Offered at Georgia Tech as CS 6220
Lege Grundlagen des Supervised, Unsupervised, Reinforcement und Deep Learning
Im Schnellverfahren zur Karriere, die dir vorschwebt.
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.
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.)
Rich Learning Content
Taught by Industry Pros
Student Support Community
Dieser kostenlose Kurs ist der erste Schritt auf dem Weg zu einer neuen Karriere mit dem Machine Learning Programm.
Erweitere deine Fähigkeiten und Karriere durch innovatives und unabhängiges Lernen.
A “second course” in algorithms and data structures, a la Georgia Tech’s CS 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
Detaillierte technische Voraussetzungen