JavaCPP VS jmh

JavaCPP and presets for working with JNI

In the meantime you might want to check out JavaCPP: https://github.com/bytedeco/javacpp

Maybe you can use JavaCPP?

Here is the code for JNI, which uses the prebuilt JavaCPP library to call the getpid function. We don't have to write all the manual C binding code and rituals as the JavaCPP library already does it.

Javacpp is the best ffi library of all https://github.com/bytedeco/javacpp

As for our approach, we maintain a library called javacpp: https://github.com/bytedeco/javacpp which proves a python wheel like experience where we distribute natively optimized c/c++ code (and even cuda accelerated code) as jar files on maven central. We also are able to develop with a python like experience by passing pointers around and other low level constructs directly allowing optimizations that you typically only get in c/c++.

https://github.com/bytedeco/javacpp

* it maps naturally and efficiently many common features afforded by the C++ language and often considered problematic, including overloaded operators, class and function templates, callbacks through function pointers, function objects (aka functors), virtual functions and member function pointers, nested struct definitions, variable length arguments, nested namespaces, large data structures containing arbitrary cycles, virtual and multiple inheritance, passing/returning by value/reference/string/vector, anonymous unions, bit fields, exceptions, destructors and shared or unique pointers (via either try-with-resources or garbage collection), and documentation comments*

I did not try myself, but for JNI maybe this could make lives easier? https://github.com/bytedeco/javacpp

JMH for benchmarks

I would recommend using JMH instead.

Reflection is probably very roughly between 10 and 1000 times slower. Why don't you measure it yourself using JMH?

Aleksey Shipilev is another such leader, whom is especially knowledgeable about the internals of the JVM. His writings are invaluable. He is (was) the lead of the Java microbenchmark framework (JMH} which is how one would write small performance experiments in Java, and learn what really makes a difference or now.

In the last episode we implemented a parallelMap operation using streams, raw threads, a threadpool with futures, and coroutines. At first glance the raw threads was quickest, followed by futures, coroutines and then streams. In this, part 56 of an exploration of where a Test Driven Development implementation of the Gilded Rose stock control system might take us in Kotlin, we investigate the performance of the different functions further, in particular digging down into why coroutines seem to be slow and finding a way to speed them up. We also find a way to use a particular ForkJoinPool to run the streams code, making it as fast as the others (bar the raw threads). Frankly we only use very rough benchmarks here, with no statistical testing except 'it looks like'. That's OK for gross differences, but is highly suspect when deciding which of two similarly performant approaches is faster. For that check out JMH and you could watch my video from KotlinConf 2017

A few months ago, we started to use JMH in our project to test and find performance issues. The tool provides multiple modes and profilers, and we found this useful for our purposes.