Difference between revisions of "GPU Tutorial/SAXPY CUDA C"
GPU Tutorial/SAXPY CUDA C
Jump to navigation
Jump to search
m |
m |
||
| Line 20: | Line 20: | ||
{ | { | ||
|type="()"} | |type="()"} | ||
| − | - functions | + | - new functions |
|| CUDA does not only add new functions, but all of these features. | || CUDA does not only add new functions, but all of these features. | ||
| − | - syntax | + | - new syntax |
|| CUDA does not only add new syntax, but all of these features. | || CUDA does not only add new syntax, but all of these features. | ||
- GPU support | - GPU support | ||
| Line 53: | Line 53: | ||
|type="()"} | |type="()"} | ||
- __host__ | - __host__ | ||
| − | || Wrong | + | || Wrong. This specifies a function that runs on the CPU. |
- __device__ | - __device__ | ||
| − | || Wrong | + | || Wrong. This indeed does specify a function that runs on the GPU, but it also needs to be called from the GPU, while we want a kernel to be launched by the CPU. |
+ __global__ | + __global__ | ||
|| Correct | || Correct | ||
- __GPU__ | - __GPU__ | ||
| − | || Wrong | + | || Wrong. This modifier doesn't exist. |
</quiz> | </quiz> | ||
{{hidden end}} | {{hidden end}} | ||
| Line 69: | Line 69: | ||
|type="()"} | |type="()"} | ||
- MyKernel() | - MyKernel() | ||
| − | || Wrong | + | || Wrong. This would just execute an ordinary function. |
- CUDA.run(NoBlocks, NoThreads, MyKernel()) | - CUDA.run(NoBlocks, NoThreads, MyKernel()) | ||
| − | || Wrong | + | || Wrong. There is no CUDA.run() |
+ <<<NoBlocks, NoThreads>>>MyKernel() | + <<<NoBlocks, NoThreads>>>MyKernel() | ||
|| Correct | || Correct | ||
- __global(NoBlocks, NoThreads)__ MyKernel() | - __global(NoBlocks, NoThreads)__ MyKernel() | ||
| − | || Wrong | + | || Wrong. __global__ and other modifiers cant have arguments and are part of a function definition, not launch. |
</quiz> | </quiz> | ||
{{hidden end}} | {{hidden end}} | ||
| Line 90: | Line 90: | ||
|| Correct | || Correct | ||
- If you use array-element-wise operations (like y.=a.*x.+b ), this is managed by the NVIDIA preprocessor. | - If you use array-element-wise operations (like y.=a.*x.+b ), this is managed by the NVIDIA preprocessor. | ||
| − | || Wrong | + | || Wrong. There are no element-wise operators in C/C++ |
- You flag a line to be parallelized via keywords, e.g.: __device__ y=a*x+b | - You flag a line to be parallelized via keywords, e.g.: __device__ y=a*x+b | ||
| − | || Wrong | + | || Wrong. These modifiers are used at function definitions. |
</quiz> | </quiz> | ||
{{hidden end}} | {{hidden end}} | ||
Revision as of 12:41, 11 November 2021
| 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 NVIDIA CUDA C/C++.
Video
Quiz
1. Which features does CUDA add to C/C++?
2. What is a kernel?
3. How do you flag a function to be a kernel?
4. Let's say you coded your kernel function called "MyKernel". How do you run it?
5. Inside your kernel function, how do you distribute your data over the threads?
Introduction Quiz
1. For which kind of program can we expect improvements with GPUs?
2. What does GPU stands for?
3. Why do we expect an overhead in the GPU timings?