Hands-on workshop CUDA-Q NVIDIA in RWTH Aachen University & Technische Universität Berlin, June 2024.
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Updated
Jun 12, 2024 - Jupyter Notebook
Hands-on workshop CUDA-Q NVIDIA in RWTH Aachen University & Technische Universität Berlin, June 2024.
Distributed tensors and Machine Learning framework with GPU and MPI acceleration in Python
High-level C++ for Accelerator Clusters
Chains stable-diffusion-webui instances together to facilitate faster image generation.
Multi-GPU & CPU OpenCL kernel executor with load-balancing as if there is one big GPU.
A dual-GPU DEM solver with complex grain geometry support
POT3D: High Performance Potential Field Solver
Almost trivial distributed parallelization of stencil-based GPU and CPU applications on a regular staggered grid
Package for writing high-level code for parallel high-performance stencil computations that can be deployed on both GPUs and CPUs
The Forge Cross-Platform Rendering Framework PC Windows, Steamdeck (native), Ray Tracing, macOS / iOS, Android, XBOX, PS4, PS5, Switch, Quest 2
GPU Framework for Radio Astronomical Image Synthesis
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, and TIMM models.
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.
Multi-threaded GUI manager for mass creation of AI-generated art with support for multiple GPUs.
Artifact for OSDI'23: MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication-Computation Pipelining on Multi-GPU Platforms.
<케라스 창시자에게 배우는 딥러닝 2판> 도서의 코드 저장소
🎯 Accumulated Gradients for TensorFlow 2
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