Show HN: JNum – Pure Java 25 NumPy/BLAS via Project Panama FFM and Vector API

JNum, a pure Java 25 and Kotlin numerical computing library built on Project Panama's Foreign Function & Memory API (JEP 454) and the Vector API, was posted to Hacker News as a NumPy/BLAS alternative with a total JAR footprint of roughly 470 KB. JNum's author reports JMH 1.37 benchmarks on Java 25 LTS against NumPy 2.4 with OpenBLAS, ND4J 1.0.0-M2.1, EJML 0.44 and ojAlgo 55.0, claiming a 0.0002 ms result on one matrix dimension versus 0.0023 ms for NumPy and 0.020 ms for ND4J, described as 11x faster than NumPy and 100x faster than ND4J. The library ships a 4-tier GEMM engine routing across Nano scalar, register-tiled, single-threaded BLIS and parallel ForkJoin kernels, O(1) zero-copy slicing, symbolic expression evaluation, Safetensors and .npy/.npz memory-mapped I/O, and a built-in jnum.nn inference module.

A high-performance, hardware-accelerated numerical computing library for modern Java JDK 25+ and Kotlin, powered by Project Panama Foreign Function & Memory API and the Vector API . JNum brings the ergonomics of NumPy and PyTorch to the JVM with native BLAS performance— without native C++ build toolchains, JNI boundaries, or garbage collection overhead. Traditional JVM numerical and tensor libraries such as ND4J or Deeplearning4j rely on native C++ backends. They manipulate memory using deprecated sun.misc.Unsafe and cross the native boundary via JNI Java Native Interface . | Metric | Legacy Java Tensor Libraries | JNum | |---|---|---| | Backend Implementation | C++ OpenBLAS, OneDNN, LibTorch bridged via JNI | 100% Pure Java 25 & Kotlin | | Native Library Bloat | 100+ MB Bundles .dll , .so , .dylib binaries | < 500 KB total JAR footprint ~470 KB | | Memory Architecture | Deprecated sun.misc.Unsafe Memory corruption risk | Foreign Function & Memory JEP 454 | | SIMD Acceleration | Opaque native compiler intrinsics | JVM Vector API jdk.incubator.vector | | Memory Safety | Out-of-bounds causes native process crash SIGSEGV | 100% JVM Memory Safe Catchable Exceptions | | Deployment Complexity | Fragile native dependencies, dynamic linker errors | Drop-in JAR: Runs anywhere Java runs | python import jnum.DType; import jnum.JNum; import jnum.NDArray; import jnum.Slice; void main { // 1. Allocate 64-byte aligned off-heap tensors NDArray a = JNum.rand 1024, 512 ; NDArray b = JNum.rand 512, 256 ; // 2. Hardware SIMD matrix multiplication 4-tier BLIS engine NDArray c = a.matmul b ; // Shape: 1024, 256 // 3. Zero-copy subviews and Pythonic slicing NDArray slice = c.slice "0:10, 0:5" ; // 4. In-place zero-allocation mutations slice.muli 2.0f ; IO.println "Top-left block:\n" + slice ; } python import jnum. fun main { val x = JNum.rand 128, 64 val w = JNum.rand 64, 10 val b = JNum.zeros 10 // Natural operator overloading and infix linear algebra val logits = x matmul w + b // Python-style multidimensional range slicing val topScores = logits 0 until 5, all println "Top scores:\n$topScores" } - Hardware SIMD Vector Lanes : Automatically probes your CPU to schedule AVX-512 16 lanes , AVX2 8 lanes , or ARM NEON 4 lanes instructions via the Vector API. - 4-Tier GEMM Engine : Hardware-adaptive matrix multiplication routing across Nano scalar, Direct register-tiled, Single-threaded BLIS, and Parallel ForkJoin macro-kernels. - Zero-Copy Layouts & Slicing : Transposition, reshaping, subviews, and strided slices are constant-time$O 1 $ operations with zero memory copying. - Symbolic Expression Evaluation : Compile formulas like JNum.eval "$0 $1 + $2", a, b, c or JNum.eval "x w + b", map into hardware FMA instructions with automatic buffer recycling. - Modern AI Checkpoint I/O : Direct zero-copy memory mapping for Hugging Face Safetensors .safetensors and NumPy binaries .npy , .npz , plus high-throughput CSV/TSV parsing. - Pure-Java Neural Network Inference : Built-in jnum.nn modules Linear with sequence rank preservation, ReLU , Sigmoid , Tanh , Softmax , and Sequential . All benchmarks were measured via OpenJDK JMH 1.37 on Java 25 LTS -Xms8g -Xmx8g -XX:+AlwaysPreTouch -XX:+UseParallelGC --add-modules jdk.incubator.vector , comparing JNum against