# CPU and Memory Usage

# Core Engine Components

# Engine CPU Utilization

The Nekuti Matching Engine is designed for high-performance, low-latency trading operations. Here are the key points regarding CPU usage:

# Production Mode Characteristics

• Utilizes 4 CPU cores at 100% capacity
• Employs spin-waiting on I/O operations to minimize latency
• Optimized for core trading operations:
  • Network reads/writes to gateways
  • Command persistence
  • Command execution

# Performance Considerations

• CPU cache can become a bottleneck under high load
• Recommended hardware: Modern x86 processor with large CPU cache
• Performance can be further optimized for specific use cases and traffic patterns
• Contact our team for assistance with custom optimization

# Engine Memory Usage

Memory consumption varies significantly based on several factors:

# Baseline Requirements

• Minimal configuration (no instruments, no order flow): ~200 MB RAM
• Production environment: Variable, potentially several gigabytes

# Scaling Factors

Memory usage scales with:

• Number of active instruments
• Market structure complexity
• Trading volume
• Order repository purge frequency

# Real-world Example

A typical production deployment processing:

• 10 million orders per day
• 10 instruments
• Consumes approximately 4 GB RAM

# Snapshotter Resource Usage

The snapshotter component is responsible for state persistence and operates with the following characteristics:

# Processing Model

• Single-threaded execution
• Periodic command file processing
• Dormant between snapshot generations

# Resource Consumption

• Memory footprint: Equivalent to the engine (~200 MB minimum)
• CPU: Single thread utilization during active snapshots
• Idle resource consumption is minimal during dormant periods

# Edge Components

# Gateway Resource Requirements

The Control Gateway and User Gateway serve as edge nodes providing REST and WebSocket APIs through Swagger and Glassfish Grizzly.

# Memory Usage

• Minimal deployment: 1 GB RAM per gateway
• High-traffic deployment: 4-8 GB RAM per gateway
• User Gateway may require additional memory for handling hostile internet traffic

# Scaling Considerations

Gateway resource consumption scales with:

• Number of concurrent connections
• Request frequency
• Payload sizes
• Authentication and validation overhead

# Performance Optimization Tips

For optimal performance, consider these factors:

• Monitor CPU, I/O and Memory pressure
• Adjusting order repository purge frequency based on memory constraints
• Hardware selection focusing on CPU cache size
• Performance profiling under expected load conditions
• Gateway memory allocation based on expected traffic patterns