https://github.com/xdagger/xdagj-native-randomx
Java Native Randomx Implement
https://github.com/xdagger/xdagj-native-randomx
java jna jni randomx xdag xdagj
Last synced: 5 months ago
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Java Native Randomx Implement
- Host: GitHub
- URL: https://github.com/xdagger/xdagj-native-randomx
- Owner: XDagger
- License: mit
- Created: 2022-11-15T01:55:01.000Z (almost 3 years ago)
- Default Branch: master
- Last Pushed: 2025-02-25T09:01:36.000Z (7 months ago)
- Last Synced: 2025-04-05T19:34:22.090Z (6 months ago)
- Topics: java, jna, jni, randomx, xdag, xdagj
- Language: Java
- Homepage:
- Size: 2.55 MB
- Stars: 5
- Watchers: 3
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Welcome to xdagj-native-randomx
---
## Introduction
`xdagj-native-randomx` is a Java implementation of the RandomX proof-of-work algorithm using JNA (Java Native Access). This library is a community-driven project based on the [xdagj](https://github.com/XDagger/xdagj) ecosystem.
### **What is RandomX?**
RandomX is a proof-of-work (PoW) algorithm optimized for general-purpose CPUs. It uses random code execution and memory-hard techniques to minimize the efficiency advantage of specialized hardware like ASICs.
For more details, visit the [RandomX GitHub repository](https://github.com/tevador/RandomX).
---
## Features
- **Native Integration**: Leverages RandomX's native C++ library via JNA.
- **Cross-Platform Support**: Works on Linux, macOS (x86_64 and aarch64), and Windows.
- **Easy Integration**: Available as a Maven dependency for seamless use in Java projects.
---
## Donation Address
Support the project with XDAG donations:
**XDAG Address**: `BkcVG4i1BfxUJLdNPdctaCReBoyn4j32d`
---
## Installation
### **1. Requirements**
- **JDK**: v17 or later
- **Maven**: v3.9.9 or later
### **2. Build Steps**
#### **2.1 Clone the Repository**
```bash
git clone https://github.com/XDagger/xdagj-native-randomx.git
cd xdagj-native-randomx
git submodule init
git submodule update
```
#### **2.2 Compile RandomX Native Library**
Compile and copy the appropriate shared library for your platform:
##### **Linux x86_64**
```bash
cd randomx
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DARCH=native -DBUILD_SHARED_LIBS=ON -DCMAKE_C_FLAGS="-fPIC"
make -j4
cp -i librandomx.so ../../src/main/resources/native/librandomx_linux_x86_64.so
```
##### **macOS x86_64**
```bash
cd randomx
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DARCH=native -DBUILD_SHARED_LIBS=ON
make -j4
cp -i librandomx.dylib ../../src/main/resources/native/librandomx_macos_x86_64.dylib
```
##### **macOS aarch64**
```bash
cd randomx
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DARCH=native -DBUILD_SHARED_LIBS=ON
make -j4
cp -i librandomx.dylib ../../src/main/resources/native/librandomx_macos_aarch64.dylib
```
##### **Windows x86_64**
```bash
cd randomx
mkdir build && cd build
cmake .. -G "MinGW Makefiles" -DCMAKE_BUILD_TYPE=Release -DARCH=native -DBUILD_SHARED_LIBS=ON
make -j4
cp -i randomx.dll ../../src/main/resources/native/librandomx_windows_x86_64.dll
```
#### **2.3 Compile Java Library**
```bash
cd ../../
mvn clean package
```
### **3. Add Maven Dependency**
To include `xdagj-native-randomx` in your project, add the following dependency to your `pom.xml`:
```xml
io.xdag
xdagj-native-randomx
0.2.0
```
---
## Usage Example
```java
package io.xdag.crypto.randomx;
import java.nio.charset.StandardCharsets;
import java.util.HexFormat;
import java.util.Set;
public class Example {
public static void main(String[] args) {
// Key to be hashed
String key = "hello xdagj-native-randomx";
byte[] keyBytes = key.getBytes(StandardCharsets.UTF_8);
// Get supported RandomX flags for the current CPU
Set flags = RandomXUtils.getFlagsSet();
System.out.println("Supported flags: " + flags);
int combinedFlags = RandomXFlag.toValue(flags);
System.out.println("Combined flags value: " + combinedFlags);
// Initialize RandomX cache with the supported flags
RandomXCache cache = new RandomXCache(flags);
cache.init(keyBytes);
// Create and configure RandomXTemplate using builder pattern
byte[] hash;
try (RandomXTemplate template = RandomXTemplate.builder()
.cache(cache)
.miningMode(false) // Set to false for normal hashing mode
.flags(flags)
.build()) {
// Initialize the template with the configured settings
template.init();
// Calculate hash of the input key
hash = template.calculateHash(keyBytes);
}
// Format and display the results
HexFormat hex = HexFormat.of();
System.out.printf("Message: %s%n", key);
System.out.printf("Hash: %s%n", hex.formatHex(hash));
}
}
```
---
## Benchmark Results
### Linux System Configuration
- **OS**: Linux 5.4.119
- **CPU**: AMD EPYC 9754 (16 cores)
- **RAM**: 32 GB
- **thread**: 8
- **RandomX Flags**: [DEFAULT, HARD_AES, JIT, ARGON2_SSSE3, ARGON2_AVX2, ARGON2]
### Linux Performance Results
| Benchmark | Mode | Cnt | Score | Error | Units |
|:------------------------------:|:-----:|:---:|:-------:|:------:|:-----:|
| RandomXBenchmark.lightBatch | thrpt | | 328.736 | | ops/s |
| RandomXBenchmark.lightNoBatch | thrpt | | 325.383 | | ops/s |
| RandomXBenchmark.miningBatch | thrpt | | 2777.939 | | ops/s |
| RandomXBenchmark.miningNoBatch | thrpt | | 2817.811 | | ops/s |
---
### MacOS System Configuration
- **OS**: macOS 15.1.1
- **CPU**: Apple M3 Pro
- **RAM**: 36 GB
- **thread**: 8
- **RandomX Flags**: [DEFAULT, HARD_AES, SECURE]
JIT flag will cause jvm to crash in MacOS
### MacOS Performance Results
| Benchmark | Mode | Cnt | Score | Error | Units |
|:------------------------------:|:-----:|:---:|:-------:|:------:|:-----:|
| RandomXBenchmark.lightBatch | thrpt | | 32.864 | | ops/s |
| RandomXBenchmark.lightNoBatch | thrpt | | 33.683 | | ops/s |
| RandomXBenchmark.miningBatch | thrpt | | 554.966 | | ops/s |
| RandomXBenchmark.miningNoBatch | thrpt | | 570.060 | | ops/s |
---
## Contribution
We welcome contributions to improve the project! Please feel free to submit issues or pull requests.
For discussions or questions, contact the [xdagj community](https://github.com/XDagger/xdagj).
---