Difference between revisions of "GPU Tutorial/SAXPY CUDA C"

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GPU Tutorial/SAXPY CUDA C
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([[Media:GPU_tutorial_saxpy_cuda_c.pdf |Slides as pdf]])
 
([[Media:GPU_tutorial_saxpy_cuda_c.pdf |Slides as pdf]])
  
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=== Quiz === <!--T:5-->   
+
=== Introduction Quiz === <!--T:5-->   
 
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{{hidden begin  
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|title = 1. For which kind of programm can we expect improvements with GPUs?}}
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|title = 1. For which kind of program can we expect improvements with GPUs?}}
 
<quiz display=simple>
 
<quiz display=simple>
 
{
 
{
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+ All of the above
 
+ All of the above
 
|| Correct!
 
|| Correct!
 +
</quiz>
 +
{{hidden end}}
 +
 +
 +
=== Quiz === <!--T:5--> 
 +
{{hidden begin
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|title = 1. Which features does CUDA add to C/C++?}}
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<quiz display=simple>
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{
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|type="()"}
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- functions
 +
|| CUDA does not only add new functions, but all of these features.
 +
- syntax
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|| CUDA does not only add new syntax, but all of these features.
 +
- GPU support
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|| CUDA does not only add GPU support, but all of these features.
 +
+ All of the above
 +
|| Correct
 +
</quiz>
 +
{{hidden end}}
 +
 +
 +
{{hidden begin
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|title = 2. What is a kernel?}}
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<quiz display=simple>
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{
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|type="()"}
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- It's a flag you can set to automatically parallelize any function.
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|| Unfortunately, life is not that easy.
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+ It's the part of your code that is run on the GPU.
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|| Correct
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- It's a new CUDA function that activates the GPU.
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|| Wrong
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</quiz>
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{{hidden end}}
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 +
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{{hidden begin
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|title = 3. How do you flag a function to be a kernel?}}
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<quiz display=simple>
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{
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|type="()"}
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- __host__
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|| Wrong
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- __device__
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|| Wrong
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+ __global__
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|| Correct
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- __GPU__
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|| Wrong
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</quiz>
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{{hidden end}}
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 +
{{hidden begin
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|title = 4. Let's say you coded you kernel function called "MyKernel". How do you run it?}}
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<quiz display=simple>
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{
 +
|type="()"}
 +
- MyKernel()
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|| Wrong
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- CUDA_GPU_run(NoBlocks,NoThreads())
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|| Wrong
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+ <<<NoBlocks,NoThreads>>>MyKernel()
 +
|| Correct
 +
- __global(NoBlocks,NoThreads)__ MyKernel()
 +
|| Wrong
 
</quiz>
 
</quiz>
 
{{hidden end}}
 
{{hidden end}}

Revision as of 11:52, 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

(Slides as pdf)

Introduction Quiz

1. For which kind of program can we expect improvements with GPUs?

serial programs
parallel programs


2. What does GPU stands for?

graphics processing unit
grand powerful unit


3. Why do we expect an onverhead in the GPU timings?

The data must be copied to an extra device first and has to be transferred back later
A GPU core is "weaker" than a CPU core
For "small" problems like the SAXPY, the whole power of a GPU is rarely used
All of the above


Quiz

1. Which features does CUDA add to C/C++?

functions
syntax
GPU support
All of the above


2. What is a kernel?

It's a flag you can set to automatically parallelize any function.
It's the part of your code that is run on the GPU.
It's a new CUDA function that activates the GPU.


3. How do you flag a function to be a kernel?

__host__
__device__
__global__
__GPU__

4. Let's say you coded you kernel function called "MyKernel". How do you run it?

MyKernel()
CUDA_GPU_run(NoBlocks,NoThreads())
<<<NoBlocks,NoThreads>>>MyKernel()
__global(NoBlocks,NoThreads)__ MyKernel()

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