Discover how to write CUDA C++ applications that efficiently and correctly utilize all available GPUs in a single node, dramatically improving the performance of applications and making the most cost-effective use of systems with multiple GPUs.
Explore how to use Numba the just-in-time, type-specializing Python function compiler to accelerate Python programs to run on massively parallel NVIDIA GPUs.
Learn how to accelerate and optimize existing C/C++ CPU-only applications to leverage the\npower of GPUs using the most essential CUDA techniques and the Nsight Systems profiler. You’ll learn how to write code, configure code parallelization with\nCUDA, optimize memory migration between the CPU and GPU accelerator, and implement the workflow that\nyou’ve learned on a new task—accelerating a fully functional, but CPU-only, particle simulator for observable\nmassive performance gains
Find out how to write and configure code parallelization with OpenACC, optimize memory movements between the CPU and GPU accelerator, and apply the techniques to accelerate a CPU-only Laplace Heat Equation to achieve performance gains.
There was a problem reporting this post.
Please confirm you want to block this member.
You will no longer be able to:
Please note: This action will also remove this member from your connections and send a report to the site admin. Please allow a few minutes for this process to complete.