Discover the potential of GPU programming with this specialization, designed for data scientists and software developers. Dive into CUDA and libraries that enable parallel computations for machine learning, image/audio signal processing, and data processing.
Throughout the course, you will develop mastery in high-performance computing, learning to create software that runs massive computations on commonly available hardware. Gain expertise in utilizing libraries to bring well-known algorithms to software without the need to redevelop existing capabilities.
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Gain expertise in GPU programming through four comprehensive modules. Learn concurrent and parallel programming with GPUs, CUDA at scale for enterprise applications, and advanced libraries for high-level mathematics operations and machine learning.
Students will learn how to develop concurrent software in Python and C/C++ programming languages. Gain an introductory level of understanding of GPU hardware and software architectures.
Utilize the CUDA framework to write C/C++ software that runs on CPUs and Nvidia GPUs. Transform sequential CPU algorithms and programs into CUDA kernels that execute 100s to 1000s of times simultaneously on GPU hardware.
Develop software that can be run in computational environments with multiple CPUs and GPUs. Create interactive GPU computational processing kernels for handling asynchronous data, and solve programming challenges including image processing using CUDA, hardware memory capabilities, and algorithms/libraries.
Learn to perform high-level mathematics operations using libraries such as cuFFT and cuBLAS. Utilize the Thrust library for data manipulation and structures that abstract away memory management. Develop machine learning software for various purposes using neural networks modeled using libraries such as cuTensor and cuDNN.
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