Search Results for "gpu max performance" - Page 13
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Showing 388 open source projects for "gpu max performance"
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RMGDFT
Real Space Multigrid based electronic structure code.
News: active RMG development has moved to github https://github.com/RMGDFT News: V4.1.0 released on 09/29/2020 News: V4.0.0 released on 09/01/2020 with major updates. News: V3.0.0 released on 06/09/2018 with major updates. News: V2.2.2 released on 10/14/2017 with minor bug fixes. News: V2.2 with performance enhancements, bug fixes and new features released on 06/26/2017. Sources are available with binaries to follow soon. News: V2.1 with many improvements released on... -
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VCTRenderer
A real time global illumination solution that achieves glossy surfaces
VCTRenderer is a real-time global illumination renderer built in C++ using Vulkan, leveraging voxel cone tracing (VCT) to achieve dynamic lighting, soft shadows, and indirect illumination. The renderer demonstrates cutting-edge rendering techniques aimed at achieving realistic lighting effects without precomputed data, making it ideal for games and interactive applications that need real-time performance with physically plausible lighting. It provides a clean implementation of voxelization... -
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Tiny
Tiny Face Detector, CVPR 2017
...It provides training/testing scripts, a demo (tiny_face_detector.m), model loading, evaluation on WIDER FACE, and supporting utilities (e.g. cnn_widerface_eval.m). The code depends on MatConvNet, which must be compiled (with GPU / CUDA / cuDNN support) for full performance. Pretrained model provided (ResNet101-based, plus alternatives). Demo and evaluation scripts for benchmark datasets. Use of “foveal descriptors” to incorporate context for low-resolution faces. Pretrained model provided (ResNet101-based, plus alternatives). -
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GCam Mod-M1
Modded Google Camera for ASUS Zenfone Max Pro M1
Modification of the existing ported google camera to make it more optimal and stable on the ASUS Zenfone Max Pro M1 device. -
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BytePS
A high performance and generic framework for distributed DNN training
BytePS is a high-performance and generally distributed training framework. It supports TensorFlow, Keras, PyTorch, and MXNet, and can run on either TCP or RDMA networks. BytePS outperforms existing open-sourced distributed training frameworks by a large margin. For example, on BERT-large training, BytePS can achieve ~90% scaling efficiency with 256 GPUs (see below), which is much higher than Horovod+NCCL. -
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Alive Kernel 10 Revolution Alive Kernel 9 (Discontinue) :(( I think soon i will make for pie again... •Feature On Alive Kernel• -Based on caf kernel linux Stable -Merged with new tag caf latest -OC to 2.2Ghz -Battre Friedly -Better Performance -Add more governor ~Nightmare ~Darknessv5 ~Alucard ~and more -Undervolt Cpu Gpu -80mV -Add Cpu Bost Control and More Control -Add Cpu hotplug -Add Gpu boost -Add New battery Calculation -Add Fast Charging and On/Off Charging -Add Sound Control -Add Kcal Support and more option -Add I/O Tweak and more scheduler -Lmk Optimize (4/6Gb Ram varian) -Add Boeffla Wakelocks -Add WireGuard -Optimize Zram for battery saving -Tcp Algorithm Optimize -Much More
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Bangla TTS
Bangla text to speech synthesis in python
Bangla text to speech Multilingual (Bangla, English) real-time ([almost] in a GPU) speech synthesis library. Installation -------------------------------------- * Install Anaconda * conda create -n new_virtual_env python==3.6.8 * conda activate new_virtual_env * pip install -r requirements.txt * While running for the first time, keep your internet connection on to download the weights of the speech synthesis models (>500 MB) * For... -
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imgaug
Image augmentation for machine learning experiments
imgaug is a library for image augmentation in machine learning experiments. It supports a wide range of augmentation techniques, allows to easily combine these and to execute them in random order or on multiple CPU cores, has a simple yet powerful stochastic interface and can not only augment images but also key points/landmarks, bounding boxes, heatmaps and segmentation maps. Affine transformations, perspective transformations, contrast changes, gaussian noise, dropout of regions,... -
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AutoBench
This program is a benchmark site data extraction util program
This program is a program that extracts the latest CPU, GPU, Drive and RAM performance scores and rankings from benchmark sites. The Output Data is saved as a csv, xlsx and xls file. CPU information is written by model name and score. GPU information is written by model name and score. Drive information is written by model name and score. RAM information is written by model name and score. -
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XMRig AMD
Monero AMD (OpenCL) miner
XMRig is a high-performance Monero (XMR) OpenCL miner, with the official full Windows support. -
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XMRig NVIDIA
Monero (XMR) NVIDIA miner
XMRig is high performance Monero (XMR) NVIDIA miner, with the official full Windows support. -
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maskrcnn-benchmark
Fast, modular reference implementation of Instance Segmentation
...It supports multi-GPU distributed training, mixed precision, and custom data loaders for new datasets. Built as a reference implementation, it became a foundation for the next-generation Detectron2, yet remains widely used for research needing a stable, reproducible environment. Visualization tools, model zoo checkpoints, and benchmark scripts make it easy to replicate state-of-the-art results or fine-tune models for custom tasks. -
