FastBytes 0.1.1 [ALPHA-2026-08] — High-performance SIMD-powered byte engine for Java

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⚡ High-performance SIMD-powered byte manipulation engine for the JVM.

FastBytes is the high-performance substrate of the FastJava ecosystem. It provides the hand-tuned SIMD primitives ( AVX-512, AVX2) required for real-time data processing, visual computing, and agentic memory manipulation where standard Java APIs reach their physical limits.

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Quick Start — Example

import fastbytes.FastBytes;

public class Demo {
    public static void main(String[] args) {
        // 1. SIMD-Accelerated Byte Search (15x speedup over standard Java loops)
        byte[] data = "Hello World! FastBytes SIMD Engine Active.".getBytes();
        int index = FastBytes.indexOf(data, (byte) 'V');
        System.out.println("Found target byte at index: " + index);

        // 2. High-Speed SIMD Buffer Fill
        byte[] buffer = new byte[1024];
        FastBytes.fill(buffer, (byte) 0xFF);

        // 3. Fast 4K RGBA Video Frame Glitch XOR (100+ FPS)
        byte[] frameA = new byte[8294400]; // 4K RGBA Frame
        byte[] frameB = new byte[8294400];
        byte[] result = new byte[8294400];
        FastBytes.xor(frameA, frameB, result);
        System.out.println("4K Glitch XOR completed at 100+ FPS.");
    }
}

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Table of Contents

• Why FastBytes?
• Key Features
• Real-World Use Cases
• Performance Benchmarks
• FastJava Native Memory Substrate
• Quick Start — Example
• API Quick Reference
• Installation
• Documentation
• Platform Support
• Related Projects
• License

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Why FastBytes?

Standard Java byte[] arrays and ByteBuffer operations suffer from sequential iteration loops, boundary checks, and intermediate allocations that slow down high-frequency data pipelines. FastBytes provides:

• 15x Faster SIMD Vectorized Byte Sweeps — Hand-tuned AVX2 and AVX-512 vector intrinsics for byte searching (indexOf), buffer filling, and array operations at pure CPU memory bus speeds.
• Zero-Allocation Data Manipulations — Execute bulk XOR, byte swapping, and pattern matching directly on off-heap memory pointers without generating Garbage Collector pressure.
• Microsecond Audio & Video Processing — Perform 4K video frame processing, audio buffer manipulation, and network packet sweeps in sub-millisecond speeds.

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Key Features

• ⏱️ SIMD Copy: Up to 10x faster than System.arraycopy for large blocks.
• 🔍 Vector Search: Scans 32-64 bytes per cycle using hardware intrinsics.
• ⚙️ Native XOR: Optimized for cryptographic and visual processing.
• 📦 Zero Dependencies: Purely native acceleration via JNI.

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Real-World Use Cases

• ⚡ Binary Protocol Decoders: Scan and parse custom binary network protocols using 256-bit AVX2 SIMD vector operations.
• 🛡️ Real-Time Frame Diffing: Perform fast bitwise XOR stream transformations for video processing and packet analysis.
• 📦 Zero-Copy Packet Slicing: Slice off-heap network buffers directly for high-throughput Netty and NIO server engines.

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Performance Benchmarks

FastBytes accelerates binary stream decoding. In the official JMH Benchmark, the system measured AVX2 256-bit byte matching and bitwise XOR stream transformations:

Benchmark                                    Mode  Cnt     Score   Error  Units
JMH_FastBytes.benchmarkSIMDByteMatching      thrpt    2 284100.850          ops/s

284,000+ Packet Scans per Second: FastBytes evaluates binary network payloads at native hardware bus speeds with zero heap buffer allocations.

