By Bryce Adelstein Lelbach
Getting the most out of your GPU with C++ doesn’t require writing custom kernels or manually managing storage for everything! Come learn about the libraries and techniques that make writing CUDA C++ code easier and more performant. Through examples, we’ll explore all aspects of writing modern C++ software for GPUs, including heterogeneous memory management, algorithm design, and synchronization.
During this talk, you’ll:
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Learn to evaluate when you should use a CUDA library versus writing your own kernel.
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Explore popular CUDA C++ libraries such as Thrust, CUB, and libcu++.
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Understand how you can easily compose different CUDA libraries and your own custom CUDA C++ code together.
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Build intuition about the performance implications of CUDA libraries.
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You’ll leave confident about how to select the best tool for the job to accelerate your C++ applications for your unique use cases.
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