VOvonnik·Aug 29, 2016
Reminds me of JavaCPP: https://github.com/bytedeco/javacpp
In Pythran: a Python to C++ compiler with a focus on scientific computingbytedeco
javacpp
Java2016–2021
bytedeco / javacpp
javacpp
The missing bridge between Java and native C++
6Distinct people
19Mentions
0Explicit recommenders
4.7KGitHub stars
What to know before exploring
What it is
The missing bridge between Java and native C++
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
No recommendation phrase was found in the indexed comments.
No classified warning is attached to this page. Read the discussions before treating silence as approval.
Also in these discussions
- deeplearning4j/libnd4j · linked in 3 of the same discussions
- deeplearning4j/deeplearning4j · linked in 2 of the same discussions
Mentioned in the same Hacker News threads. That is not proof they are substitutes.
This week on Hacker News
Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 2:59:58 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by year19 dated mentions
Observed mentions: 2016-02-13 15:43:24 UTC to 2021-03-25 14:58:56 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 9 | 1 | 8 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 4 | 0 | 4 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 3 | 0 | 3 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
9 sourcesAnyone interested in this should look at JavaCPP, the bridge between Java and C++.
https://github.com/bytedeco/javacpp
Created and maintained by a Skymind engineer.
In Project Panama: Interconnecting JVM and Native CodeTotal tangent: Anyone doing scientific computing work on the JVM may be interested in:
ND4J: N-dimensional arrays for the JVM
http://nd4j.org/
Libnd4j: The C++ engine powering the above
https://github.com/deeplearning4j/libnd4j
JavaCPP: The bridge between Java and C++ (Cython for Java)
https://github.com/bytedeco/javacpp
Fwiw, all that works on Spark with multi-GPUs.
http://deeplearning4j.org/spark
http://deeplearning4j.org/gpu
In Python JITs are comingNah I mean this stuff IS hard. That's why I offered that mailing list. Like I said a lot of people from azul and a lot of the hardcore jvm heads sit on there.
If there's a way to make java faster for that great. You are are right that the jvm DOES support some of the tight loop stuff.
It's harder to do and leads to messy code though. In C++ you can write maintainable code and add pragmas. It's a lot cleaner and with javacpp (https://github.com/bytedeco/javacpp) we just generate the jni bindings.
We found that to be easier in practice. I'm admittedly not THAT familiar with the jvm internals.
I have just tried to get it to work and have did enough reading to know jni will just be cleaner for that stuff.
The JVM is certainly fast enough, just mainly for distributed systems code (eg: databases, message queues)
Honestly that's what makes it ap
Read the rest
pealing for me. I can write fast production systems and tweak it where necessary for the HPC apps.
Java provides a fast and safe baseline for 90% of the stuff I'd want it to do. We tried for about a year to get it to work for lin alg and finally said screw it let's just do c++.
We do scientific computing on the jvm (mainly deep learning).
We wrote:
https://nd4j.org
which immitates numpy and co on the jvm with builtin gpu support and linking against blas like other frameworks.
We also hand implemented a lot of ufuncs for you in c++.
It's mainly oriented towards dense atm though.
Sparse will come at some point but it hasn't been a big feature for us.
It uses:
https://github.com/bytedeco/javacpp underneath.
Happy to answer questions!
In 135M messages a second between processes in Java (2013)TensorFlow has kind of won the Python deep-learning community, although frameworks like Keras[0] make it easier to use. While Python programmers make up the majority of deep-learning practitioners, they don't have much penetration in enterprise, which is chiefly JVM and lower-level languages.
Deeplearning4j [1] has won deep learning on the JVM. Nothing else comes close. (Disclosure: I'm one of its creators.) DL4J has the most sophisticated Spark integration, plugs into Kafka and works as a Hadoop job. It does all that while running on distributed GPUs, multiple CPUs and heterogenous hardware. It wraps cuDNN, which is faster than Tensorflow.
Finally, part of our stack, JavaCPP [2], is used by the TF communities as well as other big company projects to bridge the gap between Java and C++.
Dev mindshare in the form of Github stars doesn't eq
Read the rest
ual revenue.
Finally, there are a number of strategic problems companies face when adopting a Google project. First, they are leery of depending on Google when they also compete with it. This is particularly true because Google doesn't open source a lot of its best work, like code from DeepMind. So Tensorflow adopters are getting the leftovers in a way, and they're kneecapped in the AI race.
Secondly, Tensorflow is not platform neutral. It's optimized for TPUs, which are not publicly available. It's a play to attract people to the Google Cloud.
[0] http://keras.io/
[1] http://deeplearning4j.org/
[2] https://github.com/bytedeco/javacpp
Relevant:
JavaCPP, the glue code that acts as a bridge to C++/C
https://github.com/bytedeco/javacpp
JavaCPP's creator works with Skymind, and we use it for our open-source deep learning framework, Deeplearning4j
https://github.com/deeplearning4j/deeplearning4j/
We're push the limits of the JVM (and we love Cliff's work!).
In A JVM Does That? (2011) [pdf]No, that's because in the specific case of LLVM only the C API is currently mapped. We'd have to work a little bit harder for the C++ API, but there is interest in doing so, if only to use it as a better parser for JavaCPP itself:
Improve Parser: Use the Clang API
https://github.com/bytedeco/javacpp/issues/51
In JavaCPP