Book Image

Hands-On Concurrency with Rust

By : Brian L. Troutwine
Book Image

Hands-On Concurrency with Rust

By: Brian L. Troutwine

Overview of this book

Most programming languages can really complicate things, especially with regard to unsafe memory access. The burden on you, the programmer, lies across two domains: understanding the modern machine and your language's pain-points. This book will teach you to how to manage program performance on modern machines and build fast, memory-safe, and concurrent software in Rust. It starts with the fundamentals of Rust and discusses machine architecture concepts. You will be taken through ways to measure and improve the performance of Rust code systematically and how to write collections with confidence. You will learn about the Sync and Send traits applied to threads, and coordinate thread execution with locks, atomic primitives, data-parallelism, and more. The book will show you how to efficiently embed Rust in C++ code and explore the functionalities of various crates for multithreaded applications. It explores implementations in depth. You will know how a mutex works and build several yourself. You will master radically different approaches that exist in the ecosystem for structuring and managing high-scale systems. By the end of the book, you will feel comfortable with designing safe, consistent, parallel, and high-performance applications in Rust.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
Packt Upsell


What should we understand from all of this? To produce software that operates at the edge of the machine's ability, you must understand some important things. Firstly, if you aren't measuring your program, you're only guessing. Measuring runtime, as criterion does, is important but a coarse insight. Where is my program spending its time? is a question the Valgrind suite and perf can answer, but you've got to have benchmarks in place to contextualize your questions. Measuring and then validating behavior is also an important chunk of this work, which is why we spent so much time on QuickCheck and AFL. Secondly, have a goal in mind. In this chapter, we've made the speed of standard library HashMap our goal but, in an actual code base, there's always going to be places to polish and improve. What matters is knowing what needs to happen to solve the problem at hand, which will tell you where your time needs to be spent. Thirdly, understand your machine. Modern superscalar, parallel machines...