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.
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Spécialisation GPU ProgrammingUniversité Johns-HopkinsÀ propos de ce cours
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Dates limites flexibles
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Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 1 sur 4 dans le
Niveau débutant
Some experience programming
Approx. 20 heures pour terminer
Anglais
Sous-titres : Anglais
Ce que vous allez apprendre
Students will learn how to develop concurrent software in Python and C/C++ programming languages.
Students will gain an introductory level of understanding of GPU hardware and software architectures.
Compétences que vous acquerrez
- Cuda
- Python Programming
- Thread (Computing)
- C++
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 1 sur 4 dans le
Niveau débutant
Some experience programming
Approx. 20 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
Université 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.
Programme de cours : ce que vous apprendrez dans ce cours
3 heures pour terminer
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 heures pour terminer
3 vidéos (Total 12 min), 4 lectures, 1 quiz
3 heures pour terminer
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 heures pour terminer
6 vidéos (Total 35 min), 3 lectures, 2 quiz
6 heures pour terminer
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 heures pour terminer
6 vidéos (Total 31 min), 2 lectures, 4 quiz
3 heures pour terminer
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 heures pour terminer
9 vidéos (Total 36 min), 1 lecture, 3 quiz
À propos du Spécialisation 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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