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October 7, 2024
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Solve Challenges with Powerful GPUs. Develop mastery in high performance computing and apply to numerous fields.
Instructor: Chancellor Thomas Pascale
9,176 already enrolled
Included with
Recommended experience
Intermediate level
At least 1 year of computer programming experience, preferrably with the C/C++ programming language.
Recommended experience
Intermediate level
At least 1 year of computer programming experience, preferrably with the C/C++ programming language.
Develop CUDA software for running massive computations on commonly available hardware
Utilize libraries that bring well-known algorithms to software without need to redevelop existing capabilities
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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.
Applied Learning Project
Learners will complete at least 2 projects that allow them the freedom to explore CUDA-based solutions to image/signal processing, as well as a topic of choosing, which can come from their current or future professional career. They will also create short demonstrations of their efforts and share their code.
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.
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.
Students will learn to develop software that can be run in computational environments that include multiple CPUs and GPUs.
Students 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.
You will learn to develop software that performs high-level mathematics operations using libraries such as cuFFT and cuBLAS.
You will learn to use the Thrust library to perform a number of data manipulation and data structures that abstract away memory management.
You will learn to develop machine learning software for a variety of purposes using neural networks modeled using the cuTensor and cuDNN libraries.
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Each course in the specialization is aimed to be completed in 1 month. The full specialization should be completed in 4 months.
Prospective students should have a minimum of 1 year of programming experience. A high level of comfort in programming in C/C++ will aid in the absorbtion of material and completion of assignments.
Each course in the specialization should be completed in the following order:
Introduction to Concurrent Programming with GPUs
Introduction to Parallel Programming with CUDA
CUDA at Scale for the Enterprise
CUDA Advanced Libraries
No, this specialization is intended to earn a certificate of completion.
You will be able to develop complex software that can run on Nvidia GPUs. This will allow for solving complex challenges that would take longer on most CPUs or be less cost effective on clusters.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
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