This course will help prepare students for developing code that can process large amounts of data in parallel on Graphics Processing Units (GPUs). It will learn on how to implement software that can solve complex problems with the leading consumer to enterprise-grade GPUs available using Nvidia CUDA. They will focus on the hardware and software capabilities, including the use of 100s to 1000s of threads and various forms of memory.
von
Spezialisierung GPU ProgrammingJohns Hopkins UniversityÜber diesen Kurs
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 2 von 4 in
Stufe „Mittel“
Some experience in C/C++ programming
Ca. 22 Stunden zum Abschließen
Englisch
Untertitel: Englisch
Was Sie lernen werden
Students will learn how to utilize the CUDA framework to write C/C++ software that runs on CPUs and Nvidia GPUs.
Students will transform sequential CPU algorithms and programs into CUDA kernels that execute 100s to 1000s of times simultaneously on GPU hardware.
Kompetenzen, die Sie erwerben
- Cuda
- Algorithms
- C/C++
- GPU
- Nvidia
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 2 von 4 in
Stufe „Mittel“
Some experience in C/C++ programming
Ca. 22 Stunden zum Abschließen
Englisch
Untertitel: Englisch
von
Johns Hopkins University
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.
Lehrplan - Was Sie in diesem Kurs lernen werden
4 Stunden zum Abschließen
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 Stunden zum Abschließen
3 Videos (Gesamt 11 min), 4 Lektüren, 1 Quiz
5 Stunden zum Abschließen
Threads, Blocks and Grids
The single most important concept for using GPUs to solve complex and large-scale problems, is management of threads. CUDA provides two- and three-dimensional logical abstractions of threads, blocks and grids. Students will develop programs that utilize threads, blocks, and grids to process large 2 to 3-dimensional data sets.
5 Stunden zum Abschließen
8 Videos (Gesamt 50 min), 1 Lektüre, 4 Quiz
5 Stunden zum Abschließen
Host and Global Memory
To manage the access and modification of data in physical memory effectively, students will need to load data into CPU (host) and GPU (global) general-purpose memory. Students will create software that allocates host memory and transfers it into global memory for use by threads. Students will also learn the capabilities and speeds of these types of memories.
5 Stunden zum Abschließen
8 Videos (Gesamt 23 min)
4 Stunden zum Abschließen
Shared and Constant Memory
To improve performance in GPU software, students will need to utilized mutable (shared) and static (constant) memory. They will use them to apply masks to all items of a data set, to manage the communication between threads, and use for caching in complex programs.
4 Stunden zum Abschließen
6 Videos (Gesamt 22 min)
Über den Spezialisierung 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.
Häufig gestellte Fragen
Wann erhalte ich Zugang zu den Vorträgen und Aufgaben?
Was bekomme ich, wenn ich diese Spezialisierung abonniere?
Ist finanzielle Unterstützung möglich?
Can I program on my own desktop/laptop?
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