Java Auto-Vectorization and JDK Upgrades Quiz

3 Questions

To perform operations on multiple data elements simultaneously

Cache misalignment of the data

Performance gains

Study Notes

SIMD or Single Instruction Multiple Data can simultaneously perform operations on multiple data elements.
Java byte code is platform agnostic.
In Java, currently, vectorization is not done by the programmer, but it's done automatically by the compiler.
The compiler takes standard Java byte code and automatically determines which part could be transformed into vector instructions.
Open JDK can produce vectorized machine code.
On the topic of Auto Vectorization Enhancements, Intel has contributed to the following enhancements in open JDK.
Auto-vectorization extended to 512 bits
Support for AVX512 code generation
Super unrolled main loop
Vector post loop
Vectorize additional operations
When sources and destination are all 64 bytes aligned, the best gain is observed, and cache misalignment of the data could limit the increase.
When we look at the different operations used with auto-vectorization, we can see significant performance gains in many cases in JDK 11 and JDK 17 over JDK 8.
Vector neural network instructions have been available since JDK 12.
There are tremendous benefits to upgrading your JDK. Unfortunately, it's not always the easiest for some software teams to upgrade.
Some software might rely on JDK 8 and specific APIs, but if you're performance-focused and trying to get more cost benefits out of your application and your workload, it's best to take a look at upgrading your JDK. You'll see performance gains, especially when paired with the latest processors.

3 Questions