RWST 306 or Web Broadcast
Wed 4.30-5.30PM, Fri 2.00-3.00PM on Zoom (Canvas)
This course is an introduction to high-performance computing. High-performance computing (HPC) is the practice of leveraging parallel computing power, from computer clusters and supercomputers, to solve complex computational problems faster. In this course students will learn the main programming models, such as openMP and MPI, to write highly optimized code for modern multi-core processors, clusters, and modern computing architecture (e.g., GPUs). Furthermore, this course will explore the concepts and challenges of performance scalability, portability, and energy efficiency of HPC applications and systems.
Lecturer: Dr. Steve Petruzza TA: Kohl Kennedy
Topics include: multithread programming, openMP, distributed programming, MPI, performance profiling, parallel and distributed algorithms, parallel I/O, in situ analytics, GPU programming.
Prerequisites: a good C/C++ programming background. Most of the assignment will be in C/C++. In particular, a student needs to have knowledge of data structures, arrays, pointers and memory allocation in C/C++. If you are not too familiar with C/C++ here is a tutorial that could be useful, the “C++ basic” section is a good place to start. If you are not familiar with the Unix terminal:
|Sep 1||Introduction||IPP Ch. 1|
|Sep 3||Computing architecture and memory||IPP Ch. 2|
|Sep 8||Parallel hardware and interconnects||IPP Ch. 2|
|Sep 10||Parallel software and scalability||IPP Ch. 2||HW1|
|Sep 15||Shared memory programming: PThreads||IPP Ch. 4|
|Sep 17||Shared memory programming: PThreads (2)||IPP Ch. 4|
|Sep 22||Shared memory programming: PThreads (3)||IPP Ch. 4|
|Sep 24||C++11 Threads and running on an HPC system||CHPC||HW2|
|Sep 29||OpenMP (1)||IPP Ch. 5|
|Oct 1||OpenMP (2)||IPP Ch. 5|
|Oct 6||Distributed programming: basics||IPP Ch. 3|
|Oct 8||Distributed programming: MPI||IPP Ch. 3|
|Oct 13||Mid-term exam||Mid-term|
|Oct 15||Reviews, more MPI (2)||HW3|
|Oct 20||Applications (1)|
|Oct 22||Applications (2)|
|Oct 27||Projects, Parallel I/O||HW4|
|Oct 29||Introduction to GPU Programming and CUDA||GPU Ch.1,2,3|
|Nov 3||GPU: execution model||GPU Ch.4|
|Nov 5||GPU: memory model||GPU Ch.5|
|Nov 10||GPU: tiling and matrix multiplication||GPU Ch.6,7||HW5|
|Nov 12||GPU: convolution||GPU Ch.8|
|Nov 17||GPU: convolution (2)||GPU Ch.9|
|Nov 19||GPU: convolution analysis||GPU Ch.9|
|Nov 24||GPU: deep learning|
|Dec 1||Python for HPC|
|Dec 3||Python for HPC (2)|
|Dec 8||Parallel programming languages|
|Dec 10||Review and Project presentations|
|Dec 15||No class.|
|Dec 17||Final exam||Final|
The schedule above is subject to change
Selection/proposal deadline: Nov 15, 2020
Submission deadline: Dec 6, 2020
A [93%, 100%] A- [87%, 93%) B+ [81%, 87%) B [75%, 81%) B- [70%, 75%) C+ [65%, 70%) C [60%, 65%) C- [58%, 60%) D+ [55%, 58%) D [53%, 55%) F Below 53%
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