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Category: "CUDA"

Books

  1. Building a Native LLM Inference Engine
    Building a Native LLM Inference Engine
    Krzysztof Rybiński

    From Raw Model Weights to High-Performance C++20 and CUDA ExecutionModern large language models are only as efficient as the systems that execute them. Behind every generated token lies a complex interaction between model architecture, memory bandwidth, numerical precision, parallel computation, kernel execution, and runtime orchestration. Understanding these interactions is essential for engineers who want to move beyond high-level inference frameworks and build systems whose performance, architecture, and execution behavior they can control directly.

  2. Programming Concurrency on GPUs with Rust
    Programming Concurrency on GPUs with Rust
    Examine, Run, and Measure GPU Workloads Using CUDA, Streams, and Async Pipelines
    GitforGits | Asian Publishing House

    We will work in pure Rust, with no C++ shim or Python wrapper. We will build one small program that grows to cover the whole book. It begins as a loop that any of us could have written on a bad day. After gaining a kernel, it learns to stop waiting, grows streams and pinned memory, and ends as a pipeline fed by parallel producers through bounded channels. The device is busy almost every millisecond.

  3. CUDA C++: High-Performance GPU Programming
    CUDA C++: High-Performance GPU Programming
    Krzysztof Rybiński

    Master the GPU from the Streaming Multiprocessor to Production-Grade High-Performance KernelsCUDA C++: High-Performance GPU Programming is an advanced technical guide to GPU programming, CUDA execution, memory architecture, kernel optimization, low-level hardware analysis, and high-throughput AI workloads.Designed for engineers who want to move beyond basic CUDA programming, this 53-chapter technical reference explores the NVIDIA GPU programming stack in depth, progressing from Streaming Multiprocessor architecture and SIMT execution to PTX/SASS analysis, Tensor Cores, asynchronous data movement, multi-GPU systems, NCCL, and production inference engines.The book takes a hardware-aware approach to performance engineering. Rather than treating CUDA as a black box, it examines how threads execute, how warps communicate, how memory transactions are formed, and how registers, caches, compiler decisions, and hardware scheduling influence performance. The emphasis is on understanding the mechanisms behind kernel behavior and using that knowledge to make informed optimization decisions.

  4. Writing GPU Kernels with CUDA Rust
    Writing GPU Kernels with CUDA Rust
    A Practical Guide to High-Performance GPU Computing from Rust
    Steve Publications

    Unlock NVIDIA GPU performance from Rust. This practical guide takes you from GPU architecture and memory hierarchies to writing and optimizing real CUDA Rust kernels. Explore both official Rust CUDA approaches with runnable examples and learn how to build fast, production-ready GPU code without giving up Rust’s safety and clarity.