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Elliot Arledge

CUDA for Deep Learning focuses on using CUDA directly to get more out of NVIDIA GPUs, beyond what you can squeeze out of framework-level tweaks. The book starts at the fundamentals—writing your first kernels—and works its way up to performance-critical building blocks used in modern models, including techniques behind things like Flash Attention.

What sets this book apart is the emphasis on why an optimization works, not just how to apply it. You’ll learn how to profile with Nsight Compute, spot memory and compute bottlenecks, and reason about performance across multiple layers of abstraction. The goal is to build an intuition for CUDA that holds up even as hardware evolves.

This isn’t about replacing PyTorch or TensorFlow. It’s for cases where you need lower-level control, want to understand GPU behavior deeply, or are working on custom kernels, research code, or performance-sensitive production systems.

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