RFR: 8373026: C2 SuperWord and Vector API: vector algorithms test and benchmark [v7]

[Emanuel Peter]

On Fri, 16 Jan 2026 16:03:37 GMT, Emanuel Peter <epeter at openjdk.org> wrote:

>> This is an exploratory work. I wanted to use auto vectorization and the Vector API to implement some SIMD algorithms. We don't have too many IR tests and benchmarks, so I'm proposing an initial set of them, to be extended in the future.
>> 
>> Note: for now they are all `int` based. And some of them may not use the Vector API optimally, so feel free to propose ideas and integrate them in a follow-up RFE ;)
>> 
>> **Discussion**
>> 
>> Observations:
>> - If the loop can be auto vectorized, that is the fastest. If we cannot vectorize, we at least get reasonable scalar performance.
>> - If the Vector API code can be fully intrinsified, we get fast code. But somtimes, the Vector API is horribly slow, much slower than scalar loop performance.
>>   - `linux_aarch64_server`: `filterI`, `scanAddI`, `reduceAddIFieldsX4` are very slow
>>   - `macosx_aarch64`: `filterI`, `scanAddI`, `reduceAddIFieldsX4`, `findMinIndex` are very slow
>>   - `linux_x64_oci_server`: Vector API leads to really nice speedups
>>   - `windows_x64_oci_server`: the only one that gets good/better performance on all benchmarks
>>   - `macosx_x64_sandybridge`: `scanAddI`!, `reduceAddIFieldsX4` are very slow. Other benchmarks benefit.
>> - Compact Object Headers has some negative effect on some loop benchmarks.
>>   - `linux_aarch64_server`: `reduceAddI`, `copyI`
>>   - `macosx_aarch64`: `mapI`, `reduceAddI`, `copyI`
>>   - `linux_x64_oci_server`: `reduceAddI`, `copyI`, `findI`?
>>   - `windows_x64_oci_server`: `reduceAddI` and some others a little bit
>>   - `macosx_x64_sandybridge`: `fillI`, `iotaI`, `mapI`, `reduceAddI`, `copyI`
>> - Intrinsics can be much faster than auto vectoirzed or Vector API code.
>>   - `linux_aarch64_server`: `copyI`
>>   - `macosx_x64_sandybridge`: actually, `Arrays.fill` seems to suffer with Compact Object Headers as well.
>> - `rearrange` often needs to do the `mask load` and `and` operation inside the loop. That has a slight performance impact, I filed [JDK-8373240](https://bugs.openjdk.org/browse/JDK-8373240).
>> 
>> **Benchmark Plots**
>> 
>> Units: nanoseconds per algorithm invocation.
>> 
>> `linux_x64_oci`
>> <img width="4500" height="6000" alt="algo_linux_x64_oci_server" src="https://github.com/user-attachments/assets/f2c5bbcb-e009-4c54-a1bf-91af45326cb9" />
>> 
>> `windows_x64_oci`
>> <img width="4500" height="6000" alt="algo_windows_x64_oci_server" src="https://github.com/user-attachments/assets/8946d248-4d75-4b16-8f17-627a90dcb6c3" />
>> 
>> `macosx_x64_sandybridge`
>> <img width="4500" he...
>
> Emanuel Peter has updated the pull request incrementally with one additional commit since the last revision:
> 
>   add hashCodeB test and benchmark

I also added a `hashCode` benchmark. One of the VectorAPI approaches looks faster than our intrinsics:

Benchmark                                (NUM_X_OBJECTS)  (SEED)  (SIZE)  Mode  Cnt       Score     Error  Units
VectorAlgorithms.hashCodeB_Arrays                  10000       0  640000  avgt   10   97211.277 ±  92.931  ns/op
VectorAlgorithms.hashCodeB_VectorAPI_v1            10000       0  640000  avgt   10  362260.946 ± 375.695  ns/op
VectorAlgorithms.hashCodeB_VectorAPI_v2            10000       0  640000  avgt   10   63640.184 ± 802.249  ns/op
VectorAlgorithms.hashCodeB_loop                    10000       0  640000  avgt   10  784368.577 ± 877.616  ns/op

Note: the `v2` solution that looks fastest is inspired by:
https://www.dynatrace.com/news/blog/java-arrays-hashcode-byte-efficiency-techniques/

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PR Comment: https://git.openjdk.org/jdk/pull/28639#issuecomment-3768381551