This course will help prepare students for developing code that can process large amounts of data in parallel. It will focus on foundational aspects of concurrent programming, such as CPU/GPU architectures, multithreaded programming in C and Python, and an introduction to CUDA software/hardware.
Este curso faz parte do Programa de cursos integrados GPU Programming
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Programa de cursos integrados GPU ProgrammingUniversidade Johns HopkinsInformações sobre o curso
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Prazos flexÃveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
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Curso 1 de 4 no
NÃvel iniciante
S​ome experience programming
Aprox. 20 horas para completar
Inglês
Legendas: Inglês
O que você vai aprender
S​tudents will learn how to develop concurrent software in Python and C/C++ programming languages.
S​tudents will gain an introductory level of understanding of GPU hardware and software architectures.
Habilidades que você terá
- Cuda
- Python Programming
- Thread (Computing)
- C++
Prazos flexÃveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 1 de 4 no
NÃvel iniciante
S​ome experience programming
Aprox. 20 horas para completar
Inglês
Legendas: Inglês
oferecido por
Universidade Johns Hopkins
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Programa - O que você aprenderá com este curso
3 horas para concluir
Course Overview
The purpose of this module is for students to understand how the course will be run, topics, how they will be assessed, and expectations.
3 horas para concluir
3 vÃdeos (Total 12 mÃn.), 4 leituras, 1 teste
3 horas para concluir
Core Principles of Parallel Programming on CPUs and GPUs
In order to create software that process greater amounts of data at faster speeds, software operating systems, programming languages, and frameworks require strategies for accessing and modification of data in a manner that maximizes speed, while minimizing the possibility of data being in incorrect states. In this module, students will be presented canonical concurrency problems such as the Dining Philosophers. Additionally, they will learn how operating systems and programming languages handle these problems, and discuss real world big data concurrency applications.
3 horas para concluir
6 vÃdeos (Total 35 mÃn.), 3 leituras, 2 testes
6 horas para concluir
Introduction to Parallel Programming with C and Python
Modern programming languages allow developers to create software with complex logic for manipulation of data in parallel, taking advantage of the multiple CPU cores in most computers. Students will develop simple software, written in the C++ and Python 3 programming languages, that process data sets concurrently.
6 horas para concluir
6 vÃdeos (Total 31 mÃn.), 2 leituras, 4 testes
3 horas para concluir
NVidia GPU Hardware/Software
The purpose of this module is for students to understand the basis in hardware and software that CUDA uses. This is required to appropriately develop software to optimally take advantage of GPU resources.
3 horas para concluir
9 vÃdeos (Total 36 mÃn.), 1 leitura, 3 testes
Sobre Programa de cursos integrados GPU Programming
This specialization is intended for data scientists and software developers to create software that uses commonly available hardware. Students will be introduced to CUDA and libraries that allow for performing numerous computations in parallel and rapidly. Applications for these skills are machine learning, image/audio signal processing, and data processing.
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