Building High-Performance Systems with Julia: Numerical Computing, Multiple Dispatch, Multithreading, Distributed Computing, and GPU Programming
Language: English
Published by Independently published, 2026
- Softcover
- New
Seller: California Books, Miami, FL, U.S.A.California Books
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- Title
- Building High-Performance Systems with Julia: Numerical Computing, Multiple Dispatch, Multithreading, Distributed Computing, and GPU Programming
- Author
- Whitlock, Alden
- Publisher
- Independently published
- Publication year
- 2026
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 13
- 9798194772575
Most programming books teach you syntax and leave you to figure out architecture on your own. Building High-Performance Systems with Julia does something different: it builds one real project across all fourteen chapters — Helios, a gravitational N-body simulator — and grows it from a four-line command-line calculator into a production system that automatically detects its own hardware and picks between single-threaded, multithreaded, distributed, and GPU execution.
You will not find disconnected toy examples here. Every function you write in Chapter 1 is still running, unmodified, inside the production platform in Chapter 14.
Inside, you'll learn to:
- Write real, idiomatic Julia — types, dispatch, structs, and modules — from a true beginner starting point, with every technical term defined once and never re-explained
- Master multiple dispatch, Julia's single most distinctive feature, and use it to build physics that extends itself without a single scattered if statement
- Diagnose and fix real performance problems with @code_warntype, @btime, and Julia's built-in profiler — not guesswork, measured evidence
- Vectorize numerical code with broadcasting and BLAS, and know exactly when vectorization is the wrong tradeoff
- Build genuinely concurrent and multithreaded programs with tasks, channels, Threads.@threads, atomics, and locks — and understand why more threads don't always mean more speed
- Scale a real workload across multiple machines with Julia's Distributed standard library, complete with fault handling for a worker that disappears mid-run
- Write and tune custom CUDA.jl kernels, including GPU shared memory tiling, and build a hybrid pipeline where your CPU and GPU work simultaneously instead of one waiting on the other
- Ship it: benchmark regression testing, PackageCompiler system images, and a runtime that adapts to whatever hardware it actually finds itself on
Structured in five parts — Foundations, Core Mechanics, Concurrency and Parallelism, Distributed and GPU Computing, and Production — with a full glossary, a technical appendix, and every code example verified to run exactly as printed.
If you're ready to stop choosing between readable and fast, start here.
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