Difference between revisions of "GPU Tutorial/Open MP"
GPU Tutorial/Open MP
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| − | [[Category:Tutorials|GPU Computing ( | + | [[Category:Tutorials|GPU Computing (OpenMP)]]<nowiki /> |
| − | {{DISPLAYTITLE:GPU Computing ( | + | {{DISPLAYTITLE:GPU Computing (OpenMP)}}<nowiki /> |
{{Syllabus Introduction to GPU Computing}}<nowiki /> | {{Syllabus Introduction to GPU Computing}}<nowiki /> | ||
__TOC__ | __TOC__ | ||
| − | This video discusses the SAXPY via | + | This video discusses the SAXPY via OpenMP GPU offloading. |
OpenMP 4.0 and later enables developers to program GPUs in C/C++ and Fortran by means of OpenMP directives. In this tutorial we present the basic OpenMP syntax for GPU offloading and give a step-by-step guide for implementing SAXPY with it. | OpenMP 4.0 and later enables developers to program GPUs in C/C++ and Fortran by means of OpenMP directives. In this tutorial we present the basic OpenMP syntax for GPU offloading and give a step-by-step guide for implementing SAXPY with it. | ||
Revision as of 11:18, 3 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 OpenMP GPU offloading. OpenMP 4.0 and later enables developers to program GPUs in C/C++ and Fortran by means of OpenMP directives. In this tutorial we present the basic OpenMP syntax for GPU offloading and give a step-by-step guide for implementing SAXPY with it.
Video
Quiz
1. Which one of the following OpenMP directives can create a target region on GPU?
2. The OpenMP `map(to:...)` clause maps variables:
3. Which one of the following OpenMP directives can initialize a league of teams for execution on GPU?
4. Which one of the following OpenMP directives can distribute iterations of for-loop across GPU threads in the teams?