RAJA
cuda-samples
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| RAJA | cuda-samples | |
|---|---|---|
| 2 | 15 | |
| 437 | 5,306 | |
| 2.1% | 6.9% | |
| 9.7 | 4.9 | |
| 6 days ago | 19 days ago | |
| C++ | C | |
| BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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RAJA
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Cuda application question
Since the ability to use C++ parallel algorithms on the GPU is a relatively new thing, some applications have used other C++ abstraction libraries instead, such as Kokkos (https://kokkos.org/) and RAJA (https://github.com/LLNL/RAJA). These both have multiple backends that support GPUs and CPUs without needing to change your application code.
- Kokkos C++ Performance Portability Programming EcoSystem
cuda-samples
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Is anyone successfully using an RTX 3000-series under WSL2?
installing, building, and running WSL CUDA examples from https://github.com/nvidia/cuda-samples
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Updated Install Instructions Dec 2022
After which nvcc should be accessible to new sessions, and you can build C++ cuda stuff like cuda-samples. Python packages like pytorch should also see CUDA and be able to use it.
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Virtual Memory Management APIs for NVIDIA GPUs on Windows
I haven't found any note that these APIs do not support Windows, and it also seems that the memMapIPCDrv CUDA sample supports Windows.
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ROS with CUDA on windows
I installed nvidia-cuda-toolkit and tried to build https://github.com/NVIDIA/cuda-samples but I'm getting stupid errors... it installed nvccat /usr/bin/nvcc and the samples expect /usr/local/cuda/bin/nvcc... symlinking it to that location and it dies with
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Script to install nvidia drivers , cuda/nvcc, gcc11 and setup on Fedora 36
Can build the cuda-samples, then you have a working nvcc.
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Can't get some CUDA Samples to work
I have installed cuda and cudnn, and was testing the installation with the cuda-samples, as the Arch Wiki suggested. But, I am not able to get samples like nbody, smokeparticles, Mandelbrot, etc. to run. Although devicequery works fine, and I get the expected output, so I think there is not a problem with my cuda installation.
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Cuda application question
Hi, I don't have much experience with Nvidia Jetsons. You can find some examples on GitHub (here https://github.com/NVIDIA/cuda-samples). You can find CUDA implementations of most functions on the internet though, you just have to look for the specific thing you are looking for. Cuda kernels are not platform specific, they should work on GPUs and embedded developer boards without problems as long as you respect the limits imposed by the "compute capability" of your device, you just have to compile your code using the right architecture flag. The biggest limit you have to deal with when developing for Jetson nano is the low amount of memory.
- My GPU-accelerated raytracing renderer
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Tutorial for ubuntu 20.04
—> git clone https://github.com/NVIDIA/cuda-samples.git —> cd cuda-samples/Samples/1_Utilities/deviceQuery/ —> make —> ./deviceQuery (Result=pass?good) —> cd ~/ —> wget https://repo.anaconda.com/archive/Anaconda3-2021.11-Linux-x86_64.sh
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cuda_kde_depth_packet_processor.cu:39:10: fatal error: helper_math.h: File or directory not found
is this the source code that u are talking about ? : https://github.com/NVIDIA/cuda-samples ? I dont see any CMakeLists.txt inside...
What are some alternatives?
kokkos - Kokkos C++ Performance Portability Programming Ecosystem: The Programming Model - Parallel Execution and Memory Abstraction
VkFFT - Vulkan/CUDA/HIP/OpenCL/Level Zero/Metal Fast Fourier Transform library
mfem - Lightweight, general, scalable C++ library for finite element methods
catboost - A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
CHAI - Copy-hiding array abstraction to automatically migrate data between memory spaces
geodesic_raytracing
Umpire - An application-focused API for memory management on NUMA & GPU architectures
hashcat - World's fastest and most advanced password recovery utility
Vc - SIMD Vector Classes for C++
nvidia-auto-installer-for-fedora-linux - A CLI tool which lets you install proprietary NVIDIA drivers and much more easily on Fedora Linux (32 or above and Rawhide)
blender-cuda-subdivision-surface-gpu - A Blender 3.0.0 fork that will allow you to subdivide complex meshes using CUDA compatible GPUs. (WIP)
ros-noetic - vinca configuration files for ros-noetic