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SELF Chain Advanced TPS Optimization

🎯 Performance Targets: The metrics described in this document represent our performance optimization targets and architectural design goals. Actual performance may vary based on network conditions, hardware specifications, and implementation progress.

Overview​

This document outlines the advanced optimizations and benchmarking capabilities of SELF Chain, designed to target Solana-level performance (50,000+ TPS).

Core Optimizations​

1. Advanced Sharding​

  • Geographic-based sharding
  • Dynamic load balancing
  • Network latency optimization
  • Parallel validation
  • Cross-shard optimization

2. Hardware Acceleration​

  • GPU acceleration
  • SIMD (AVX/SSE) optimization
  • Cache optimization
  • Batch processing
  • Memory efficiency

3. Performance Monitoring​

  • Real-time TPS tracking
  • Latency measurement
  • Resource utilization
  • Network monitoring
  • Alert system

4. Benchmarking Suite​

  • Multiple load patterns
  • Performance metrics
  • Resource utilization
  • Validation time
  • Network bandwidth

Implementation Details​

Advanced Sharding​

struct ShardingManager {
    config: ShardingConfig,
    shards: Arc<RwLock<Vec<Shard>>>,
    rebalance_interval: tokio::time::Interval,
}

Benchmarking​

struct BenchmarkSuite {
    config: BenchmarkConfig,
    metrics: Arc<RwLock<BenchmarkMetrics>>,
    grid_compute: Arc<GridCompute>,
    performance_monitor: Arc<PerformanceMonitor>,
}

Performance Targets​

  • Target TPS: 50,000+ transactions per second (design goal)
  • Peak TPS Target: 100,000+ transactions per second (theoretical maximum)
  • Target Average Latency: < 1ms (under optimal conditions)
  • Target Network Latency: < 10ms (datacenter environments)
  • Memory Usage: Optimization in progress
  • Target CPU Utilization: < 90% (at full load)
  • Target GPU Utilization: < 90% (when GPU acceleration enabled)

Benchmarking Scenarios​

  1. Constant Load
  2. Ramp-Up Load
  3. Spike Load
  4. Random Load

Optimization Strategy​

  1. Sharding:

    • Geographic-based distribution
    • Dynamic load balancing
    • Network latency optimization
    • Resource utilization

  2. Hardware:

    • GPU acceleration
    • SIMD optimization
    • Cache efficiency
    • Batch processing

  3. Network:

    • Gossipsub optimization
    • Batch messaging
    • Network latency
    • Resource utilization

  4. Validation:

    • Parallel processing
    • Batch validation
    • Cache optimization
    • Resource utilization

Security Considerations​

  • Secure sharding
  • Validation integrity
  • Network security
  • Resource isolation
  • Attack prevention

Testing and Verification​

  • Comprehensive benchmarking
  • Load testing
  • Stress testing
  • Performance monitoring
  • Security testing