GPU Computing (CUDA C)

This video discusses the SAXPY via NVIDIA CUDA C/C++. CUDA is an application programming interface (API) for NVIDIA GPUs. In general, CUDA works with many programming languages, but this tutorial is going to focus on C/C++. CUDA gives access to a GPUs instruction set, which means we have to go through everything step-by-step, since many things do not happen automatically.

Video

(Slides as pdf)

Quiz

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

	new functions
	new 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 your kernel function called "MyKernel". How do you run it?

	MyKernel();
	CUDA.run(NoBlocks, NoThreads, MyKernel());
	<<<NoBlocks, NoThreads>>>MyKernel();
	__global(NoBlocks, NoThreads)__ MyKernel();

5. Inside your kernel function, how do you distribute your data over the GPU threads?

	You don't have to, CUDA does that automatically for you.
	Each thread has has an index attached to it, which is addressed via threadIdx.x
	If you use array-element-wise operations, e.g.: y.=a.*x.+b . This is managed by the NVIDIA preprocessor.
	You flag a line to be parallelized via keywords, e.g.: __device__ y=a*x+b

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 overhead 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