GPU Support Guide

Overview

Logly provides optional GPU/CUDA acceleration for high-throughput logging scenarios.

Installation

[dependencies]
logly = { version = "0.1.7", features = ["gpu"] }

Requirements:

• CUDA Toolkit installed
• NVIDIA GPU with CUDA support
• Compile with --features gpu

Basic Usage

use logly::prelude::*;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let logger = Logger::new();
    
    let mut config = LoggerConfig::default();
    config.enable_gpu = true;
    config.gpu_buffer_size = 2 * 1024 * 1024;  // 2MB
    
    logger.configure(config);
    logger.enable_gpu()?;
    
    // Check GPU status
    println!("{}", logger.gpu_info());
    
    // Logs are now accelerated by GPU
    for i in 0..10000 {
        logger.info(format!("Message {}", i))?;
    }
    
    Ok(())
}

Configuration

#![allow(unused)]
fn main() {
let mut config = LoggerConfig::default();
config.enable_gpu = true;
config.gpu_buffer_size = 2 * 1024 * 1024;  // 2MB buffer
logger.configure(config);
}

GPU Operations

Enable GPU

#![allow(unused)]
fn main() {
logger.enable_gpu()?;
}

Disable GPU

#![allow(unused)]
fn main() {
logger.disable_gpu();
}

Check Status

#![allow(unused)]
fn main() {
let info = logger.gpu_info();
println!("{}", info);
}

Graceful Fallback

If GPU is unavailable:

• Logs warning message
• Falls back to CPU logging
• No errors or crashes
• Application continues normally

#![allow(unused)]
fn main() {
match logger.enable_gpu() {
    Ok(_) => println!("GPU enabled"),
    Err(e) => println!("GPU unavailable: {}, using CPU", e),
}
}

Performance

GPU acceleration is beneficial for:

• High-throughput logging (>10,000 logs/sec)
• Real-time systems
• Data-intensive applications
• Parallel processing workloads

Limitations

• Experimental feature
• Requires CUDA toolkit
• NVIDIA GPUs only
• Additional memory overhead
• Not needed for typical applications

Example Output

GPU Logging: Enabled
Device: CUDA Device 0
Buffer Size: 2097152 bytes
Status: Active

Troubleshooting

GPU Not Available

Error: CUDA device not available

Solution: Install CUDA toolkit and ensure GPU is detected

Compilation Error

Error: cudarc not found

Solution: Compile with --features gpu

Performance Not Improved

Solution: GPU acceleration helps with high-volume logging. For typical applications, CPU logging is sufficient.

Best Practices

1. Use for high-throughput: Only enable for >10,000 logs/sec
2. Monitor memory: GPU buffer uses device memory
3. Test fallback: Ensure application works without GPU
4. Profile first: Measure if GPU actually improves performance