Search Results for "gpu processing" - Page 5

This library makes it possible to reuse the LCD/button frontpanels of Canon Pixma MP620 / MP630 inkjet printers in your own projects. The library makes use of the processing power and RAM that is built into the frontpanels. The library is written for maple / olimexino but might be expanded in the future. Please see the wiki for info on how the display works, the protocol used and how to connect your display to your board: http://sourceforge.net/p/pixmafrontpanel/wiki/Home/

GPU-accelerated LIBSVM is a modification of the original LIBSVM that exploits the CUDA framework to significantly reduce processing time while producing identical results. The functionality and interface of LIBSVM remains the same.

GPUBench2 is a cross platform suite to analyze the performance of GPU( Graphics Processing Unit). GPUBench2 will gather of state-of-the-art parameters from different interfaces ( OpenGL, Cg, CUDA) available.

Qaquarelle is the opensource Qt4-based graphical editor, whose goal is to provide the native way of painting with emulated traditional instruments, including the full support of tablet input and OpenGL-based processing in GPU.

This code is provided as supplementary material for the book chapter "Exploiting graphics processing units for computational biology and bioinformatics," by Payne, Sinnott-Armstrong, and Moore.

The GWO library is numerical calculation library for the diffraction integrals using a GPU (Graphics Processing Unit). If optics engineers and researchers have no knowledge of GPU, the GWO library provides them with the GPU calculation power easily.

BrokenFinger is a collection of demo 3D programs showing latest implementation in the areas of: Document Object Model, GPU Bound Processing, and Procedural Modeling(e.g. CGA Shape).

proGPUKLT is a library for the Processing programming language and environment that wraps a GPU-implementation of the Kanade-Lucas-Tomasi feature tracker used for computer vision applications.

An OS/hardware vendor independent GPU accelerated image and video processing library written in C. Interface allows easy combination and manipulation of customizable filters. Works with an active OpenGL context.

GLEWpy aims to bring advanced OpenGL extensions to Python. This allows the Python OpenGL developer to use features such as fragment/vertex shaders and image processing on the GPU. It serves as a compliment to PyOpenGL and toolkits such as GLUT and SDL.

GPUVision is a framework for creating GPU based general purpose programs, image processing programs, and computer vision programs in C++. Supported libraries include matrix operations, graph partitioning, kernels, corner detection, edge detection etc.

Zen3D is a full 3D engine and editor to create games. It supports DX12/11/GL/Vulkan & Metal. It has a bespoke scripting language called ZenScript. It includes RTX support for Raytraced or Hybrid rendering.

...Unlike other tools, it provides significant memory efficiency compared to the TorchAudio forced alignment API. Users can integrate it easily through the Python package ctc-forced-aligner, and it supports GPU acceleration via PyTorch. The alignment pipeline includes audio processing, emission generation, tokenization, and span detection, making it suitable for speech analysis, transcription syncing, and dataset creation. This model is especially useful for researchers and developers working with low-resource languages or building multilingual speech systems.

Ministral 3 8B Instruct 2512 is a balanced, efficient model in the Ministral 3 family, offering strong multimodal capabilities within a compact footprint. It combines an 8.4B-parameter language model with a 0.4B vision encoder, enabling both text reasoning and image understanding. This FP8 instruct-fine-tuned variant is optimized for chat, instruction following, and structured outputs, making it ideal for daily assistant tasks and lightweight agentic workflows. Designed for edge deployment,...

...As the base pretrained model, it is not fine-tuned for instructions or reasoning, making it the ideal foundation for custom post-training, domain adaptation, or specialized downstream tasks. The model is fully optimized for edge deployment and can run locally on a single GPU, fitting in 16GB VRAM in BF16 or less than 8GB when quantized. It supports dozens of languages, making it practical for multilingual, global, or distributed environments. With a large 256k token context window, it can handle long documents, extended inputs, or multi-step processing workflows even at its small size.

Ministral 3 14B Instruct 2512 is the largest model in the Ministral 3 family, delivering frontier performance comparable to much larger systems while remaining optimized for edge-level deployment. It combines a 13.5B-parameter language model with a 0.4B-parameter vision encoder, enabling strong multimodal understanding in both text and image tasks. This FP8 instruct-tuned variant is designed specifically for chat, instruction following, and agentic workflows with robust system-prompt...