NumPy 2.4 OpenBLAS/C backend and standard JVM numerical engines EJML 0.44 , ojAlgo 55.0 , and ND4J 1.0.0-M2.1 with native OpenBLAS backend . | Matrix Dimensions | JNum Pure Java 25 | NumPy C / OpenBLAS | ND4J OpenBLAS C++ | EJML Pure Java | Performance Highlights | |---|---|---|---|---|---| | | 0.0002 ms | 0.0023 ms | 0.020 ms | ≈ 0.0001 ms | 11× faster than NumPy, 100× faster than ND4J | | | 0.0004 ms | 0.0028 ms | 0.019 ms | 0.003 ms | 7× faster than NumPy, 47× faster than ND4J | | | 0.015 ms | 0.013 ms | 0.190 ms | 0.140 ms | On-par with NumPy, 9.3× faster than EJML | | | 0.138 ms | 0.254 ms | 0.499 ms | 1.028 ms | 1.8× faster than NumPy, 7.4× faster than EJML | | | 2.50 ms | 1.86 ms | 2.67 ms | 27.16 ms | Matches C++ BLAS, 10.8× faster than EJML | | | 13.85 ms | 8.98 ms | 12.18 ms | 216.56 ms | Near-native speed, 15.6× faster than EJML | | | 94.62 ms | 62.99 ms | 85.29 ms | 2,049.86 ms | Pure Java competitive with C BLAS, 21.6× faster than EJML | | | 942.38 ms | 487.92 ms | 857.41 ms | 17,419.52 ms | Matches ND4J C++, 18.4× faster than EJML | | | 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 | Note Why JNum wins on small-to-medium matrices: Legacy C++ wrappers ND4J and Python C-API bindings NumPy incur dispatch and JNI call boundary overheads. JNum's Tier 0 nano-scalar and Tier 1 direct SIMD kernels execute inside CPU vector registers without crossing native boundaries or scheduling thread pools. | Vector Math Op | JNum Pure Java 25 | NumPy | EJML | ojAlgo | ND4J Native C++ | JNum Advantage | |---|---|---|---|---|---|---| | sin x | 151.59 ms | 154.87 ms | 1,334.80 ms | 1,338.30 ms | 1,693.35 ms | On-par with NumPy , 8.8× – 11.2× faster than JVM peers | | cos x | 170.91 ms | 151.35 ms | 1,077.31 ms | 1,542.43 ms | 1,979.36 ms | 6.3× – 11.6× faster than JVM peers | | exp x | 111.49 ms | 122.10 ms | 804.97 ms | 913.84 ms | 1,988.33 ms | 1.1× faster than NumPy , 7.2× – 17.8× faster than JVM peers | | log x | 121.47 ms | 164.22 ms | 844.08 ms | 1,062.01 ms | 1,243.39 ms | 1.35× faster than NumPy , 6.9× – 10.2× faster than JVM peers | | sigmoid x | 115.01 ms | 440.49 ms | 973.74 ms | 1,033.69 ms | 1,843.23 ms | 3.8× faster than NumPy , 8.5× – 16.0× faster than JVM peers | | tanh x | 301.43 ms | 197.38 ms | 1,493.26 ms | 1,602.51 ms | 3,130.68 ms | 5.0× – 10.4× faster than JVM peers | Tip Why JNum wins on transcendental ops: EJML and ojAlgo process elements through scalar Math. calls in a single-threaded loop. JNum utilizes vectorized minimax Chebyshev polynomials scheduled across AVX2/AVX-512 lanes, computing 8 or 16 transcendental operations per CPU clock cycle. | 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 | 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 | Add JNum to your project choose Java or Kotlin : For Java: <dependency <groupId io.github.ch-abhinav</groupId <artifactId jnum</artifactId <version 0.2.0</version </dependency For Kotlin bundles core + Kotlin extensions : <dependency <groupId io.github.ch-abhinav</groupId <artifactId jnum-kotlin</artifactId <version 0.2.0</version </dependency For Java: dependencies { implementation "io.github.ch-abhinav:jnum:0.2.0" } For Kotlin bundles core + Kotlin extensions : dependencies { implementation "io.github.ch-abhinav:jnum-kotlin:0.2.0" } Important Because JNum leverages Project Panama, compilation requires --add-modules jdk.incubator.vector , and runtime execution requires --add-modules jdk.incubator.vector --enable-native-access=ALL-UNNAMED . 👉 Read the Full Installation & Setup Guide https://github.com/CH-Abhinav/JNum/blob/main/docs/guide/01-getting-started.md for copy-paste Maven/Gradle compiler plugins and single-file runner configurations. JNum ships with a complete, progressive documentation suite structured like The Rust Book , paired with an advanced systems engineering deep dive: 1. Getting Started https://github.com/CH-Abhinav/JNum/blob/main/docs/guide/01-getting-started.md — JDK 25 prerequisites, Panama JVM flags, Maven & Gradle setup. 2. Tensors & Fundamentals https://github.com/CH-Abhinav/JNum/blob/main/docs/guide/02-tensors-and-fundamentals.md — Off-heap mental model, DTypes, and scoped Arena lifecycles. 