GPU Programming using Rust and CUDA

Exploring Rust's potential in GPU and parallel computing using Rust-CUDA, cuda-oxide, and RustaCUDA

Produkt bewerten

Maris Fenlor

Verlag:

GitforGits

Unsere-Artikel-Nr.: P36540898

EAN: 9789349174375

Erhältlich:

Nicht auf Lager

Zustellung: Mo, 14.09.2026

Versand: Kostenlos

-12.6 %

CHF 119.–

CHF 104.–

Beschreibung

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.

Spezifikationen

Sprache

Englisch

Autor

Maris Fenlor

Erscheinungsjahr

2026

Format

Buch (Softcover)

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