Tue-Thu 1-2PM (Zoom/Old Main 424)
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: Shubham Gupta
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:
|Aug 29||Introduction||IPP Ch. 1|
|Aug 31||Computing architecture and memory||IPP Ch. 2|
|Sep 5||Parallel hardware and interconnects||IPP Ch. 2|
|Sep 7||Labor Day|
|Sep 12||Parallel software and scalability||IPP Ch. 2||HW1|
|Sep 14||Parallel software and scalability (2)||IPP Ch. 4|
|Sep 19||Shared memory programming: PThreads||IPP Ch. 4|
|Sep 21||Shared memory programming: PThreads (2)||IPP Ch. 4|
|Sep 26||C++11 Threads and running on an HPC system||CHPC||HW2|
|Sep 28||OpenMP (1)||IPP Ch. 5|
|Oct 3||OpenMP (2)||IPP Ch. 5|
|Oct 5||OpenMP (3)||IPP Ch. 5|
|Oct 10||Introduction to GPU Programming and CUDA||GPU Ch.1,2,3||HW3|
|Oct 12||Review and GPU programming (2)||GPU Ch.4|
|Oct 17||Mid-term exam||Mid-term|
|Oct 19||GPU: execution model||GPU Ch. 4|
|Oct 24||GPU: memory model||GPU Ch.5|
|Oct 26||GPU: tiling and matrix multiplication||GPU Ch.6,7|
|Oct 31||GPU: convolution||GPU Ch.8||HW4|
|Nov 2||GPU: convolution analysis||GPU Ch.9|
|Nov 7||GPU: deep learning|
|Nov 14||Distributed programming: basics||IPP Ch. 3||HW5|
|Nov 16||Distributed programming: MPI||IPP Ch. 3|
|Nov 21||MPI (2)||IPP Ch. 3|
|Nov 23||MPI Parallel I/O (3)||IPP Ch. 3|
|Nov 28||Parallel I/O, Applications|
|Nov 30||Python for HPC|
|Dec 7||Review and Project presentations|
|Dec 12||No class.|
|Dec 14||Final exam||Final|
The date in the “Homework” column represents when the homework is assigned. Homework are generally due after two weeks. The schedule above is subject to change.
Selection/proposal deadline: TBD, 2022
Submission deadline: TBD, 2022
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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