CUDA brings together several things: Massively parallel hardware designed to run generic (non-graphic) code, with appropriate drivers for doing so. A programming language based on C for programming said hardware, and an assembly language that other programming languages can use as a target. A software development kit that includes libraries, various debugging, profiling and compiling tools ...
CUDA Programming and Performance General discussion area for algorithms, optimizations, and approaches to GPU Computing with CUDA C, C++, Thrust, Fortran, Python (pyCUDA), etc. CUDA NVCC Compiler Discussion forum for CUDA NVCC compiler.
I’m a career software engineer but a novice to CUDA/GPU programming. This post may belong in another topic category (its own?) under CUDA Developer Tools, but I don’t see a way to create one. I found the Altimesh Essentials package, which comes tantalizingly close to what I want to do, which is interface to my GPU via CUDA from C#. The main gap I see is the package was last committed 2-3 ...
Question: Is there an emulator for a Geforce card that would allow me to program and test CUDA without having the actual hardware? Info: I'm looking to speed up a few simulations of mine in CUDA, ...
Usually, do people read the CUDA C Programming Guide reference manual from Nvidia completely to have a rough grasp of CUDA (to know what is available in the toolbox named CUDA), or do they read CUDA-related books (like CUDA by Example by Sanders et al., etc)? Or look into the manual as and when required, avoiding reading the entire book or manual.
CUDA Fortran is a Fortran compiler with CUDA extensions, along with a host API. PyCUDA is more of a host API and convenience utilities, but kernels still have to be written in CUDA C++. "CUDA Python", part of Numba, is a compiler for CUDA-annotated Python to run on GPUs.
Would you rather learn CUDA programming concepts (threads, blocks, grids, streams, shared memory, and so on) in CUDA C/C++ or the newly released CUDA Python? You can read more about CUDA Python in this great Anandtech article: NVIDIA and Continuum Analytics Announce NumbaPro, A Python CUDA Compiler
I’d like to know how to compile and run them Information is given at that link. Particularly the requirements, building, and run sections. For example, to get started with building the mpi variant, you would git clone the repo, then cd multi-gpu-programming-models/mpi. At that point you’ll want to change the gencode flags to match your GPU architecture (s), then make. You’ll need to make ...
I am a new beginner for CUDA. I have installed CUDA tools, but I have found that there are many problems hasn’t been mentioned in doc. E.g. when using nvcc to compile .cu file, you need to specify -arch or your binary will probably not work. Is there an online platform provide the basic environment for us? Just like google colab for Python.
