Introduction to Parallel Programming with CUDA (Coursera)

Course 2 of 4 in the GPU Programming Specialization.

What You Will Learn

- S​tudents will learn how to utilize the CUDA framework to write C/C++ software that runs on CPUs and Nvidia GPUs.

- S​tudents will transform sequential CPU algorithms and programs into CUDA kernels that execute 100s to 1000s of times simultaneously on GPU hardware.

Syllabus

WEEK 1

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.

WEEK 2

Threads, Blocks and Grids

The single most important concept for using GPUs to solve complex and large-scale problems, is management of threads. CUDA provides two- and three-dimensional logical abstractions of threads, blocks and grids. Students will develop programs that utilize threads, blocks, and grids to process large 2 to 3-dimensional data sets.

WEEK 3

Host and Global Memory

To manage the access and modification of data in physical memory effectively, students will need to load data into CPU (host) and GPU (global) general-purpose memory. Students will create software that allocates host memory and transfers it into global memory for use by threads. Students will also learn the capabilities and speeds of these types of memories.

WEEK 4

Shared and Constant Memory

To improve performance in GPU software, students will need to utilized mutable (shared) and static (constant) memory. They will use them to apply masks to all items of a data set, to manage the communication between threads, and use for caching in complex programs.

WEEK 5

Register Memory

In this module, students will learn the benefits and constraints of GPUs most hyper-localized memory, registers. While using this type of memory will be natural for students, gaining the largest performance boost from it, like all forms of memory, will require thoughtful design of software. Students will develop implementations of algorithms using each type of memory and generate performance analysis.