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SELF Chain Advanced TPS Optimization

Performance Targets

The metrics described in this document represent theoretical performance optimization targets and architectural design goals, not achieved performance. These are aspirational targets based on our planned architecture.

Current Reality: Testnet achieves ~1,000 TPS in controlled environments. The 50,000+ TPS target requires all optimizations described here to be fully implemented and tested.

Overview​

This document outlines the planned advanced optimizations and benchmarking capabilities for SELF Chain, with a long-term goal of achieving high-performance transaction processing.

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 (Aspirational)​

  • Target TPS: 50,000+ transactions per second (long-term design goal, not yet achieved)
  • Peak TPS Target: 100,000+ transactions per second (theoretical maximum requiring all optimizations)
  • 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