Professional CUDA C Programming Included here are the code files for any samples used in the chapters as illustrative examples. Each chapter has its own code folder that includes the sample .c and .cu files for that chapter. The per-chapter folders each also include a Makefile that can be ...
这是一个学习笔记,PDF可以从 这里 下载,这个repo 是 fork 自 mapengfei-nwpu/ProfessionalCUDACProgramming。 Chapter 1 Heterogeneous Parallel Computing with CUDA 在这一章里面,讨论了: 异构编程架构 并行编程(parallel programming)的范式 GPU 编程的一点点基础 CPU 和 GPU编程的不同 作者是从 HPC (High Perfor...
这是一个学习笔记,PDF可以从这里下载,这个repo 是 fork 自mapengfei-nwpu/ProfessionalCUDACProgramming。 Chapter 2 CUDA Programming Model 在这一章里面会学习 CUDA 的编程模型: 写一个 CUDA 程序 执行一个 CUDA kernel 核函数 通过grids 和 blocks 管理线程 评估GPU 的性能 CUDA编程模型 关于编程模型的定义,总...
InvokeAI is supported across Linux, Windows and macOS. Linux users can use either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm driver).SystemYou will need one of the following:An NVIDIA-based graphics card with 4 GB or more VRAM memory. 6-8 GB of VRAM is...
cupy- NumPy-like API accelerated with CUDA thrust- Thrust is a C++ parallel programming library which resembles the C++ Standard Library. ArrayFire- ArrayFire: a general purpose GPU library. OpenMP- OpenMP is an application programming interface that supports multi-platform shared memory multiprocessing...
CUDA, and MPI from industry leaders in the field ● Optimized legacy Fortran77 MPI code using hardware-software features to attain an up to 20% speedup ● Co-presented poster "Analyzing the Scalability of Nek5000" to the University of Illinois parallel community National Aeronautics and Space Adm...
Professional CUDA C Programming Included here are the code files for any samples used in the chapters as illustrative examples. Each chapter has its own code folder that includes the sample .c and .cu files for that chapter. The per-chapter folders each also include a Makefile that can be ...
InvokeAI is supported across Linux, Windows and macOS. Linux users can use either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm driver).SystemYou will need one of the following:An NVIDIA-based graphics card with 4 GB or more VRAM memory. 6-8 GB of VRAM is...
We publish official container images in Github Container Registry: https://github.com/invoke-ai/InvokeAI/pkgs/container/invokeai. Both CUDA and ROCm images are available. Check the above link for relevant tags.Important Ensure that Docker is set up to use the GPU. Refer to NVIDIA or AMD ...
InvokeAI is supported across Linux, Windows and macOS. Linux users can use either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm driver).SystemYou will need one of the following:An NVIDIA-based graphics card with 4 GB or more VRAM memory. 6-8 GB of VRAM is...