In order to create software that process greater amounts of data at faster speeds, software operating systems, programming languages, and frameworks require strategies for accessing and modification of data in a manner that maximizes speed, while minimizing the possibility of data being in incorrect states. In this module, students will be presented canonical concurrency problems such as the Dining Philosophers. Additionally, they will learn how operating systems and programming languages handle these problems, and discuss real world big data concurrency applications.

Modern programming languages allow developers to create software with complex logic for manipulation of data in parallel, taking advantage of the multiple CPU cores in most computers. Students will develop simple software, written in the C++ and Python 3 programming languages, that process data sets concurrently.

In order to write effective GPU-accelerated software, a developer must first understand the underlying hardware architecture and the software ecosystem that enables it. In this module, students will be presented with the key hardware architectures of NVidia GPUs. Additionally, they will learn about the CUDA software layers, the installation and compilation process, and the core differences between the Runtime and Driver APIs.

The purpose of this module is to transition from theory to practice by introducing the fundamentals of GPU programming. Students will learn the essential CUDA software keywords and syntax required for writing device code. They will also be guided through best practices for structuring both simple and complex projects, applying these concepts in hands-on assignments within common development environments.