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GPU Programming using Rust and CUDA: Exploring Rust's potential in GPU and parallel computing using Rust-CUDA, cuda-oxide, and RustaCUDA
Paperback
Publisher Price: $79.99
ISBN13: 9789349174375
Publisher: Gitforgits
Published: Jul 25 2026
Pages: 166
Weight: 0.65
Height: 0.35 Width: 7.50 Depth: 9.25
Language: English
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 LearningsLaunch, 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 ContentNew 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
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