This course introduces concepts, languages, techniques, and patterns for programming heterogeneous, massively parallel processors. Its contents and structure have been significantly revised based on the experience gained from its initial offering in 2012. It covers heterogeneous computing architectures, data-parallel programming models, techniques for memory bandwidth management, and parallel algorithm patterns.

All computing systems, from mobile to supercomputers, are becoming heterogeneous, massively parallel computers for higher power efficiency and computation throughput. While the computing community is racing to build tools and libraries to ease the use of these systems, effective and confident use of these systems will always require knowledge about low-level programming in these systems. This course is designed for students to learn the essence of low-level programming interfaces and how to use these interfaces to achieve application goals. CUDA C, with its good balance between user control and verboseness, will serve as the teaching vehicle for the first half of the course. Students will then extend their learning into closely related programming interfaces such as OpenCL, OpenACC, and C++AMP.

The course is unique in that it is application oriented and only introduces the
necessary underlying computer science and computer engineering knowledge for
understanding. It covers the concept of data parallel execution models,
memory models for managing locality, tiling techniques for reducing bandwidth
consumption, parallel algorithm patterns, overlapping computation with
communication, and a variety of
heterogeneous parallel programming interfaces. The concepts learned in this
course form a strong foundation for learning other types of parallel
programming systems.

**Week One:**Introduction to Heterogeneous Computing, Overview of CUDA C, and Kernel-Based Parallel Programming, with lab tour and programming assignment of vector addition in CUDA C.-
**Week Two:**Memory Model for Locality, Tiling for Conserving Memory Bandwidth, Handling Boundary Conditions, and Performance Considerations, with programming assignment of simple matrix-matrix multiplication in CUDA C. **Week Three:**Parallel Convolution Pattern, with programming assignment of tiled matrix-matrix multiplication in CUDA C.**Week Four:**Parallel Scan Pattern, with programming assignment of parallel convolution in CUDA C**.****Week Five:**Parallel Histogram Pattern and Atomic Operations, with programming assignment of parallel scan in CUDA C.**Week Six:**Data Transfer and Task Parallelism, with programming assignment of parallel histogram in CUDA C.**Week Seven:**Introduction to OpenCL, Introduction to C++AMP, Introduction to OpenACC, with programming assignment of vector addition using streams in CUDA C.**Week Eight:**Course Summary, Other Related Programming Models –Thrust, Bolt, and CUDA FORTRAN, with programming assignment of simple matrix-matrix multiplication in choice of OpenCL, C++AMP, or OpenACC**.****Week Nine:**complete any remaining lab assignments, with optional, bonus programming assignments in choice of OpenCL, C++AMP, or OpenACC.

Programming experience in C/C++.

Although the class is designed to be self-contained, students wanting to expand their knowledge beyond what we can cover in a one-quarter class can find a much more extensive coverage of this topic in the book Programming Massively Parallel Processors: A Hands-on Approach (Applications of GPU Computing Series) - 2nd Edition, by David Kirk and Wen-mei Hwu, published by Morgan Kaufmann (Elsevier), ISBN 0123814723.

The class will consist of weekly lecture videos, which are between 15 and 20 minutes in length. There will also be weekly quizzes and programming assignments.

**What resources will I need for this class?**A laptop or desktop computer. GPU enabled hardware can be helpful but will not be required.

**Why not teach the whole course using OpenCL?**While OpenCL is an industry standard and widely supported by many CPU and GPU vendors, it is much more complex and tedious to use than CUDA. The complexity and tedious details distract from the concepts and techniques that one should master. From our experience, it is much more productive to use CUDA to teach the concepts and techniques. We will then teach the additional complexities of OpenCL so that students can comfortably apply all the concepts to OpenCL.

**How did the students do in the previous offering of the HPP Course?**Out of the 9,908 students who did quizzes and programming assignments,.2,811 received Certificate of Achievement or Certificate of Distinction.

**What is the coolest thing I'll learn if I take this class?**You will learn how to unleash the massive computing power from mobile processors to supercomputers for your applications.