Search Results for "silont-kernel-onclite"
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Showing 29 open source projects for "silont-kernel-onclite"
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Copy Fail - CVE-2026-31431
epository that demonstrates and analyzes a Linux kernel vulnerability
Copy Fail - CVE-2026-31431 is a proof-of-concept repository that demonstrates and analyzes a specific Linux kernel vulnerability identified as CVE-2026-31431. The project provides experimental scripts and documentation to reproduce and study the exploit in controlled environments. It is designed for security researchers and engineers who want to understand the mechanics of the vulnerability. The repository includes tested configurations across multiple Linux distributions and kernel versions. ... -
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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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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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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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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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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. ... -
7
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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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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Multik
Multidimensional array library for Kotlin
Multidimensional array library for Kotlin. -
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
...We have outsourced a lot of functionality of PyTorch Geometric to other packages, which needs to be additionally installed. These packages come with their own CPU and GPU kernel implementations based on C++/CUDA extensions. We do not recommend installation as root user on your system python. Please setup an Anaconda/Miniconda environment or create a Docker image. We provide pip wheels for all major OS/PyTorch/CUDA combinations. -
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FlashMLA
FlashMLA: Efficient Multi-head Latent Attention Kernels
FlashMLA is a high-performance decoding kernel library designed especially for Multi-Head Latent Attention (MLA) workloads, targeting NVIDIA Hopper GPU architectures. It provides optimized kernels for MLA decoding, including support for variable-length sequences, helping reduce latency and increase throughput in model inference systems using that attention style. The library supports both BF16 and FP16 data types, and includes a paged KV cache implementation with a block size of 64 to efficiently manage memory during decoding. ... -
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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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eCapture
Capturing SSL/TLS plaintext without a CA certificate using eBPF
Capture SSL/TLS text content without a CA certificate using eBPF. Supports Linux/Android kernel versions x86_64 4.18 and above, aarch64 5.5 and above. Does not support Windows and macOS systems. -
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Swoole
Coroutine-based concurrency library for PHP
...The network layer in Swoole is event-based and takes full advantage of the underlying epoll/kqueue implementation, making it really easy to serve millions of requests. Swoole 4.x uses a brand new engine kernel and now it has a full-time developer team, so we are entering an unprecedented period in PHP history which offers a unique possibility for rapid evolution. -
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FFTW.jl
Julia bindings to the FFTW library for fast Fourier transforms
This package provides Julia bindings to the FFTW library for fast Fourier transforms (FFTs), as well as functionality useful for signal processing. These functions were formerly a part of Base Julia. Users with a build of Julia based on Intel's Math Kernel Library (MKL) can use MKL for FFTs by setting a preference in their top-level project by either using the FFTW.set_provider!() method, or by directly setting the preference using Preferences.jl. Note that this choice will be recorded for the current project, and other projects that wish to use MKL for FFTs should also set that same preference. ... -
16
oneDNN
oneAPI Deep Neural Network Library (oneDNN)
This software was previously known as Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) and Deep Neural Network Library (DNNL). oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform performance library of basic building blocks for deep learning applications. oneDNN is part of oneAPI. The library is optimized for Intel(R) Architecture Processors, Intel Processor Graphics and Xe Architecture graphics. oneDNN has experimental support for the following architectures: Arm* 64-bit Architecture (AArch64), NVIDIA* GPU, OpenPOWER* Power ISA (PPC64), IBMz* (s390x), and RISC-V. oneDNN is intended for deep learning applications and framework developers interested in improving application performance on Intel CPUs and GPUs. ... -
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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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Unikraft
A next-generation cloud native kernel designed to unlock performance
Unikraft powers the next generation of cloud-native, containerless applications by enabling you to radically customize and build custom OS/kernels; unlocking best-in-class performance, security primitives, and efficiency savings. Unikraft optimizes resource utilization, leading to smaller footprints (meaning higher server saturation) and improved efficiency in resource-constrained environments. Unikraft is an open-source project driven by a vibrant community of over 100 developers, fostering... -
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Asmosis
A set of tools related to assembly language programming.
...The tools include: - the Asm::X86 Perl module, - AsmDoc - an HTML documentation generator for assembly language, - Asm4Doxy - Assembly converter for Doxygen (http://www.doxygen.org/), - converters between various assembly language dialects: NASM (The Netwide Assembler, https://www.nasm.us), fasm (flat assembler, https://flatassembler.net) and GNU as, - converters from C/C++ header files to assembly language header files, - make4fasm - a Makefile generator for fasm, - Linux-2.6 kernel module helpers for fasm and NASM, - macros for Autoconf (https://www.gnu.org/software/autoconf/), - pieces of code that may be useful for starting developing a simple operating system. See the project homepage https://asmosis.sourceforge.io and the project Wiki in the menu above. -
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Neural Tangents
Fast and Easy Infinite Neural Networks in Python
Kernel evaluation is highly optimized for speed and memory, and computations can be automatically distributed across accelerators with near-linear scaling. -
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LMAX Disruptor
High performance inter-thread messaging library
...Performance testing showed that using queues to pass data between stages of the system was introducing latency, so we focused on optimising this area. The Disruptor is the result of our research and testing. We found that cache misses at the CPU-level, and locks requiring kernel arbitration are both extremely costly, so we created a framework which has "mechanical sympathy" for the hardware it’s running on, and that’s lock-free. This is not a specialist solution, it’s not designed to work only for a financial application. The Disruptor is a general-purpose mechanism for solving a difficult problem in concurrent programming. ... -
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Plymouth Themes
A huge collection (80+) of Plymouth themes ported from Android
A big collection of Plymouth themes, ported from Android bootanimation. Plymouth is a project from Fedora and now listed among the freedesktop.org's official resources providing a flicker-free graphical boot process. It relies on kernel mode setting (KMS) to set the native resolution of the display as early as possible, then provides an eye-candy splash screen leading all the way up to the login manager. -
23
Periph
Peripherals I/O in Go
...Its philosophy is “no daemon, no codegen”—you link a Go package and speak directly to the hardware via stable abstractions, keeping deployments simple and auditable. The project auto-detects host capabilities at runtime and selects the best driver backend, smoothing over kernel and board differences across Raspberry Pi, BeagleBone, and other platforms. A large set of device drivers (sensors, displays, LED controllers, storage, RTCs) give you batteries-included building blocks for prototypes and production systems. Command-line tools like periph-info help verify wiring, enumerate buses, and inspect capabilities before you ship. ... -
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Minkowski Engine
Auto-diff neural network library for high-dimensional sparse tensors
The Minkowski Engine is an auto-differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unspooling, and broadcasting operations for sparse tensors. The Minkowski Engine supports various functions that can be built on a sparse tensor. We list a few popular network architectures and applications here. To run the examples, please install the package and run the command in the package root directory. Compressing a neural network to... -
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Awesome Graph Classification
Graph embedding, classification and representation learning papers
A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations. Relevant graph classification benchmark datasets are available. Similar collections about community detection, classification/regression tree, fraud detection, Monte Carlo tree search, and gradient boosting papers with implementations.
