Resumo do curso

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.)

Legenda
Inglês
Tempo estimadoTempo total entre hoje e dia da formatura depende do seu compromisso semanal. Em média, os nossos graduados completam este nanodegree em 4 meses
4 meses
Nível
avançado
O curso inclui

Videoaulas

Testes interativos

Aulas com profissionais do setor

Ritmo individual de aprendizado

Comunidade de apoio aos alunos

Sua jornada de aprendizagem

Este curso é seu primeiro passo em direção a uma nova carreira com o programa Engenheiro de Machine Learning

Curso Aberto

High Performance Computing

porGeorgia Institute of Technology

Desenvolva habilidades que aumentarão suas chances de contratação adquirindo conhecimentos inovadores de forma independente.

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Pré-requisitos

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

Rich Vuduc

Rich Vuduc

Instrutor

Catherine Gamboa

Catherine Gamboa

Instrutora

Por que fazer este curso?

Quais são os recursos?
Vídeos dos instrutoresExercícios práticosAulas com profissionais do setor