Introduction to Concurrent Programming with GPUs
Learn parallel programming with C and Python, and optimize code for processing large data sets on CPUs and GPUs. Understand CUDA software/hardware and develop efficient software.
This course will help prepare students for developing code that can process large amounts of data in parallel. It will focus on foundational aspects of concurrent programming, such as CPU/GPU architectures, multithreaded programming in C and Python, and an introduction to CUDA software/hardware.
What you will learn
Course Overview
The purpose of this module is for students to understand how the course will be run, topics, how they will be assessed, and expectations.
Core Principles of Parallel Programming on CPUs and GPUs
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.
Introduction to Parallel Programming with C and Python
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.
NVidia GPU Hardware/Software
The purpose of this module is for students to understand the basis in hardware and software that CUDA uses. This is required to appropriately develop software to optimally take advantage of GPU resources.
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