3. Layouts, Strides, and Slicing https://github.com/CH-Abhinav/JNum/blob/main/docs/guide/03-layouts-strides-and-slicing.md — Strides, subviews, Slice tokens, and zero-copy views vs copies. 4. SIMD Arithmetic & Broadcasting https://github.com/CH-Abhinav/JNum/blob/main/docs/guide/04-simd-arithmetic-and-broadcasting.md — Vector lanes, broadcasting rules, and zero-GC output buffers. 5. Comparisons & Boolean Logic https://github.com/CH-Abhinav/JNum/blob/main/docs/guide/05-comparisons-and-boolean-logic.md — Clamping maximum / minimum , boolean masks, and truth reductions. 6. Transcendental & Unary Operations https://github.com/CH-Abhinav/JNum/blob/main/docs/guide/06-transcendental-and-unary-ops.md — Vectorized exp , log , sigmoid , and circular/hyperbolic trigonometry. 7. Linear Algebra & Decompositions https://github.com/CH-Abhinav/JNum/blob/main/docs/guide/07-linear-algebra-and-decompositions.md — 4-tier GEMM, matrix properties det , norm , solvers, and factorizations QR, SVD, Cholesky, Eig . 8. Descriptive & Inferential Statistics https://github.com/CH-Abhinav/JNum/blob/main/docs/guide/08-descriptive-and-inferential-stats.md — The Stats facade, moments, 5 quantile algorithms, and covariance. 9. Symbolic Expression Engine https://github.com/CH-Abhinav/JNum/blob/main/docs/guide/09-symbolic-expression-engine.md — Eliminating temporary arrays with JNum.eval and AST buffer reuse. 10. Tabular & NumPy I/O https://github.com/CH-Abhinav/JNum/blob/main/docs/guide/10-tabular-and-numpy-io.md — CSV/TSV with CsvOptions , .npy files with memory mapping mmap , and .npz archives. 11. Safetensors & Asynchronous I/O https://github.com/CH-Abhinav/JNum/blob/main/docs/guide/11-safetensors-and-async-io.md — Hugging Face checkpoints, scoped mmap MmapTensors , and async pipelines. 12. Neural Network Inference https://github.com/CH-Abhinav/JNum/blob/main/docs/guide/12-neural-network-inference.md — Module , Linear with sequence rank preservation, activations, and Sequential . 13. Idiomatic Kotlin DSL https://github.com/CH-Abhinav/JNum/blob/main/docs/guide/13-idiomatic-kotlin-dsl.md — First-class operator overloads + , , infix math matmul , dot , and range slicing. 14. Safety & Error Diagnostics https://github.com/CH-Abhinav/JNum/blob/main/docs/guide/14-safety-and-error-diagnostics.md — Memory safety, closed arena states, shape mismatches, and NaN / Inf diagnostics. - Off-Heap Memory & Stride Calculus https://github.com/CH-Abhinav/JNum/blob/main/docs/internals/01-memory-strides-and-ffm.md — Native MemorySegment mechanics, row-major stride proofs, and 64-byte cache alignment. - Pure Java GEMM & SIMD Micro-Kernels https://github.com/CH-Abhinav/JNum/blob/main/docs/internals/02-pure-java-gemm-and-simd.md — The 4-tier dispatch matrix, BLIS cache hierarchy, and register-blocked micro-panels $M R \times N R = 6 \times 2 \cdot VL$ . - Dynamic Hardware & Cache Probing https://github.com/CH-Abhinav/JNum/blob/main/docs/internals/03-dynamic-hardware-and-cache-probing.md — Probing Linux sysfs , macOS sysctl , and Windows WMI to derive analytical$K C, M C, N C$ cache tiles. - Zero-Copy I/O & Custom Parsers https://github.com/CH-Abhinav/JNum/blob/main/docs/internals/04-zero-copy-io-architecture.md — Virtual memory paging, safe deterministic unmapping, and zero-heap byte parsers. - AST Compilation & The Symbolic Engine https://github.com/CH-Abhinav/JNum/blob/main/docs/internals/05-eval-engine-ast-compilation.md — Shunting-Yard AST generation, hardware FMA optimization, and topological buffer recycling. - NumPy to JNum Rosetta Stone https://github.com/CH-Abhinav/JNum/blob/main/docs/reference/numpy-to-jnum-rosetta-stone.md — Side-by-side syntax comparison across NumPy Python , JNum Java 25 , and JNum Kotlin . - JVM & Project Panama Tuning Flags https://github.com/CH-Abhinav/JNum/blob/main/docs/reference/jvm-panama-tuning-flags.md — JVM flags, AVX-512/NEON verification, and overriding cache heuristics. - JDK 25 or later Temurin, OpenJDK, or Zulu - Architectures supported: x86 64 SSE4.2, AVX, AVX2, AVX-512 and AArch64 / ARM64 NEON, SVE . This project is licensed under the Apache License 2.0. See the LICENSE https://github.com/CH-Abhinav/JNum/blob/main/LICENSE file for details.