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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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What's inside

Four courses

Introduction to Concurrent Programming with GPUs

(0 hours)

This course introduces concurrent programming with GPUs. It covers CPU/GPU architectures, multithreaded programming in C and Python, and CUDA software/hardware.

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Introduction to Parallel Programming with CUDA

(0 hours)

This course prepares students to develop code that processes large amounts of data in parallel on Graphics Processing Units (GPUs). Students will learn to implement software that solves complex problems using Nvidia CUDA on consumer to enterprise-grade GPUs. The focus is on hardware and software capabilities, including the use of 100s to 1000s of threads and various forms of memory.

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CUDA at Scale for the Enterprise

(0 hours)

This course will aid students in learning 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.

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CUDA Advanced Libraries

(0 hours)

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.

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Learning objectives

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

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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Time

1 hours

Level

Intermediate

Via

Coursera

Institution

Johns Hopkins University

Instructor

Chancellor Thomas Pascale

Level

Intermediate

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