN user space applications
SocketCAN userspace utilities and tools. This repository contains some userspace utilities for the Linux CAN subsystem (aka SocketCAN). -
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MonoTorrent
The repository for MonoTorrent, a bittorrent library for .NET
This is a list of all the BEPs which have been implemented in MonoTorrent. A full list of all available BEPs can be seen. Prioritise specific files. Selective file downloading (including the ability to not download specific files). Rarest first piece picking (takes priorisation into account). End-game mode to boost the last 1-2% of the download. Sequential downloading (for media files). Per-torrent download/upload rate limiting. Overall download/upload rate limiting. In memory cache to... -
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Inspektor Gadget
The eBPF tool and systems inspection framework for Kubernetes
...It manages the packaging, deployment, and execution of eBPF programs in a Kubernetes cluster, including many based on BCC tools, as well as some developed specifically for use in Inspektor Gadget. It automatically maps low-level kernel primitives to high-level Kubernetes resources, making it easier and quicker to find the relevant information. -
20
Spyder notebook plugin
Jupyter notebook integration with Spyder
...Currently, it supports basic functionality such as creating new notebooks, opening any notebook in your filesystem and saving notebooks at any location. You can also use Spyder's file switcher to easily switch between notebooks and open an IPython console connected to the kernel of a notebook to inspect its variables in the Variable Explorer. -
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Git
Fast, scalable, distributed revision control system
Git is a free, distributed version control system created by Linus Torvalds in 2005 to manage the development of the Linux kernel. It emphasizes speed, data integrity, and support for non-linear workflows, and remains the most popular VCS for collaborative software development. Git is an Open Source project covered by the GNU General Public License version 2 (some parts of it are under different licenses, compatible with the GPLv2). It was originally written by Linus Torvalds with help of a group of hackers around the net. ... -
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Laravel Multilingual Routes
A package to handle multilingual routes in your Laravel application
A package to register multilingual routes for your application. To detect and change the locale of the application based on the request automatically, you can add the middleware to your app/Http/Kernel. It must be the first item in the web middleware group. -
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The Amazon Linux hibernation agent
The Amazon Linux hibernation agent
...If there's no swap space, it launches a background thread to create it and touch all of its blocks to make sure that EBS volumes are pre-warmed if configured. This is configurable. It updates the offset of the swap file in the kernel using SNAPSHOT_SET_SWAP_AREA ioctl. It updates the resume offset and resume device in grub file. -
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Armbian Linux Build Framework
Armbian Linux Build Framework
...Special config utilities are optional. A distributed image is compressed to its real data size which starts below 1G. Login is possible via serial, HDMI/VGA or SSH. Boot loader and kernel optimizations, memory caching, ZRAM swap, and video acceleration where applicable. Images are made fully automatized from sources. Releases are PGP signed and code is regularly inspected by the community. Long-term support, quick security fixes, documentation and community-based end-user support. Make sure you have a good & reliable SD card and a proper power supply. ... -
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nbmake
Pytest plugin for testing notebooks
Pytest plugin for testing and releasing notebook documentation. To raise the quality of scientific material through better automation. Research/Machine Learning Software Engineers who maintain packages/teaching materials with documentation written in notebooks.
