FastSIMD 0.1.3 [ALPHA-2026-08] — Hardware Vectorization Framework (AVX2, AVX-512, NEON)

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⚡ High-performance hardware vector acceleration engine (AVX2, AVX-512, NEON) for the JVM.

FastSIMD provides a unified SIMD abstraction layer for Java. It encapsulates 32-byte parallel delimiter matching, bulk memory transfers, vector math, and pixel format conversions using hardware intrinsics.

Watch Demo (YouTube) | Watch JMH Benchmark (YouTube)

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Quick Start

import fastsimd.*;
import fastmemory.Memory;
import fastpointer.Pointer;

public class Demo {
    public static void main(String[] args) {
        // 1. Allocate 32-byte aligned memory via FastMemory
        try (Memory memory = Memory.allocateAligned(1024, 32)) {
            Pointer ptr = memory.pointer();

            // Write 32 bytes of test string
            byte[] bytes = "Hello World! FastSIMD AVX2 Test".getBytes();
            for (int i = 0; i < bytes.length; i++) {
                ptr.setByte(i, bytes[i]);
            }

            // 2. Perform 32-byte SIMD parallel search for 'F'
            int index = SIMD.findByte(ptr, bytes.length, (byte) 'F');
            System.out.println("SIMD parallel found 'F' at index: " + index);
        }
    }
}

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Table of Contents

• Why FastSIMD?
• Quick Start
• Key Features
• Real-World Use Cases
• Performance Benchmarks
• API Quick Reference
• Technical Demos & Benchmarks
• Installation
• Documentation
• Platform Support
• License

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Why FastSIMD?

Standard Java loops process arrays sequentially byte-by-byte or rely on unpredictable JIT auto-vectorization that often fails on complex branching. FastSIMD provides:

• Explicit 256-Bit AVX2 Vector Acceleration — Process 32 bytes or 8 single-precision floats per CPU cycle using native SIMD intrinsics (_mm256_cmpeq_epi8, _mm256_movemask_epi8).
• 28+ GB/sec Scan Bandwidth — Execute ultra-fast log scanning, string searching, and delimiter matching at pure CPU memory bus speeds (15x faster than standard Java loops).
• Zero-Allocation Hardware Intrinsics — Perform bulk memory transfers, vector math, and byte sweeps directly on off-heap native memory pointers without triggering Garbage Collector pauses.

FastSIMD bypasses compiler guesswork by emitting deterministic hardware vector instructions directly via JNI:

Feature	Standard Java Loop	JDK Vector API (Incubator)	FastSIMD
Instruction Execution	Scalar (1 byte / cycle)	JIT-compiled IR vectors	Explicit Hardware Intrinsics
Vector Width	None (1 byte)	128 / 256 / 512 bit	256-Bit AVX2 (32 Bytes / Cycle)
Scan Bandwidth	~1.8 GB/sec	~12–18 GB/sec	> 28 GB/sec (Bus Speed)
Branching Resilience	Scalar fallback	JIT bails on branch	Branchless Vector Masking
JVM Configuration	Default Java runtime	Requires --add-modules	0 JVM Flags (Pure Native DLL)
Dependencies	JDK standard lib	JVM Incubator module	Pure Java 17+ backed by FastCore

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Key Features

• 🔍 SIMD Scan: 32-byte parallel delimiter & byte searching (_mm256_cmpeq_epi8, _mm256_movemask_epi8).
• ⚡ Bulk Memory & Prefetching: 256-bit unaligned/aligned loads/stores and cache prefetching.
• 🔢 Vector Math: 8-way Float32 and 4-way Float64 SIMD arithmetic for 3D and matrix operations.
• 🎨 Pixel & Convert: Color space conversion (RGBA <-> BGRA) and Float↔Int transformations.

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Real-World Use Cases

• ⚡ High-Frequency Trading (HFT): Scan FIX protocol streams and order book market ticks at 28+ GB/sec without GC pauses.
• 🔍 SIEM & Security Log Mining: Search multi-gigabyte server logs for threat patterns (CRITICAL_ALERT, JWT_EXPIRED) in milliseconds.
• 🤖 AI Tensor Data Pipelines: Perform zero-copy vector normalization and memory alignment for local GGUF LLM models.

