Mastering Simd with Java Vector API: Unlocking Single-Core Performance Through Simd Optimization
暫譯: 精通 Java 向量 API 的 SIMD：透過 SIMD 優化解鎖單核心效能

Learn how to write Java code faster by using the Vector API for SIMD operations. This book shows you how to apply data-level parallelism to common algorithms without leaving the Java ecosystem, making modern CPU capabilities accessible to everyday developers.

The book uses a problem-first approach. Each chapter starts with a familiar coding task--such as array summation or prefix sums--and then reworks the solution using vectorization. You'll move from simple element-wise operations to advanced techniques like parallel scans, bitonic sorting, and sliding window optimizations. Extensive code examples, performance benchmarks, diagrams, and assembly analysis ensure you understand both the "how" and the "why" behind every optimization.

Today, the rise of computer-heavy applications in finance, scientific computing, machine learning and real-time analytics makes understanding vectorization an essential skill for developers. Modern processors dedicate significant silicon to vector units, yet most Java code never uses them. By mastering the Vector API, you'll learn to recognize patterns that benefit from SIMD, write production-ready vector algorithms, and make informed architectural decisions. This is your guide to writing faster, smarter Java code that fully leverages today's hardware.

What You Will Learn:

Master Java Vector API and SIMD across x86 and ARM architectures Build vector-length-agnostic algorithms for any hardware platform Spot patterns for vectorization and use masked ops for conditional logic Optimize vertical vs. horizontal ops and avoid instruction-level hazards Ensure numerical stability and verify vectorization via assembly analysis

Who This Book is for:

Intermediate to advanced Java developers who are familiar with core language features, OOP, arrays, loops, and basic algorithms and are comfortable with Big-O notation and concurrency concepts; multi-threading knowledge helpful but not required. No prior SIMD or low-level optimization experience needed.