Mastering SIMD with Java Vector API
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Mastering SIMD with Java Vector API, Apress
Unlocking Single-Core Performance Through SIMD Optimization
Von Roman Snytsar, im heise shop in digitaler Fassung erhältlich
Produktinformationen "Mastering SIMD with Java Vector API"
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
Artikel-Details
- Anbieter:
- Apress
- Autor:
- Roman Snytsar
- Artikelnummer:
- 9798868826764
- Veröffentlicht:
- 27.06.26
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