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 ➱ • The Vector API in JDK 17
◦ Examples, patterns and performances ➱ https://github.com/openjdk/jdk/tree/m...
◦ 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
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