This course will complete the GPU specialization, focusing on the leading libraries distributed as part of the CUDA Toolkit. Students will learn how to use CuFFT, and linear algebra libraries to perform complex mathematical computations. The Thrust library’s capabilities in representing common data structures and associated algorithms will be introduced. Using cuDNN and cuTensor they will be able to develop machine learning applications that help with object detection, human language translation and image classification.
CUDA Advanced Libraries
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CUDA Advanced Libraries
This course is part of GPU Programming Specialization
Instructor: Chancellor Thomas Pascale
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3.2
(10 reviews)
Intermediate level
Recommended experience
25 hours to complete
3 weeks at 8 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
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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Taught in English
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This course is part of the GPU Programming Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 5 modules in this course
The purpose of this module is for students to understand how the course will be run, topics, how they will be assessed, and expectations.
What's included
4 videos1 programming assignment1 discussion prompt1 ungraded lab
cuFFT provides the ability to perform fast Fourier transforms (FFTs) on large datasets. Students will learn of common use cases such as fast multiplication of large polynomials, signal processing, and matrix operations. They will use this library to develop software that process audio or video signals.
What's included
6 videos1 reading1 programming assignment1 discussion prompt1 ungraded lab
The CUDA Toolkit includes a number of linear algebra libraries, such as cuBLAS, NVBLAS, cuSPARSE, and cuSOLVER. Students will learn the different capabilities and limitations of many of them and apply that knowledge to compute matrix dot products, determinant, and finding solutions to complex linear systems.
What's included
6 videos1 reading1 programming assignment1 discussion prompt1 ungraded lab
Most developers utilize data structures beyond the primitives and pointers that make up the core of CUDA programmers, which makes pure CUDA development difficult. Students will learn about the Thrust library that adds the vector data structure and associated algorithms that allow for simplification of their code. Students will create software that transform, reduction, and sort large datasets.
What's included
7 videos1 reading1 programming assignment1 ungraded lab
Data Scientists, Machine Learning, and Artificial intelligence experts are using neural networks to solve problems such as human language translation, image classification, and object detection/avoidance. Using the cuDNN and cuTensor, students will be able to develop a variety of neural networks and similar structures. At the completion of this module students will be asked to develop a course-wide project that brings together their knowledge from all previous courses and lessons to develop a capstone software project.
What's included
6 videos1 reading1 peer review1 discussion prompt2 ungraded labs
Instructor
Offered by
Recommended if you're interested in Algorithms
University of California, Irvine
Coursera Project Network
Università di Napoli Federico II
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