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Performance Benchmarks

FastSIMD is engineered for ultra-high-throughput vector acceleration. In the official JMH Benchmark, the system measured native AVX2 256-bit vector search vs standard scalar loops over a 64 MB memory buffer:

Benchmark                                    Mode  Cnt      Score   Error  Units
JMH_SIMD.benchmarkAVX2FindByte               thrpt    2  19632.158          ops/s

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API Quick Reference

Method	Description	Docs
SIMD.findByte(ptr, length, target)	Scans memory 32 bytes per cycle for target byte (AVX2).	Reference
SIMD.copy(src, dst, bytes)	High-throughput 256-bit SIMD bulk memory copy.	Reference
SIMD.isAVX2Supported()	Returns true if CPU AVX2 vector extensions are supported.	Reference

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Technical Demos & Benchmarks

Case	Java Example	Launcher	Description
AVX2 SIMD Parallel Search	Demo.java	run-demo.bat	End-to-end 100 MB streaming comparison matching delimiters via 256-bit AVX2 vector instructions vs scalar Java loops.
JMH Microbenchmark Suite	Benchmark.java	run-benchmark.bat	OpenJDK JMH throughput & latency test suite for SIMD byte scanning and vectorized off-heap memory copies.

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Installation

Option 1: Maven (Recommended)

Add the JitPack repository and the dependencies to your pom.xml:

<repositories>
    <repository>
        <id>jitpack.io</id>
        <url>https://jitpack.io</url>
    </repository>
</repositories>

<dependencies>
    <!-- FastSIMD Hardware Vector Engine -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastSIMD</artifactId>
        <version>0.1.3</version>
    </dependency>

    <!-- FastMemory Aligned Allocator -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastMemory</artifactId>
        <version>0.1.1</version>
    </dependency>

    <!-- FastPointer Address Wrapper -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastPointer</artifactId>
        <version>0.1.1</version>
    </dependency>

    <!-- FastCore Native Loader -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastCore</artifactId>
        <version>0.1.0</version>
    </dependency>
</dependencies>

Option 2: Gradle (via JitPack)

repositories {
    maven { url 'https://jitpack.io' }
}

dependencies {
    implementation 'com.github.andrestubbe:FastSIMD:0.1.3'
    implementation 'com.github.andrestubbe:FastMemory:0.1.1'
    implementation 'com.github.andrestubbe:FastPointer:0.1.1'
    implementation 'com.github.andrestubbe:FastCore:0.1.0'
}

Option 3: Direct Download (No Build Tool)

Download the latest JARs directly to add them to your classpath:

1. ⚡ FastSIMD-0.1.3.jar (Hardware Vector Engine)
2. 💾 FastMemory-0.1.1.jar (32-Byte Aligned Allocator)
3. 📍 FastPointer-0.1.1.jar (Native Primitive Pointer)
4. ⚙️ fastcore-0.1.0.jar (Mandatory Native Loader)

Important

All JARs must be in your classpath for the JNI calls to function correctly.

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Documentation

• CHANGELOG.md: Release notes.
• COMPILE.md: Full compilation guide (MSVC C++17 build chain + JNI Setup).
• REFERENCE.md: Full API descriptions, border configurations, and codepoint index.
• PHILOSOPHY.md: The engineering rationale for zero-allocation performance.
• ROADMAP.md: Future milestones and planned features.

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Platform Support

Platform	Status
Windows 10/11 (AVX2 / x64)	✅ Fully Supported
Linux (AVX2 / x64)	🚧 Planned
macOS (Apple Silicon NEON)	🚧 Planned

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Related Projects

• FastBytes — High-performance SIMD-powered byte engine
• FastMemory — SIMD 32-byte aligned off-heap memory allocation and page locking
• FastPointer — Zero-overhead native address arithmetic
• FastCore — Native JNI loader for FastJava libraries

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License

MIT License — See LICENSE for details.

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Part of the FastJava Ecosystem — Making the JVM faster. Small package. Maximum speed. Zero bloat. 🚀📋