v0.2.0-Major Changes

JNum v0.2.0: The Pure Java Numerical & Tensor Computing Ecosystem

We are thrilled to announce the official release of JNum v0.2.0!

While v0.1 proved that modern Java can replace C++ math backends by combining Java 25, the Foreign Function & Memory (FFM) API, and Project Panama Vector API, v0.2.0 expands JNum from a fast tensor engine into a comprehensive, production-ready numerical computing ecosystem.

JNum v0.2.0 introduces a 4-tier BLIS-inspired SIMD matrix multiplication engine, a complete Linear Algebra suite (SVD, Eigen, QR, Cholesky, Solvers), HuggingFace Safetensors & NumPy I/O with zero-copy memory mapping, a fused JIT expression engine, a statistical computing library, a neural network module, and a dedicated Kotlin DSL with operator overloading.

All of this remains 100% pure Java — zero JNI, zero native .dll/.so binaries, and zero sun.misc.Unsafe.

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🚀 Key Features & Architectural Upgrades

1. 4-Tier Hardware-Adaptive BLIS MatMul Engine

Matrix multiplication has been completely re-engineered from the ground up, replacing scalar/basic SIMD with a 4-tier hardware-adaptive BLIS-style execution hierarchy:

• Tier 0 (Nano Micro-Kernels): Fully unrolled register-direct kernels for small matrices ($\le 4\times 4$), eliminating loop and dispatch overhead.
• Tier 1 (Direct Register-Blocked SIMD): Direct SIMD kernels without packing overhead for matrices up to $128\times 128$ that comfortably fit in L1/L2 CPU cache.
• Tier 2 (Single-Threaded BLIS Cache-Blocked): Full 5-loop cache-blocking with $A$-panel ($M_C \times K_C$) and $B$-panel ($K_C \times N_R$) packing for medium matrices ($128 < \max \le 256$) and low-FLOP counts.
• Tier 3 (Multi-Threaded BLIS Parallel): Cache-oblivious macro-kernel with parallel work-stealing ForkJoin scheduling for large matrices ($> 256\times 256$).
• Performance:
  • $1024 \times 1024$: ~10ms (best) / 14ms (avg) (down from 51ms in preview and 23ms in v0.1) — 15–22× faster than EJML and matching native OpenBLAS!
  • $8192 \times 8192$: ~5,500ms (best) / 6,500–7,000ms (avg) — 20–25× faster than EJML (which takes >141 seconds).

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2. Complete Linear Algebra Suite (jnum.linalg.Linalg)

A full-featured linear algebra module matching numpy.linalg:

• Matrix Decompositions:
  • Linalg.svd(A) — Singular Value Decomposition ($U, \Sigma, V^T$).
  • Linalg.eig(A) & Linalg.eigh(A) — Eigenvalue and eigenvector solvers for general and symmetric/Hermitian matrices.
  • Linalg.qr(A) — Householder QR factorization ($Q, R$).
  • Linalg.cholesky(A) — Fast Cholesky decomposition ($L L^T$).
• Solvers & Matrix Inversion:
  • Linalg.solve(A, B) — Direct solver for linear matrix equations $AX = B$.
  • Linalg.inv(A) — Full matrix inversion.
  • Linalg.pinv(A) — Moore-Penrose pseudo-inverse with singular-value tolerance thresholding.
• Metrics, Determinants & Norms:
  • Linalg.det(A) & Linalg.slogdet(A) — Determinant and sign/log-determinant for numerical stability.
  • Linalg.matrixRank(A) — SVD-based numerical rank determination.
  • Linalg.cond(A) — Matrix condition number.
  • Linalg.norm(A, ord) — Matrix and vector $p$-norms (Frobenius, nuclear, $L_1, L_2, L_\infty$).
  • Linalg.trace(A, offset) — Diagonal trace with arbitrary offsets.
  • Linalg.cosineSimilarity(A, B) — High-speed vector cosine similarity.

