javacpp

The missing bridge between Java and native C++

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6Distinct people

19Mentions

0Explicit recommenders

4.7KGitHub stars

REPOSITORY AT A GLANCE

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

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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, 4:00:59 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

UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
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

4 sources

agibsonccc·Oct 5, 2017

I'm not exactly sure I'd call those "bindings". If you want an actual compelling competitor to us because we're not a big name, at least look at mxnet. That's more credible. It's incredible that folks insist on this still. Both those frameworks aren't even going to be aiming for any level of integration with the JVM. At least take a look at what makes us different. A big one being: https://github.com/bytedeco/javacpp We own the whole stack here and integrate pretty deeply. This has lead to some great performance improvements. We aren't just "java bindings" but offering a lot more in one place than the other frameworks. Also: I highly doubt you've even looked at the numbers. We're actually doing pretty well in the rankings: https://twitter.com/fchollet/status/915366704401719296 We're not #1 but we have an actual user base. I think a lot o

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f our upcoming things like our python bindings where people don't have to "see" java and integrations with spark will win over some folks at big companies..maybe not DL researchers which is also fine. Our upcoming api will be like pytorch though. Regardless: I'm not sure the framework will matter long term given that model import and things like ONNX are becoming more common. Finally, I'll just say this: It doesn't hurt the ecosystem to have more competition and different interests than research. Every single person who says this usually dismisses us for 1 silly reason or another ranging from: 1. Not a "big name" (despite being put in production by a ton of them) 2. Not a lot of research papers (yeah no crap we're not a research framework, we do have publications though!) 3. They see java bindings and can't tell the difference between a full application suite and a tensor lib with java bindings generated by SWIG 4. Usually a PHD student at a university who hasn't worked at a large company and doesn't understand my actual target audience

In Show HN: Eclipse Deeplearning4j

agibsonccc·Sep 7, 2017

Disclaimer: I run a competing library to the uncomplicate series of libraries. If you want that set of guarantees with just as ease of use,you can look in to ours: http://nd4j.org/ We have various usage examples here: https://github.com/deeplearning4j/dl4j-examples/tree/master/... and the userguide: http://nd4j.org/userguide We have a lot going on underneath the hood, but we pre compile everything out of the box. We maintain a whole software stack for JNI called javacpp: https://github.com/bytedeco/javacpp One of those includes something called a "preset" which bundles openblas as a maven dependency (with the ability to link to mkl if you want) We support cuda as well. If you have any questions, stop by our live chat: https://gitter.im/deeplearning4j/deeplearning4j

In ClojureCUDA – CUDA programming in Clojure

agibsonccc·Jan 8, 2017

We wrote a lot of that stack ourselves. And our compute code is in c++. There is a lot of stuff going on in big data land (including us) wrapping a lot of the c++ in java and enabling people to use the JVM for what it's good at (data access). We are pushing this with nvidia: https://blogs.nvidia.com/blog/2016/10/06/how-skymind-nvidia-... I also work pretty closely with a lot of the spark/gpu folks at IBM. While I do largely agree with you, we have our own JNI compiler called javacpp that alleviates a lot of those concerns already: https://github.com/bytedeco/javacpp Having our own pointer class and doing our own memory management has helped a lot. What you're talking about is using java for the compute. There's no reason you can't wrap that in a runtime that most people know how to use. A lot of python folks do that now..but then you have

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to deal with python's limitations at which point the majority of your code (way more than needs be) will end up being in c anyways vs java where you can write a significant part of your app in java and have it be fast out of the box.

In Challenging Clojure in Common Lisp (2014)

agibsonccc·Jan 8, 2017

It can though. We are a java shop that does HPC and deep learning. Our work is open source. We've been around 2.5 going on 3 years now. Over the years we've surpassed bottleneck after bottleneck in the JVM. It's a faster wrapper around c code for us. We maintain and created: https://github.com/bytedeco/javacpp which wraps a lot of c++ components. This is also how we do GPUs and the like with our own memory management for our "numpy for java": https://github.com/deeplearning4j/nd4j/tree/master/nd4j-back... Short of it "off heap and NO GC" matters a lot. We have 1 c codebase we use that's meant to be controlled via JNI here: https://github.com/deeplearning4j/libnd4j The speed gains we've seen are massive. Java can't compete with good ole simd and the like for numerical computing. We are a big advocate of the JVM as a platform but let's be c

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lear about its weaknesses. You need to use unsafe (see: netty,aeron, and any other low level database written in java or using java as the core language with some c++)and other tricks out of the box for real performance.

In Challenging Clojure in Common Lisp (2014)