Geology 105 (F2F)
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: Parker Bywater
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 31||Introduction||IPP Ch. 1|
|Sep 2||Computing architecture and memory||IPP Ch. 2|
|Sep 7||Parallel hardware and interconnects||IPP Ch. 2|
|Sep 9||Parallel software and scalability||IPP Ch. 2||HW1|
|Sep 14||Shared memory programming: PThreads||IPP Ch. 4|
|Sep 16||Shared memory programming: PThreads (2)||IPP Ch. 4|
|Sep 21||Shared memory programming: PThreads (3)||IPP Ch. 4|
|Sep 23||C++11 Threads and running on an HPC system||CHPC||HW2|
|Sep 28||OpenMP (1)||IPP Ch. 5|
|Sep 30||OpenMP (2)||IPP Ch. 5|
|Oct 5||Distributed programming: basics||IPP Ch. 3|
|Oct 7||Distributed programming: MPI||IPP Ch. 3|
|Oct 12||Mid-term exam||Mid-term|
|Oct 14||Reviews, more MPI (2)||HW3|
|Oct 19||Applications (1)|
|Oct 21||Applications (2)|
|Oct 26||Projects, Parallel I/O||HW4|
|Oct 28||Introduction to GPU Programming and CUDA||GPU Ch.1,2,3|
|Nov 2||GPU: execution model||GPU Ch.4|
|Nov 4||GPU: memory model||GPU Ch.5|
|Nov 9||GPU: tiling and matrix multiplication||GPU Ch.6,7||HW5|
|Nov 11||GPU: convolution||GPU Ch.8|
|Nov 16||GPU: convolution (2)||GPU Ch.9|
|Nov 18||GPU: convolution analysis||GPU Ch.9|
|Nov 23||GPU: deep learning|
|Nov 30||Python for HPC|
|Dec 2||Python for HPC (2)|
|Dec 7||Parallel programming languages|
|Dec 9||Review and Project presentations|
|Dec 14||No class.|
|Dec 16||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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