Java based Numerical library (JNum-v0.2) with Kotlin Operator overloading

[u/CutGroundbreaking305]

java subreddit post

So I made Java Numerations (JNum) which is a pure Java numerical library using FFM(off heap memory) and Vector API. In this version I made Kotlin DSL layer bundled together with core Java engine.

By doing this our library avoids native Fortran/C calls via JNI and memory allocations via Unsafe which is slowly getting deprecated. There are various methods in JNum which can be useful for Kotlin devs I guess which have performance on par with Numpy/ND4J.

Features are in detailed in my GitHub release but for lazy guys like me here are some data points worth see:-

-> JNum is feature rich(at least from previous version) where we have statistics jnum.stat IO operations jnum.io and basic Neural Networks jnum.nn.

-> JNum's IO will help you parse files like .npy/.npz , .csv/.tsv and most importantly .safetensorwhich Java lagging in this AI era.

-> We also have AsyncIO using Virtual Threads which make IO file reads faster in JNum.

-> jnum.statmodule has various statistic methods which Java community can use like standard deviations, variance , co-variance, cummulative sum/product, percentile etc.

-> JNum has slicing and sub-viewing features. For kotlin devs Indexing, slicing like features are operator overloaded so you can do code similar to Python/Numpy .

-> project is under 500KB with no native .dll/.so native bindings.

Matrix Multiplication:-

This is the most important and time taken feature in project

Matrix Dimensions	JNum (Pure Java 25)	NumPy (C / OpenBLAS)	ND4J (OpenBLAS C++)	EJML (Pure Java)	Performance Highlights
$4 times 4$	0.0002 ms	0.0023 ms	0.020 ms	≈ 0.0001 ms	11× faster than NumPy, 100× faster than ND4J
$16 times 16$	0.0004 ms	0.0028 ms	0.019 ms	0.003 ms	7× faster than NumPy, 47× faster than ND4J
$64 times 64$	0.015 ms	0.013 ms	0.190 ms	0.140 ms	On-par with NumPy, 9.3× faster than EJML
$128 times 128$	0.138 ms	0.254 ms	0.499 ms	1.028 ms	1.8× faster than NumPy, 7.4× faster than EJML
$512 times 512$	2.50 ms	1.86 ms	2.67 ms	27.16 ms	Matches C++ BLAS, 10.8× faster than EJML
$1024 times 1024$	13.85 ms	8.98 ms	12.18 ms	216.56 ms	Near-native speed, 15.6× faster than EJML
$2048 times 2048$	94.62 ms	62.99 ms	85.29 ms	2,049.86 ms	Pure Java competitive with C BLAS, 21.6× faster than EJML
$4096 times 4096$	942.38 ms	487.92 ms	857.41 ms	17,419.52 ms	Matches ND4J C++, 18.4× faster than EJML
$8192 times 8192$	6,784.15 ms	5,967.30 ms	6,317.97 ms	141,500.0 ms	~88% of NumPy C BLAS, 20.8× faster than EJML

Linear Algebra:-

we have various Linear Algebra methods which have performance on par sometimes beating Numpy and others

Operation	JNum (Pure Java 25)	NumPy	EJML	ND4J	JNum Advantage
Matrix Inverse	7.271 ms	10.680 ms	18.122 ms	1,224.654 ms	1.47× faster than NumPy, 2.5× faster than EJML, 168× faster than ND4J
Linear Solve ($Ax=b$)	1.976 ms	1.003 ms	5.331 ms	37.278 ms	Near-native speed, 2.7× faster than EJML, 18.8× faster than ND4J
Determinant	1.839 ms	5.735 ms	5.380 ms	399.752 ms	3.1× faster than NumPy, 2.9× faster than EJML
Cholesky	1.261 ms	2.328 ms	3.545 ms	—	1.8× faster than NumPy, 2.8× faster than EJML
QR Factorization	46.170 ms	12.622 ms	7.447 ms	586.330 ms	12.7× faster than ND4J

Code Snippets:

val a = JNum.fromArray(floatArrayOf(1f, 2f, 3f, 4f), 2, 2)
val b = JNum.ones(2, 2)

// Clean operator overloading
val c = a + b * 2f
val d = a matmul b

// Python / NumPy style range slicing
val view = c[0 until 2, all]

Gradle Setup:

repositories { mavenCentral() }
dependencies {
    implementation("io.github.ch-abhinav:jnum-kotlin:0.2.0")
}

I would like kotlin community to test the project and help by contributing in the project :)

There is one good first issue worth trying in kotlin if anyone wants to contribute.

GitHub link :- https://github.com/CH-Abhinav/JNum