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GPUImage 2
Framework for GPU-accelerated video and image processing
...By relying on the GPU to run these operations, performance improvements of 100X or more over CPU-bound code can be realized. This is particularly noticeable in mobile or embedded devices. On an iPhone 4S, this framework can easily process 1080p video at over 60 FPS. On a Raspberry Pi 3, it can perform Sobel edge detection on live 720p video at over 20 FPS. -
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Imogen
GPU Texture Generator
Imogen is a real-time, node-based procedural texture generation tool aimed at artists, developers, and shader enthusiasts. It allows users to build complex material textures using a graph-based interface, combining operations like blending, noise, filters, and color correction in a non-destructive workflow. Built with Vulkan and ImGui, Imogen provides immediate visual feedback and supports GPU acceleration for high-resolution texture output. It's particularly useful in game development, VFX,... -
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Effeckt.css
A Performant Transitions and Animations Library
Effeckt.css is a showcase and toolkit of high-performance UI transitions and animations for the web. It catalogs common interaction patterns—button states, list reveals, modals, off-canvas menus, and page transitions—implemented with CSS transforms and opacity for smooth, GPU-friendly motion. The project focuses on practical details such as using compositing-friendly properties, keeping DOM structures lean, and providing hooks so JavaScript can toggle classes without micromanaging animation state. ... -
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Tensorpack
A Neural Net Training Interface on TensorFlow, with focus on speed
...On common CNNs, it runs training 1.2~5x faster than the equivalent Keras code. Your training can probably gets faster if written with Tensorpack. Scalable data-parallel multi-GPU / distributed training strategy is off-the-shelf to use. Squeeze the best data loading performance of Python with tensorpack.dataflow. Symbolic programming (e.g. tf.data) does not offer the data processing flexibility needed in research. Tensorpack squeezes the most performance out of pure Python with various auto parallelization strategies. ... -
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DarkMagic
This is a Custom Kernel for Redmi Note 5 Pro a.k.a Whyred [PIE]
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Video Nonlocal Net
Non-local Neural Networks for Video Classification
video-nonlocal-net implements Non-local Neural Networks for video understanding, adding long-range dependency modeling to 2D/3D ConvNet backbones. Non-local blocks compute attention-like responses across all positions in space-time, allowing a feature at one frame and location to aggregate information from distant frames and regions. This formulation improves action recognition and spatiotemporal reasoning, especially for classes requiring context beyond short temporal windows. The repo... -
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Mixup-CIFAR10
mixup: Beyond Empirical Risk Minimization
mixup-cifar10 is the official PyTorch implementation of “mixup: Beyond Empirical Risk Minimization” (Zhang et al., ICLR 2018), a foundational paper introducing mixup, a simple yet powerful data augmentation technique for training deep neural networks. The core idea of mixup is to generate synthetic training examples by taking convex combinations of pairs of input samples and their labels. By interpolating both data and labels, the model learns smoother decision boundaries and becomes more... -
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RpiOptimisation
Performance optimization tool for Raspberry Pi.
Support : [Raspberry Pi 0-1-2-3] [EN] Performance optimization tool for Raspberry Pi (CPU,GPU,SD card read and write speed) by model. [FR] Outil d'optimisation des performances pour Raspberry Pi (CPU, GPU, vitesse de lecture et d'écriture de la carte SD) par modèle. -
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xmrMiner
A CUDA based miner for Monero
An optimized Monero miner designed to maximize GPU mining efficiency. -
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Microtonal Organ
Software organ created with Max and fluidsynth.
This is a soundfont instrument for performance and experiments with intonation systems. It is set up like an organ, while any instrument available in the sf2-format can be used. A database of several hundred tunings are included, and can be expanded by the user. Sources of these tunings are described in the manual. This program is made with Max 7, and standalones are provided for Mac Osx Yosemite and High Sierra. -
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DIGITS
Deep Learning GPU training system
The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. ... -
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SLFS
Simple Log Structured Filesystem for Linux
SLFS is a implementation of log-structured file system designed for flash memory based storages(SSD, SD card, eMMC, …). Like other log-structured file systems, SLFS shows good performance under random write. <Build> 1. deploy SLFS source code at your Kernel Source # tar xzf slfs.tar.gz # mv slfs KERNEL_SRC/fs/. # vi KERNEL_SRC/fs/Makefile (add following line) obj-$(CONFIG_SLFS_FS) += slfs/ # vi KERNEL_SRC/fs/Kconfig (find section “MISC_FILESYSTEMS” and add following... -
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Mocha.jl
Deep Learning framework for Julia
...It offers efficient implementations of gradient descent solvers and common neural network layers, supports optional unsupervised pre-training, and allows switching to a GPU backend for accelerated performance. The development of Mocha.jl happens in relative early days of Julia. Now that both Julia and the ecosystem has evolved significantly, and with some exciting new tech such as writing GPU kernels directly in Julia and general auto-differentiation supports, the Mocha codebase becomes excessively old and primitive. ...
