Difference between revisions of "GPU Tutorial/Julia"
GPU Tutorial/Julia
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+ CUDA.@sync @cuda(threads=nthreads, blocks=nblocks, gpu_kernel()) | + CUDA.@sync @cuda(threads=nthreads, blocks=nblocks, gpu_kernel()) | ||
| + | || Correct | ||
| + | - CUDA.parallelize(@auto) @cuda<<<nthreads, nblocks>>>gpu_kernel() | ||
|| Correct | || Correct | ||
</quiz> | </quiz> | ||
{{hidden end}} | {{hidden end}} | ||
Revision as of 14:57, 13 January 2022
| Tutorial | |
|---|---|
| Title: | Introduction to GPU Computing |
| Provider: | HPC.NRW
|
| Contact: | tutorials@hpc.nrw |
| Type: | Multi-part video |
| Topic Area: | GPU computing |
| License: | CC-BY-SA |
| Syllabus
| |
| 1. Introduction | |
| 2. Several Ways to SAXPY: CUDA C/C++ | |
| 3. Several Ways to SAXPY: OpenMP | |
| 4. Several Ways to SAXPY: Julia | |
| 5. Several Ways to SAXPY: NUMBA | |
This video discusses the SAXPY via Julia and CUDA.jl. The CUDA.jl package is the main programming interface for working with NVIDIA CUDA GPUs using Julia. It features a user-friendly array abstraction, a compiler for writing CUDA kernels in Julia, and wrappers for various CUDA libraries.