Search Results for "mystic-kernel"
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Showing 201 open source projects for "mystic-kernel"
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1
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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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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Nitro Enclaves Command Line Interface
Tooling for Nitro Enclave management
This repository contains a collection of tools and commands used for managing the lifecycle of enclaves. The Nitro CLI needs to be installed on the parent instance, and it can be used to start, manage, and terminate enclaves. The Nitro Enclaves kernel driver is available in the upstream Linux kernel starting with the v5.10 kernel for x86_64 and starting with the v5.16 kernel for arm64. The codebase from the 'drivers/virt/nitro_enclaves' directory in this GitHub repository is similar to the one merged into the upstream Linux kernel. The enclaves do not have access to a physical disk, just a RAM filesystem. ... -
4
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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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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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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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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Triton
Development repository for the Triton language and compiler
Triton is a programming language and compiler framework specifically designed for writing highly efficient custom deep learning operations, particularly for GPUs. It aims to bridge the gap between low-level GPU programming, such as CUDA, and higher-level abstractions by providing a more productive and flexible environment for developers. Triton enables users to write optimized kernels for machine learning workloads while maintaining readability and control over performance-critical aspects... -
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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. -
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LZ4
Extremely fast compression algorithm
...A high compression derivative, called LZ4_HC, is available, trading customizable CPU time for compression ratio. LZ4 library is provided as open-source software using a BSD license. This benchmark simulates simple "static content transfer" scenario such as OS Kernel compression or video game's static assets (text/images/tables/scripts/etc) which loading from Flash Memory / HDD / SSD. In this case, compression time is completely ignored. Because only content developers compress the data at once and usually they don't care about its computational cost. But they always care end user's experience a.k.a. ... -
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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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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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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. -
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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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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. ... -
16
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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IElixir
Jupyter's kernel for Elixir programming language
Jupyter's kernel for Elixir. You can manage your packages in runtime with Boyle. Name of the package honours remarkable chemist, Robert Boyle. This package allows you to manage your Elixir virtual enviromnent without need of restarting erlang virtual machine. Boyle installs environment into ./envs/you_new_environment directory and creates new mix project there with requested dependencies. -
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fswatch
A cross-platform file change monitor with multiple backends
...A monitor based on kqueue, an event notification interface introduced in FreeBSD 4.1 and supported on most BSD systems (including OS X). A monitor based on inotify, a Linux kernel subsystem that reports file system changes to applications. A monitor based on File Events Notification, a Solaris/Illumos kernel API that reports file events. A monitor based on ReadDirectoryChangesW, a Microsoft Windows API that reports changes to a directory. -
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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. ... -
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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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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. ... -
22
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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Multik
Multidimensional array library for Kotlin
Multidimensional array library for Kotlin. -
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CadQuery
A python parametric CAD scripting framework based on OCCT
...The scripts use a standard programming language, Python, and thus can benefit from the associated infrastructure. This includes many standard libraries and IDEs. CadQuery's CAD kernel Open CASCADE Technology (OCCT) is much more powerful than the CGAL used by OpenSCAD. Features supported natively by OCCT include NURBS, splines, surface sewing, STL repair, STEP import/export, and other complex operations. -
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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.
