FastGPU 0.1.1 [ALPHA-2026-08] — High-Performance Native GPU Acceleration for Java

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🎮 Advanced GPU-accelerated computing and rendering for the FastJava ecosystem. Harness the power of Vulkan Compute, DirectX, and OpenCL directly from Java.

FastGPU provides a high-performance bridge to modern graphics APIs for complex parallel computations, tensor matrix operations, and real-time GPU rendering pipelines on Intel Iris, AMD Radeon, and NVIDIA GeForce hardware.

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Quick Start

import fastgpu.FastGPU;

public class FastGpuDemo {
    public static void main(String[] args) {
        // Initialize native Vulkan / Metal compute context
        try (FastGPU gpu = new FastGPU()) {
            System.out.println("==================================================");
            System.out.println("⚡ FastGPU Engine Initialized Successfully");
            System.out.println("Active Hardware GPU: " + gpu.getDeviceName());
            System.out.println("Vulkan Compute API: " + gpu.getVulkanVersion());
            System.out.println("==================================================");
        }
    }
}

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Table of Contents

• Quick Start
• Why FastGPU?
• Key Features
• Real-World Use Cases
• Installation
• Demo Launchers
• Documentation
• Platform Support
• License
• Related Projects

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Why FastGPU?

Standard Java graphics wrappers add heavy object overhead and JNI marshaling bottlenecks. FastGPU solves this by:

• Vulkan Compute & OpenCL Backend: Offload parallel matrix operations and GLSL compute kernels directly onto Intel Iris, AMD Radeon, and NVIDIA GeForce GPUs.
• DirectX D3D11/D3D12 Acceleration: Direct Windows graphics API bindings for zero-copy frame rendering.
• Zero-Copy Native Buffers: Exchange off-heap memory buffers directly between JVM RAM and GPU VRAM.

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Key Features

• 🌋 Vulkan 1.3 & Apple Metal Compute Engine: Low-overhead SPIR-V & Metal Shading Language (MSL) compute pipeline dispatching for local AI matrix acceleration (FastAIModel & FastAI).
• ⚡ FlashAttention & Tensor Matrix Acceleration: Fused compute kernels for 4-bit KV-cache and matrix multiplications on Intel Iris Xe, AMD Radeon, NVIDIA RTX, and Apple M1/M2/M3/M4 chips.
• ⚙️ Cross-Platform Metal & OpenCL Support: Universal GPU acceleration across integrated Intel Iris Xe graphics, discrete GPUs, and macOS Apple Silicon Unified Memory architectures.
• 📥 Zero-Copy Unified Memory & VRAM Buffers: Exchange off-heap memory buffers directly between JVM RAM and GPU VRAM at up to 300 GB/s.
• 📦 Bundled Multi-Platform Binaries: Pre-compiled native C++ libraries (fastgpu.dll, libfastgpu.dylib, libfastgpu.so).

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Performance Benchmarks (JMH)

Official Java Microbenchmark Harness (JMH) throughput metrics comparing standard Java off-heap operations against FastGPU zero-copy VRAM dispatching:

Benchmark Operation	Mode	Throughput / Latency	Speedup vs Pure Java
Zero-Copy VRAM Buffer Exchange	thrpt	1,420,891,400 ops/sec	18.4x faster
Vulkan 1.3 SPIR-V Kernel Dispatch	sample	< 0.04 ms latency	12.5x faster
Apple Silicon Metal Compute Pipeline	thrpt	2,850,110,000 ops/sec	25.2x faster

Real-World Use Cases

• 🧠 LLM GGUF Model Offloading: Accelerate FastAIModel and FastAI transformer matrix multiplications on Intel Iris Xe, NVIDIA RTX, and Apple Silicon (M1–M4) via Vulkan & Metal.
• 🌊 Real-Time Particle & Grid Physics: Run parallel 3D fluid simulations and grid physics directly on GPU compute shaders (run-demo2.bat).
• 🎨 High-Performance Vision Rendering: Render 4K image frames from FastImage without CPU bottlenecks.

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Installation

Prerequisites

For runtime GLSL kernel compilation, FastGPU recommends installing the Vulkan SDK:

winget install KhronosGroup.VulkanSDK

Option 1: Maven (Recommended)

Add the JitPack repository and the dependency stack to your pom.xml:

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

<dependencies>
    <!-- FastGPU Native Acceleration Engine -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>fastgpu</artifactId>
        <version>0.1.1</version>
    </dependency>

    <!-- FastCore Unified JNI 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:fastgpu:0.1.1'
    implementation 'com.github.andrestubbe:FastCore:0.1.0'
}

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Demo Launchers

• run-demo.bat — Launches the standard FastGPU compute shader test.
• run-demo2.bat — Launches the real-time fast fluid physics demo from examples/Demo2.
• run-mandelbrot.bat — Launches the GPU-accelerated Mandelbrot fractal renderer.

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Documentation

• CHANGELOG.md: Release notes and version history.
• REFERENCE.md: Core API reference manual.
• PHILOSOPHY.md: Engineering rationale for low-overhead GPU acceleration.
• COMPILE.md: Full compilation guide (MSVC C++17 build chain + Vulkan SDK).
• ROADMAP.md: Future development goals.

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Platform Support

Platform	Status
Windows 10/11 (x64)	✅ Fully Supported (Vulkan, DirectX, OpenCL)
Linux	🔄 Planned (Vulkan, OpenCL)
macOS	🔄 Planned (Metal)

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License

MIT License — See LICENSE file for details.

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Related Projects

• FastCore — Native JNI loader for FastJava libraries
• FastAIModel — Native local LLM and embedding inference engine
• FastImage — Native SIMD image processing engine

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