[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvODE4MDc","title","GPU Programming using Rust and CUDA","state","published","contactAuthorMethod","email","aboutText"," C++ has been the go-to for GPU programming for almost 20 years. Can Rust do the job, and how well?

This book is all about getting hands-on with different toolchains that connect Rust to NVIDIA hardware. There's RustaCUDA for safe host-side control, the Rust-CUDA project for writing kernels in pure Rust, and NVIDIA's experimental cuda-oxide compiler with its typed launches and async execution graphs.

We're going to build one Cargo workspace that keeps on growing. It'll include device queries, launch planning, Rust-written kernels, memory optimization, parallel reductions and scans, multi-stream pipelines, matrix multiplication benchmarked against cuBLAS, a Monte Carlo option pricer validated against a closed formula, and a complete batched inference application measured against a Python baseline. We'll check every result against a CPU reference, and the reports will give accurate numbers, including where libraries outperform hand-written kernels and where experimental toolchains are still a work in progress.

Key Learnings

Launch, synchronize, and verify GPU kernels with ownership-managed device memory.
Write real CUDA kernels using Rust-CUDA and cuda-oxide.
Plan grids, blocks, and warps for 2D workloads.
Accelerate transfer speeds with pinned memory and coalesced access patterns.
Build race-free thread cooperation using shared memory, barriers, and atomics.
Overlap transfers with computation using streams, events, and async Rust pipelines.
Optimize matrix multiplication and benchmark against cuBLAS ceiling.
Wrap CUDA C library safely with handles, error enums, and Drop.
Ship complete batched GPU inference application against Python baselines.
Diagnose performance with Nsight Systems, Nsight Compute, and compute-sanitizer.

Table of Content

New Beneficiary of GPU Computing
Thinking in Threads
Commanding GPU
Writing GPU Kernels
Cleaner Kernels with cuda-oxide
Mastering GPU Memory
Making Threads Cooperate
Keeping GPU Busy
Delivering Real Math
Borrowing NVIDIA's Muscle
Shipping Complete GPU Application
Proving Performance

","authors",[24],{"_12":25,"_26":27,"_28":29},"Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6UHJvZHVjdEF1dGhvci84NzQyMQ","name","GitforGits | Asian Publishing House","user",{"_30":31},"avatar",{"_32":33,"_34":35,"_36":37},"url","https://d39qdlcrvnra4b.cloudfront.net/avatars/2867706/medium/gitforgits_shoplogo.jpg?1753097646","styleName","medium","exists",true,"shoppingCartId","shoppingCart",{"_12":71,"_72":73,"_74":37,"_75":76,"_77":78,"_79":80,"_81":37},"locale","en","readerTheme","light","constants",{"_69":70},"publishers",{"_65":66,"_67":68},"learnerOrganizations",[],"needsLocaleCookieSync",false,"announcements",{"_59":60,"_61":-5},"sentryEnvironment","production","sentryRelease","06fff6bf2a3f19ff2ef76f94f389bccdbec7f9bd","announcement_store",{"_62":52,"_63":64},"announcement_authors","flag","text","

Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

\n","companies",[],"universities",[],"paypalClientId","AX53Cwz8jR6rVVzzZiZNWWXl0dGagi-GZrjG-S3Ua14E5nrKMn7DxDQ730O4R8LusLPp49ALn6aRVaEy","no-cart","count",0,"free","updatedAt","","paymentGateway","STRIPE","cartItems",[],"loading"]

Email the Author