The track-record of Java isn't very good IMHO. For example:
1) the Hotspot JVM isn't very good at identifying where SIMD instructions 
should be used (Azul Zing with the LLVM backend does a better job). 
2) There is no official integration for GPGPU (General-Purpose computing on 
Graphics Processing Units) even though powerful GPU's are everything but 
rare. 
3) Support for hardware transactional memory
4) Hardware accelerated encryption; although Java finally caught up in Java 
11. 
5) Memory layout of objects
6) record types (to reduce the overhead of objects) 

So based on that I would not be surprised if official integration would be 
added very late if added at all.

On Monday, October 21, 2019 at 1:25:05 PM UTC+3, Benoît Paris wrote:
>
> Hello all!
>
> I'm seeing more and more news about new exotic silicon pushing code closer 
> to the data, and I was wondering what was the future of JVMs in all this.
>
> As an example: Upmem <https://www.upmem.com/technology/>is starting to 
> offer some RAM with compute capabilities. They claim 2TB/s of 
> RAM-compute-RAM bandwidth for a 128GB set. The low level API seems to be an 
> LLVM backend and code 
> <https://github.com/upmem/dpu_demo/tree/sdk-2019.3/checksum>close to a 
> map operation: for each chunk of computing ram, send a tasklet for a local 
> computation.
>
> I have lots of questions:
>
> * Are Java and the JVMs suited to make good use of that hardware?
> * What's the mechanically sympathetical API to it? Are map operations in 
> Parallel Streams a good abstraction?
> * Surely the results must be stored locally, within each RAM chunk. What 
> would automated memory management look like with this? Is it per RAM chunk, 
> can it be global? Is there a need for rebalancing/shuffling data between 
> RAM chunks?
> * Are we going to see it at all on JVMs? What's the integration cost? Do 
> you translate Java bytecode to LLVM to use their backend? Can it be done 
> through the upcoming Vector API?
>
> Cheers
> Ben
>

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