Learn how to write fast Java code with the Vector API - JEP Café #18

The Vector API can tremendously speed up computations by using the SIMD capabilities of your CPU. Learn how parallel computing works on a SIMD machine, how the Java Vector API gives you access to these capabilities, and how you can structure your code to leverage these capabilities for amazing performances. ⎯⎯⎯⎯⎯⎯ Chapters ⎯⎯⎯⎯⎯⎯ 0:00 Intro 1:37 Introducing the SIMD features of your CPU 2:41 Scalar parallel computing based on concurrency 5:53 Vector parallel computing based on SIMD machine 8:51 Shape and species of a Vector 10:33 Creating vectors from arrays to sum them 12:25 Loading any array in a vector using masks 15:56 Avoiding masking when it is not supported 17:16 Parallel cross-lanes and lane-wise operations 18:58 Computing the norm of a vector in parallel 21:19 Computing the average of vector components in parallel 22:12 Filtering and compressing a vector in parallel 24:20 Reducing a vector in parallel 24:44 Wrapping up parallel computations using vectors 25:29 Examples, patterns and performances 27:22 Outro ⎯⎯⎯⎯⎯⎯ Resources ⎯⎯⎯⎯⎯⎯ ◦ JEP 438: Vector API ➱ https://openjdk.org/jeps/438 ◦ The Vector API in the JDK 17 by Paul Sandoz ➱ https://youtu.be/1JeoNr6-pZw ◦ Examples, patterns and performances ➱ https://github.com/openjdk/jdk/tree/master/test/micro/org/openjdk/bench/jdk/incubator/vector ◦ Dev.java ➱ https://dev.java ◦ Inside.java ➱ https://inside.java ◦ JDK 20 ➱ https://openjdk.org/projects/jdk/20 ◦ OpenJDK ➱ https://openjdk.org ◦ Oracle Java ➱ https://www.oracle.com/java/ Tags: #Java #Java17 #Java20 #OpenJDK #JDK #JDK17 #VectorAPI #JEPCafe #insidejava #SIMD