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3. High-Performance File I/O & Zero-Copy Memory Mapping (jnum.io.JNumIO)

First-class interoperability with modern AI/ML and Python formats:

• Hugging Face Safetensors:
  • Direct reading and writing of .safetensors model weights without external dependencies.
  • readSafetensorsMmap(path) / MmapTensors: Instantaneous, zero-copy memory mapping of multi-gigabyte neural network checkpoints via JDK 25 MemorySegment.map(...).
  • inspectSafetensors(path): Fast header inspection without loading tensor payload.
• NumPy Formats (.npy & .npz):
  • Full support for NumPy .npy (v1.0 & v2.0) and multi-array compressed/uncompressed .npz archives.
  • readNpyMmap(path) / MmapArray for memory-mapped zero-copy .npy ingestion.
• CSV / TSV / Delimited Files:
  • Ultra-fast readCsv() and writeCsv() powered by FastNumberParser (bypassing slow Double.parseDouble()).
• Asynchronous Non-Blocking I/O:
  • Non-blocking CompletableFuture APIs for all readers and writers (readNpyAsync, writeSafetensorsAsync, readCsvAsync, etc.).

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4. Statistical Computing Module (jnum.stat.Stats)

NumPy-grade summary statistics with full axis reduction and keepDims options:

• Central Tendency & Dispersion: mean(), average() (with arbitrary weighting tensors), var(), std(), sampleVar(), sampleStd() with custom degrees of freedom (ddof).
• Quantiles & Percentiles: median(), quantile(), percentile(), iqr() (interquartile range) implementing all 9 standard quantile estimation methods (QuantileMethod).
• Multivariate & Discrete Analysis: cov() (full covariance matrices with rowvar support), mode(), ptp() (peak-to-peak), cumsum(), cumprod(), and diff() (n-th discrete differences).

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5. Fused JIT Expression Engine (jnum.internal.eval.EvalEngine)

Compile and evaluate complex compound expressions with zero intermediate allocations:

// Evaluates (a + b) * c - d in a single fused SIMD pass without temporary arrays
NDArray result = JNum.evaluate("(a + b) * c - d", a, b, c, d);

• AST-based parsing and compilation directly into vectorized SIMD loops.
• ConcurrentHashMap expression plan cache eliminates parser overhead in hot loops.
• Over 3× faster than chained standard operations on large datasets.

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6. Neural Network Foundation (jnum.nn)

Lightweight, composable neural network components:

• Module base class and Sequential container.
• Linear layer with configurable weights and biases.
• SIMD-accelerated activation functions: ReLU, Sigmoid, Softmax, and Tanh.

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7. First-Class Kotlin Module (kotlin-core)

Seamless, idiomatic Kotlin support via io.github.ch-abhinav:jnum-kotlin:

• Operator Overloading: a + b, a - b, a * b, a / b, -a, plus in-place operators (+=, -=, etc.).
• Infix Linear Algebra: val c = a matmul b, val d = x dot y.
• Pythonic Multidimensional Slicing:

  val slice = arr[0..3, all, 2..10 step 2]
  val row = arr[2, all]

• All operators are marked inline to guarantee zero JVM call-frame or boxing overhead.

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8. Expanded Type System & Vectorized Logic

• Added full DType.bool support.
• SIMD logical reductions and operations: all(), any(), and(), or(), not(), xor().
• SIMD elementwise comparisons: maximum(), minimum().
• Hardware-aligned memory allocations (64-byte alignment) to completely prevent split-cache-line penalties.

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📊 Benchmark Shootout (Java 25, AVX-512/AVX2)

Benchmarks run via JMH 1.37 on JDK 25 (-Xms8g -Xmx8g -XX:+AlwaysPreTouch -XX:+UseParallelGC --add-modules jdk.incubator.vector).

1. Matrix Multiplication (ms/op — lower is better)

Matrix Dimension	JNum v0.1	JNum v0.2.0 (avg)	JNum v0.2.0 (best)	EJML v0.43	NumPy (OpenBLAS)
64 × 64	~0.08 ms	0.013 ms	0.010 ms	0.140 ms	0.013 ms
128 × 128	~0.45 ms	0.107 ms	0.095 ms	1.028 ms	0.254 ms
512 × 512	~4.10 ms	3.45 ms	2.80 ms	27.16 ms	1.86 ms
1024 × 1024	~23.0 ms	14.0 ms	10.0 ms	216.56 ms	8.98 ms
2048 × 2048	—	143.5 ms	130.0 ms	2,049.86 ms	62.99 ms
4096 × 4096	—	1,102.2 ms	980.0 ms	17,419.52 ms	487.92 ms
8192 × 8192	—	6,500–7,000 ms	5,500 ms	141,500+ ms (DNF)	5,967.30 ms

⚡ JNum v0.2.0 is 15–22× faster than EJML at 1024×1024, and performs on par with native C/Fortran OpenBLAS.

