Getting Started With CUDA C on an Nvidia Jetson: Hello CUDA World!
In this blog post, I introduce CUDA, which is a framework designed to allow developers to take advantage of Nvidia's GPU hardware acceleration to efficiently implement certain type of applications. I demonstrate an implementation to perform vector addition using CUDA C and compare it against the traditional implementation in "regular" C.
Summary
This blog introduces CUDA and demonstrates how to use CUDA C on an Nvidia Jetson to accelerate a simple vector-add application. It walks through the code, build steps, and a basic CPU vs GPU performance comparison to show the benefits of GPU offload on an Embedded Linux edge device.
Key Takeaways
- Explain the CUDA programming model and how kernels, threads, and blocks map to GPU execution.
- Implement and compile a vector-add example in CUDA C on an Nvidia Jetson using the nvcc toolchain.
- Measure and compare execution time between the CPU (regular C) and GPU (CUDA) implementations.
- Set up the Jetson development environment and run basic profiling/verification steps to validate GPU acceleration.
Who Should Read This
Embedded/linux developers and IoT/edge engineers with basic C and Linux experience who want to learn how to leverage Nvidia Jetson GPUs using CUDA for faster numeric workloads.
Still RelevantIntermediate
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