This course will aid in students in learning in concepts that scale the use of GPUs and the CPUs that manage their use beyond the most common consumer-grade GPU installations. They will learn how to manage asynchronous workflows, sending and receiving events to encapsulate data transfers and control signals. Also, students will walk through application of GPUs to sorting of data and processing images, implementing their own software using these techniques and libraries.
oferecido por
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
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 3 de 4 no
NÃvel intermediário
S​ome experience in C/C++ programming
Aprox. 29 horas para completar
Inglês
Legendas: Inglês
O que você vai aprender
S​tudents will learn to develop software that can be run in computational environments that include multiple CPUs and GPUs.
S​tudents will develop software that uses CUDA to create interactive GPU computational processing kernels for handling asynchronous data.
Students will use CUDA, hardware memory capabilities, and algorithms/libraries to solve programming challenges including image processing.
Habilidades que você terá
- Cuda
- Algorithms
- C/C++
- GPU
- Nvidia
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 3 de 4 no
NÃvel intermediário
S​ome experience in C/C++ programming
Aprox. 29 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
4 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.
4 horas para concluir
3 vÃdeos (Total 11 mÃn.), 1 leitura, 1 teste
10 horas para concluir
Multiple CPU/GPU Systems
In professional settings, use of one CPU managing one GPU, is not a viable configuration to solve complex challenges. Students will apply CUDA capabilities for allowing multiple CPUs to communicate and manage software kernels on multiple GPUs. This will allow for scaling the size of input data and computational complexity. Students will learn the advantages and limitations of this form of synchronous processing.
10 horas para concluir
7 vÃdeos (Total 32 mÃn.)
4 horas para concluir
CUDA Events and Streams
Students will learn to utilize CUDA events and streams in their programs, to allow for asynchronous data and control flows. This will allow more interactive and long-lasting software, including analytic user interfaces, near live-streaming video or financial feeds, and dynamic business processing systems.
4 horas para concluir
5 vÃdeos (Total 28 mÃn.), 2 leituras, 1 teste
5 horas para concluir
Sorting Using GPUs
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.
5 horas para concluir
11 vÃdeos (Total 57 mÃn.), 1 leitura, 1 teste
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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Can I program on my own desktop/laptop
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