Introduction

This is a 2 day hands on workshop for programers who want to learn how to program and utilize the parallel computing power of the Graphics Processing Unit (GPU) using NVIDIA's CUDA programming framework.

Prerequisites

The attendees will needs to have some basic C or C++ knowledge, but no prior knowledge of parallel computing concepts is necessary. A laptop will be required to remotely connect and try out programming exercises on a CUDA enabled machine. The course will be held in English.

Course Description

1. Introduction & Setup (Day 1)

The course will start by introducing the concepts of general purpose GPU programming and go into the process of installing and setting up the development environment on the 3 OS's that support CUDA. We will also talk about the different language bindings for languages like Java, Python and Ruby.

2. CUDA Basic Concepts

Then we will give a hands on introduction to CUDA.

3. Hardware & Memory

The main gist of the course will involve learning the concepts of CUDA memory management together with the hardware capability of the GPU we are developing on.

4. Streams, Atomics & Graphics (Day 2)

Once we are familiar with the core concepts, we will talk about interoperability of the CUDA library with rendering and also the use of atomic primitives to accomplish things which are quite trivial in the traditional CPU case. Then we will talk about the concept of CUDA streams.

5. Optimization Techniques

We will go into the various concepts of GPU kernel optimization to get the most out of the hardware we are planning to write code for.

6. External Libraries

We will talk about the different external libraries both 3rd party as well as provided by NVIDIA optimized for the GPU, that implement many useful algorithms for applications ranging from Finance to Medical Imaging.

7. Future (Given time)

We will talk about the future of GPU computing, in particular the new features in CUDA 4.0 and GPUs on the cloud. We will also give an introduction to OpenCL, since many of the concepts from this course will carry over to OpenCL. We will talk about the advantages and disadvantages of OpenCL here as well.