In this 4-days online workshop you will learn how to accelerate your applications with OpenACC, CUDA C/C++ and CUDA Python on NVIDIA GPUs.

The workshop combines lectures about Fundamentals of Accelerated Computing with OpenACC, CUDA C/C++ and Python on a single GPU with a lecture about Accelerating CUDA C++ Applications with Multiple GPUs.

The lectures are interleaved with many hands-on sessions using Jupyter Notebooks. The exercises will be done on a fully configured GPU-accelerated workstation in the cloud.

The workshop is co-organised by Leibniz Supercomputing Centre (LRZ), Erlangen National High Performance Computing Center (NHR@FAU) and NVIDIA Deep Learning Institute (DLI). NVIDIA DLI offers hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning.

All instructors are NVIDIA certified University Ambassadors.

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

2nd day: Fundamentals of Accelerated Computing with CUDA C/C++

This lecture teaches the fundamental tools and techniques for accelerating C/C++ applications to run on massively parallel GPUs with CUDA. You’ll learn how to write code, configure code parallelisation with CUDA, optimise memory migration between the CPU and GPU accelerator, and implement the workflow that you’ve learned on a new task—accelerating a fully functional, but CPU-only, particle simulator for observable massive performance gains. At the end of the lecture, you will be able to create new GPU-accelerated applications on your own.

4th day: Accelerating CUDA C++ Applications with Multiple GPUs

Computationally intensive CUDA C++ applications in high-performance computing, data science, bioinformatics, and deep learning can be accelerated by using multiple GPUs, which can increase throughput and/or decrease your total runtime. When combined with the concurrent overlap of computation and memory transfers, computation can be scaled across multiple GPUs without increasing the cost of memory transfers. For organisations with multi-GPU servers, whether in the cloud or on NVIDIA DGX systems, these techniques enable you to achieve peak performance from GPU-accelerated applications. And it’s important to implement these single-node, multi-GPU techniques before scaling your applications across multiple nodes. 

This lecture covers how to write CUDA C++ applications that efficiently and correctly utilise all available GPUs in a single node, dramatically improving the performance of your applications and making the most cost-effective use of systems with multiple GPUs.

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NVIDIA Deep Learning Institute

The NVIDIA Deep Learning Institute delivers hands-on training for developers, data scientists, and engineers. The program is designed to help you get started with training, optimising, and deploying neural networks to solve real-world problems across diverse industries such as self-driving cars, healthcare, online services, and robotics.