Search results
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.
- Log In
Students will develop software that uses CUDA to create...
- Log In
7 maj 2017 · I also noticed that the GPU fan is running at max speed because its very noisy. I turned it off, gave it some time to break, when I turned it on again, it remains the same (fans are on, gpu...
Learn GPU Programming, earn certificates with paid and free online courses from Johns Hopkins, University of New Mexico and other top universities around the world. Read reviews to decide if a class is right for you.
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.
Using the CUDA Toolkit you can accelerate your C or C++ applications by updating the computationally intensive portions of your code to run on GPUs. To accelerate your applications, you can call functions from drop-in libraries as well as develop custom applications using languages including C, C++, Fortran and Python.
Parallel algorithms running on GPUs can often achieve up to 100x speedup over similar CPU algorithms, with many existing applications for physics simulations, signal processing, financial modeling, neural networks, and countless other fields. This course covers programming techniques for the GPU.
Explore C Programming courses that cover skills in low-level programming, memory management, and algorithm design. Prepare for careers in software development, embedded systems, and systems programming.