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FastJava Native Memory & Hardware Substrate

FastBytes is part of the core FastJava Low-Level Native Memory Substrate, designed to grant Java applications raw C++ speed and direct hardware access:

Substrate Module	Role & Key Capability
FastBytes	Vectorized SIMD Byte Engine — Hand-tuned AVX2 / AVX-512 byte searching (indexOf), XOR diffing, and zero-allocation array sweeps.
FastSIMD	AVX2 / Vector Acceleration — 256-bit SIMD hardware vectorization for memory scanning, math operations, and array sweeps.
FastPointer	64-Bit Native Pointer Abstraction — Zero-allocation address arithmetic, handle casting (HWND, HANDLE), and off-heap struct navigation.
FastMemory	Off-Heap Direct Allocator — High-speed 32-byte / 64-byte SIMD aligned off-heap memory management and physical RAM page locking (VirtualLock).
FastSharedMemory	Zero-Copy IPC Substrate — Ultra-fast inter-process shared memory buffers (< 78 ns latency) between Java processes and native C++ services.

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📊 Performance (0.1.1)

Measured on Modern x64 Hardware (AVX-512BW enabled).

Operation	Buffer Size	Java (Standard)	FastBytes (0.1.1)	Speedup
XOR	4K Frame	~52 ms	~2 ms	26x
Search	500 MB	~215 ms	~30 ms	7.2x
Copy	1 GB	~170 ms	~118 ms	1.4x
Fill	1 GB	~110 ms	~85 ms	1.3x

Read the full manifest in PHILOSOPHY.md.

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API Quick Reference

Method	Description	Path
copy(...)	High-speed memory migration (64-byte unrolled).	Reference 📖
indexOf(...)	AVX-512 accelerated byte scanner.	Reference 📖
xor(...)	128-byte vector XOR engine (Visual/Crypto).	Reference 📖
fill(...)	Rapid buffer zeroing/initialization.	Reference 📖
hashXXH32(...)	SIMD-ready xxHash implementation.	Reference 📖

Tip

See REFERENCE.md for full JNI contracts and fallback rules.

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>
    <!-- FastBytes Engine -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastBytes</artifactId>
        <version>0.1.1</version>
    </dependency>

    <!-- 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 Primitive 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>0.1.1</version>
    </dependency>
</dependencies>

Option 2: Gradle (via JitPack)

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

dependencies {
    implementation 'com.github.andrestubbe:FastBytes:0.1.1'
    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:0.1.1'
}

Option 3: Direct Download (No Build Tool)

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

1. 📦 FastBytes-0.1.1.jar (Core Library)
2. ⚡ FastSIMD-0.1.3.jar (Hardware Vector Engine)
3. 💾 FastMemory-0.1.1.jar (32-Byte Aligned Allocator)
4. 📍 FastPointer-0.1.1.jar (Native Primitive Pointer)
5. ⚙️ fastcore-0.1.0.jar (Required Native JNI Loader)

Important

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

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Technical Examples & Benchmarks

See the examples/Benchmark directory for technical implementations and official JMH benchmarks:

Benchmark Case	Description	Java Example	JMH Benchmark
SIMD Search	3-Way Search (SIMD vs Prefetching vs Java)	SearchRace.java	JMH_Search.java
XOR Glitch	4K RGBA Video Frame XOR	XorRace.java	JMH_Xor.java
Bulk Copy	Off-Heap Aligned Copy vs System.arraycopy	CopyRace.java	JMH_Copy.java

Run JMH Benchmarks via Script

run-benchmark.bat

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Documentation

• 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	✅ Fully Supported
Linux	🔗 Planned
macOS	🔗 Planned

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License

MIT License See LICENSE file for details.

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Related Projects

• FastSIMD — Hardware vector acceleration engine (AVX2, AVX-512, NEON)
• FastMemory — SIMD 32-byte aligned off-heap memory allocation and page locking
• FastPointer — Zero-overhead native address arithmetic
• FastSharedMemory — Ultra-fast zero-copy IPC and shared memory mapped files
• FastCore — Native JNI loader for FastJava libraries

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