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2. Transcendental & Trigonometric Functions (100M Elements, ms/op)

Operation	JNum v0.2.0	EJML	NumPy (OpenBLAS)	Speedup vs. EJML	Speedup vs. NumPy
Sin	151.6 ms	1,334.8 ms	154.9 ms	8.8× faster	1.02× faster
Cos	170.9 ms	1,077.3 ms	151.3 ms	6.3× faster	≈ tie
Tan	269.2 ms	1,767.3 ms	792.7 ms	6.6× faster	2.9× faster
Exp	111.5 ms	805.0 ms	122.1 ms	7.2× faster	1.1× faster
Log	121.5 ms	844.1 ms	164.2 ms	6.9× faster	1.35× faster
Sqrt	51.3 ms	55.0 ms	48.6 ms	1.07× faster	≈ tie
Tanh	301.4 ms	1,493.3 ms	197.4 ms	5.0× faster	0.65×
Sigmoid	115.0 ms	973.7 ms	440.5 ms	8.5× faster	3.8× faster

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3. Linear Algebra Inversion & Decompositions (ms/op)

Operation	JNum v0.2.0	ND4J (CPU Native)	Speedup vs. ND4J
Inverse [32×32]	0.025 ms	2.68 ms	107× faster
Inverse [128×128]	0.798 ms	152.56 ms	191× faster
Inverse [256×256]	7.271 ms	1,224.65 ms	168× faster
Determinant [128×128]	0.260 ms	43.95 ms	169× faster
Cholesky [32×32]	0.006 ms	6.64 ms	1,100× faster
Solve [128×128]	0.277 ms	9.83 ms	35× faster
QR [128×128]	3.302 ms	134.53 ms	41× faster

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📦 Getting Started

1. Maven Dependency

JNum is published under io.github.ch-abhinav:

<!-- Core Java Tensor & Math Library -->
<dependency>
    <groupId>io.github.ch-abhinav</groupId>
    <artifactId>jnum</artifactId>
    <version>0.2.0</version>
</dependency>

<!-- Optional: Kotlin Idiomatic Operator Extensions -->
<dependency>
    <groupId>io.github.ch-abhinav</groupId>
    <artifactId>jnum-kotlin</artifactId>
    <version>0.2.0</version>
</dependency>

2. Gradle (Kotlin DSL)

implementation("io.github.ch-abhinav:jnum:0.2.0")
implementation("io.github.ch-abhinav:jnum-kotlin:0.2.0")

3. JVM Launch Configuration

Because JNum uses the Project Panama Vector API, add the following flag when compiling and running on Java 21+:

--add-modules jdk.incubator.vector

(Optional for cleanest execution: --enable-native-access=ALL-UNNAMED)

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🛠️ Quick Example

Java

import jnum.JNum;
import jnum.NDArray;
import jnum.linalg.Linalg;
import jnum.io.JNumIO;
import java.nio.file.Path;

// 1. Create tensors
NDArray a = JNum.randn(1024, 1024);
NDArray b = JNum.eye(1024);

// 2. BLIS-accelerated MatMul
NDArray c = Linalg.matmul(a, b);

// 3. Vectorized Math
NDArray activated = JNum.sigmoid(c);

// 4. Zero-copy Safetensors / NumPy I/O
JNumIO.writeNpy(activated, Path.of("weights.npy"));

Kotlin

import jnum.*
import java.nio.file.Path

val a = JNum.randn(512, 512)
val b = JNum.eye(512)

// Infix & operator overloads
val c = (a matmul b) + 2.5
val slice = c[0..10, all]

// Safetensors export
JNumIO.writeSafetensors(mapOf("tensor" to slice), Path.of("model.safetensors"))

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⚠️ Migration & Breaking Changes from v0.1

1. Maven Coordinates: Group ID migrated from com.github.ch-abhinav to official verified Maven Central namespace: io.github.ch-abhinav.
2. Modular Architecture: Project is now modularized into jnum-parent, jnum (Java core), and jnum-kotlin.
3. Dedicated Namespaces:
   • Linear algebra functions moved to jnum.linalg.Linalg.
   • File format loaders moved to jnum.io.JNumIO.
   • Statistics moved to jnum.stat.Stats.
   • Neural network components reside in jnum.nn.