Podcast
Questions and Answers
True or false: Java currently supports vectorization done by the programmer.
True or false: Java currently supports vectorization done by the programmer.
False
What is the maximum performance gain that can be achieved by comparing JDK 17 or even adding some benchmarks for JDK 18?
What is the maximum performance gain that can be achieved by comparing JDK 17 or even adding some benchmarks for JDK 18?
What is SIMD?
What is SIMD?
True or false: Vector Neural Network Instructions have been available since JDK 11.
True or false: Vector Neural Network Instructions have been available since JDK 11.
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What should you do to take advantage of the performance enhancements in Crypto hash?
What should you do to take advantage of the performance enhancements in Crypto hash?
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What is the main benefit of using Open JDK?
What is the main benefit of using Open JDK?
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What is the best gain observed when sources and destinations are 64 bytes aligned?
What is the best gain observed when sources and destinations are 64 bytes aligned?
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True or false: Auto-vectorization extended to 512 bits is an enhancement contributed by Intel to Open JDK.
True or false: Auto-vectorization extended to 512 bits is an enhancement contributed by Intel to Open JDK.
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What type of applications are likely to benefit from the performance enhancements in Crypto hash?
What type of applications are likely to benefit from the performance enhancements in Crypto hash?
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True or false: Upgrading your JDK could result in cost benefits for your application and workload.
True or false: Upgrading your JDK could result in cost benefits for your application and workload.
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What is not an example of a JDK version mentioned in the text?
What is not an example of a JDK version mentioned in the text?
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What is the best option for software teams trying to get more cost benefits out of their application and workload?
What is the best option for software teams trying to get more cost benefits out of their application and workload?
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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.
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Description
Test your knowledge about Java JDK, auto-vectorization, and performance gains through this quiz. Learn about SIMD, platform agnostic byte code, vectorization in Java, Open JDK enhancements, and the benefits of upgrading your JDK.
