1^st day: Lecture on Accelerated Computing with OpenACC

• How to profile and optimize your CPU-only applications to identify hot spots for acceleration
• How to use OpenACC directives to GPU accelerate your codebase
• How to optimize data movement between the CPU and GPU accelerator

Upon completion, you'll be ready to use OpenACC to GPU accelerate CPU-only applications.

2^nd day: Introduction to Deep Learning

• Implement common deep learning workflows, such as image classification and object detection
• Experiment with data, training parameters, network structure, and other strategies to increase performance and capability
• Deploy your neural networks to start solving real-world problems

Upon completion, you’ll be able to start solving problems on your own with deep learning.

3^rd day: Introduction to Deep Learning for Multi-GPUs

• Approaches to multi-GPUs training
• Algorithmic and engineering challenges to large-scale training
• Key techniques used to overcome the challenges mentioned above

Upon completion, you'll be able to effectively parallelize training of deep neural networks using TensorFlow.

1^st day: Lecture on Accelerated Computing with OpenACC (9:00 - 17:00)

On the first day you learn the basics of OpenACC, a high-level programming language for programming on GPUs. Discover how to accelerate the performance of your applications beyond the limits of CPU-only programming with simple pragmas.

2^nd day: Introduction to Deep Learning  (9:00 - 17:00)

Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities.

During this day, you’ll learn the basics of deep learning by training and deploying neural networks.

3^rd day: Introduction to Deep Learning for Multi-GPUs (9:00 - 17:00)

The computational requirements of deep neural networks used to enable AI applications like self-driving cars are enormous. A single training cycle can take weeks on a single GPU or even years for larger datasets like those used in self-driving car research. Using multiple GPUs for deep learning can significantly shorten the time required to train lots of data, making solving complex problems with deep learning feasible.

On the third day we will teach you how to use multiple GPUs to train neural networks.