The course High Performance Computing is an online class provided by Udacity. The skill level of the course is Advanced. It may be possible to receive a verified certification or use the course to prepare for a degree.
The goal of this course is to give you solid foundations for developing, analyzing, and implementing parallel and locality-efficient algorithms. This course focuses on theoretical underpinnings. To give a practical feeling for how algorithms map to and behave on real systems, we will supplement algorithmic theory with hands-on exercises on modern HPC systems, such as Cilk Plus or OpenMP on shared memory nodes, CUDA for graphics co-processors (GPUs), and MPI and PGAS models for distributed memory systems.
High Performance Computing
Offered at Georgia Tech as CS 6220
This course is a graduate-level introduction to scalable parallel algorithms. “Scale” really refers to two things: efficient as the problem size grows, and efficient as the system size (measured in numbers of cores or compute nodes) grows. To really scale your algorithm in both of these senses, you need to be smart about reducing asymptotic complexity the way you’ve done for sequential algorithms since CS 101; but you also need to think about reducing communication and data movement. This course is about the basic algorithmic techniques you’ll need to do so.
The techniques you’ll encounter covers the main algorithm design and analysis ideas for three major classes of machines: for multicore and many core shared memory machines, via the work-span model; for distributed memory machines like clusters and supercomputers, via network models; and for sequential or parallel machines with deep memory hierarchies (e.g., caches). You will see these techniques applied to fundamental problems, like sorting, search on trees and graphs, and linear algebra, among others. The practical aspect of this course is implementing the algorithms and techniques you’ll learn to run on real parallel and distributed systems, so you can check whether what appears to work well in theory also translates into practice. (Programming models you’ll use include Cilk Plus, OpenMP, and MPI, and possibly others.)
Work-Span or Dynamic Multithreading Model
Distributed Memory or Network Models
Two-Level Memory or I/O Models
Intro to the basic algorithmic model
Intro to OpenMP, a practical programming model
Comparison-based sorting algorithms
Scans and linked list algorithms
Graph algorithms, e.g., breadth-first search
The basic algorithmic model
Intro to the Message Passing Interface, a practical programming model
Reasoning about the effects of network topology
Dense linear algebra
Sparse graph algorithms
Efficiency metrics, including “emerging” metrics like energy and power
A “second course” in algorithms and data structures, a la Georgia Tech’sCS 3510-B or Udacity’s Intro to Algorithms
For the programming assignments, programming experience in a “low-level” “high-level” language like C or C++
Experience using command line interfaces in *nix environments (e.g.,Unix, Linux)
Course readiness survey. You should feel comfortable answering questions like those found in the Readiness Survey Course, HPC-0
See the Technology Requirements for using Udacity.
To obtain a verified certificate from Udacity you have to finish this course or the latest version of it, if there is a new edition. The class may be free of charge, but there could be some cost to receive a verified certificate or to access the learning materials. The specifics of the course may have been changed, please consult the provider to get the latest quotes and news.
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