Über diesen Kurs

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.)

Kursgebühren
Kostenlos
Niveau
Profis
Vorteile

Rich Learning Content

Interactive Quizzes

Taught by Industry Pros

Self-Paced Learning

Student Support Community

Begib' dich auf den Weg des Erfolgs

Dieser kostenlose Kurs ist der erste Schritt auf dem Weg zu einer neuen Karriere mit dem Machine Learning Engineer Programm.

Kostenlose Kurse

Hochleistungsrechnen

mit Georgia Institute of Technology

Erweitere deine Fähigkeiten und Karriere durch innovatives und unabhängiges Lernen.

Icon steps 54aa753742d05d598baf005f2bb1b5bb6339a7d544b84089a1eee6acd5a8543d
 
 

Tutoren

Rich Vuduc
Rich Vuduc

Tutor

Catherine Gamboa
Catherine Gamboa

Tutor

Voraussetzungen

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

Was spricht für diesen Kurs?

Was bekomme ich?
Instructor videos Learn by doing exercises Taught by industry professionals