There are 4 repositories under multi-gpu topic.
The Forge Cross-Platform Rendering Framework PC Windows, Steamdeck (native), Ray Tracing, macOS / iOS, Android, XBOX, PS4, PS5, Switch, Quest 2
Toolkit for efficient experimentation with Speech Recognition, Text2Speech and NLP
Multi-threaded GUI manager for mass creation of AI-generated art with support for multiple GPUs.
Extract video features from raw videos using multiple GPUs. We support RAFT flow frames as well as S3D, I3D, R(2+1)D, VGGish, CLIP, ResNet features.
GPU-ready Dockerfile to run Stability.AI stable-diffusion model v2 with a simple web interface. Includes multi-GPUs support.
Package for writing high-level code for parallel high-performance stencil computations that can be deployed on both GPUs and CPUs
A PyTorch implementation of the 'FaceNet' paper for training a facial recognition model with Triplet Loss using the glint360k dataset. A pre-trained model using Triplet Loss is available for download.
Code for training py-faster-rcnn and py-R-FCN on multiple GPUs in caffe
Distributed tensors and Machine Learning framework with GPU and MPI acceleration in Python
multi-gpu pre-training in one machine for BERT from scratch without horovod (Data Parallelism)
Chains stable-diffusion-webui instances together to facilitate faster image generation.
Almost trivial distributed parallelization of stencil-based GPU and CPU applications on a regular staggered grid
Efficient Distributed GPU Programming for Exascale, an SC/ISC Tutorial
Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
Multi-device OpenCL kernel load balancer and pipeliner API for C#. Uses shared-distributed memory model to keep GPUs updated fast while using same kernel on all devices(for simplicity).
Neutron: A pytorch based implementation of Transformer and its variants.
A dual-GPU DEM solver with complex grain geometry support
<케라스 창시자에게 배우는 딥러닝 2판> 도서의 코드 저장소
:dart: Accumulated Gradients for TensorFlow 2
Co-attending Regions and Detections for VQA.
GPU Framework for Radio Astronomical Image Synthesis
Artifact for OSDI'23: MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication-Computation Pipelining on Multi-GPU Platforms.
Deep Learning Toolbox for Torch
Graphic Techniques Implemented on The Forge API, a cross-platform rendering framework on top of Vulkan, DirectX, Metal
Source code for the CPU-Free model - a fully autonomous execution model for multi-GPU applications that completely excludes the involvement of the CPU beyond the initial kernel launch.
Deep Neural Network Compression based on Student-Teacher Network
Distributed training with Multi-worker & Parameter Server in TensorFlow 2
MelGAN Multi GPU Implementation.
Damavand is a quantum circuit simulator. It can run on laptops or High Performance Computing architectures, such CPU distributed architectures or multi GPU distributed architectures.