Cuda
Showing 23 of 95 projects
A graphical application for rapidly prototyping and deploying computer vision algorithms, primarily for robotics.
An optimized, lightweight CUDA-based 3D reconstruction implementation derived from the original Kinfu algorithm.
A CUDA backend for Torch7 that enables GPU-accelerated tensor operations with a familiar Torch API.
A Common Lisp library for NVIDIA CUDA programming, providing a kernel description language and memory management.
A massively parallel library for training self-organizing maps on multicore CPUs, GPUs, and clusters with support for dense and sparse data.
A CPU and GPU-accelerated matrix library optimized for high-performance data mining operations.
A distributed storage benchmark tool for file systems, object stores, and block devices with GPU support.
CUDA backend implementation for Torch's neural network package, enabling GPU acceleration for deep learning models.
A Clojure library for GPU-accelerated computing using NVIDIA CUDA, enabling high-performance parallel processing.
A CUDA-based implementation of KinectFusion for real-time dense surface reconstruction and tracking using a Kinect camera.
A pure Crystal machine learning library for building and training neural networks with CPU/GPU support and PyTorch compatibility.
An open-source CAD framework for designing, simulating, and deploying deep neural networks on embedded platforms.
Dockerized hashcat with multiple backends (CUDA, OpenCL, POCL) for GPU-accelerated password recovery and hash cracking.
A PyTorch implementation of the DeepDream algorithm for generating psychedelic, dream-like images from neural network activations.
A fast, flexible, and compact deep learning framework for Julia that runs on CPU and CUDA GPU.
A set of libraries enabling native execution of D code on GPUs and other accelerators via OpenCL and CUDA runtimes.
A PPX-based DSL for writing GPU kernels in OCaml syntax that compiles to multiple backends (CUDA, OpenCL, Vulkan, Metal).
A GPU-accelerated (CUDA) C++ template library for building and training artificial neural networks, including self-organizing maps and back-propagation networks.
A library of reusable CUDA C++ software components for parallel algorithms like sorting, prefix scan, reduction, and histogram.
A pre-configured Docker image with deep learning frameworks, data science tools, and GPU support for rapid environment setup.
Standalone CPU and GPU miner for Zcash/Equihash on macOS with AVX and CUDA support.
A modern, fast, and modular deep learning and machine learning framework for Python built on PyTorch.
A learning-focused, high-performance tensor computation library built from scratch in Rust with automatic differentiation and CPU/CUDA backends.
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