In the era of machine learning and artificial intelligence, GPU acceleration has become indispensable for handling computationally intensive tasks efficiently. This talk explores how to integrate CUDA - NVIDIA's powerful parallel computing platform - with Go, to leverage GPU power in your applications. We'll start with an introduction to GPU computing and the CUDA programming model, explaining why and when to use GPU acceleration, particularly in the context of AI and ML. Finally, we'll guide you through setting up your development environment to work with Go and CUDA.

Using Go's cgo, we will demonstrate:

How to call CUDA functions from Go;
Managing memory between the CPU and GPU;
Debugging and performance monitoring.
We will also cover common patterns that will help your applications run smoothly. Through practical examples, you will learn how to implement and optimize GPU-accelerated computations in Go. By the end of this talk, you'll be equipped to start incorporating GPU acceleration into your Go projects, achieving significant performance improvements for compute-intensive tasks, including those in machine learning and artificial intelligence.

GopherCon (Gopher Academy)

A GopherCon 2025 talk covering how to integrate CUDA GPU programming with Go using CGO. The speaker walks through GPU architecture fundamentals, CUDA's thread/block/grid model, memory hierarchy (registers, shared, global), and practical code examples including array increment and matrix multiplication kernels. The integration pattern uses CGO annotations to link compiled CUDA (.cu) shared libraries into Go programs, with careful attention to memory safety across the Go garbage collector and C manual memory management boundary. Performance profiling with NVIDIA Nsight Systems and Nsight Compute is also covered, including how to identify memory transfer bottlenecks and compute-bound kernels.

GopherCon 2025: Go Faster: Integrating CUDA in Go for GPU Acceleration - Sam Burns