Performance Optimization¶

Overview¶

AIDDDMAP implements various performance optimization strategies to handle large datasets, complex computations, and real-time data processing efficiently. This guide covers the key optimization features and best practices for maintaining high performance across the platform.

Data Processing Optimizations¶

Chunked Processing¶

const CHUNK_SIZE = 100;
const MAX_DISPLAY_POINTS = 1000;

// Example of chunked data processing
async function processDataChunks(data: FitnessDataPoint[]) {
  const chunks = [];
  for (let i = 0; i < data.length; i += CHUNK_SIZE) {
    chunks.push(data.slice(i, i + CHUNK_SIZE));
  }

  const results = [];
  for (const chunk of chunks) {
    const processed = await processChunk(chunk);
    results.push(...processed);
  }

  return results;
}

Features:

Configurable chunk sizes
Memory usage optimization
Progress tracking
Error recovery per chunk
Performance monitoring

Batch Operations¶

interface BatchConfig {
  maxBatchSize: number;
  concurrency: number;
  retryAttempts: number;
  timeout: number;
}

// Example of batch processing
async function processBatch(items: any[], config: BatchConfig) {
  const batches = [];
  for (let i = 0; i < items.length; i += config.maxBatchSize) {
    batches.push(items.slice(i, i + config.maxBatchSize));
  }

  return Promise.all(
    batches.map((batch) => processBatchWithRetry(batch, config)),
  );
}

Features:

Parallel processing
Configurable batch sizes
Automatic retries
Timeout handling
Progress monitoring

Memory Management¶

State Management¶

// Example of optimized state management
function updateState(newData: FitnessDataPoint[]) {
  setFitnessData((prev) => {
    const combined = [...prev, ...newData];
    // Keep only the most recent points for visualization
    return combined.slice(-MAX_DISPLAY_POINTS);
  });
}

Features:

Efficient state updates
Memory usage limits
Automatic cleanup
Performance monitoring
Cache management

Resource Optimization¶

interface ResourceConfig {
  memoryLimit: number;
  cpuThreshold: number;
  cleanupInterval: number;
}

// Example of resource monitoring
class ResourceMonitor {
  async checkResources(config: ResourceConfig) {
    const metrics = await getSystemMetrics();
    if (metrics.memory > config.memoryLimit) {
      await this.cleanup();
    }
    if (metrics.cpu > config.cpuThreshold) {
      await this.optimizeProcessing();
    }
  }
}

Features:

Resource usage monitoring
Automatic cleanup
Performance thresholds
Alert system
Optimization triggers

Encryption Performance¶

FHE Optimization¶

interface FHEConfig {
  scheme: "BFV" | "CKKS";
  securityLevel: number;
  polyModulusDegree: number;
  plainModulus?: number;
}

// Example of optimized FHE setup
const fheHandler = new FHEHandler({
  scheme: "BFV",
  securityLevel: 128,
  polyModulusDegree: 4096,
  plainModulus: 1024,
});

Features:

Parameter optimization
Memory usage control
Batch encryption
Performance monitoring
Hardware acceleration support

ZK Proof Optimization¶

interface ZKConfig {
  proofType: "range" | "equality" | "membership";
  constraints: number;
  optimization: "speed" | "memory" | "balanced";
}

// Example of optimized ZK proof generation
async function generateOptimizedProof(data: any, config: ZKConfig) {
  const circuit = await optimizeCircuit(data, config);
  return generateProof(circuit);
}

Features:

Circuit optimization
Constraint minimization
Proof caching
Parallel verification
Memory efficiency

Network Optimization¶

Data Streaming¶

interface StreamConfig {
  bufferSize: number;
  compression: boolean;
  batchInterval: number;
}

// Example of optimized data streaming
class DataStream {
  async streamData(data: AsyncIterator<any>, config: StreamConfig) {
    const buffer = [];
    for await (const chunk of data) {
      buffer.push(chunk);
      if (buffer.length >= config.bufferSize) {
        await this.processBatch(buffer);
        buffer.length = 0;
      }
    }
  }
}

Features:

Buffered streaming
Compression
Batch processing
Error handling
Progress tracking

Connection Management¶

interface ConnectionConfig {
  poolSize: number;
  timeout: number;
  keepAlive: boolean;
  retryStrategy: RetryConfig;
}

// Example of connection pool management
class ConnectionPool {
  async getConnection(config: ConnectionConfig) {
    const connection = await this.pool.acquire();
    try {
      await this.validateConnection(connection);
      return connection;
    } catch (error) {
      await this.handleConnectionError(error);
    }
  }
}

Features:

Connection pooling
Automatic reconnection
Load balancing
Error recovery
Performance monitoring

Best Practices¶

1. Data Processing¶

Use appropriate chunk sizes based on data type
Implement batch processing for bulk operations
Monitor memory usage and cleanup regularly
Implement proper error handling and recovery
Use progress tracking for long operations

2. State Management¶

Limit state size for UI components
Implement efficient update mechanisms
Use appropriate data structures
Clean up unused resources
Monitor performance metrics

3. Network Operations¶

Use connection pooling
Implement retry mechanisms
Compress data when appropriate
Monitor network performance
Handle errors gracefully

4. Encryption Operations¶

Choose appropriate encryption parameters
Use batch encryption when possible
Implement caching strategies
Monitor encryption performance
Optimize proof generation

Performance Monitoring¶

1. Metrics Collection¶

interface PerformanceMetrics {
  memory: {
    used: number;
    total: number;
    peak: number;
  };
  cpu: {
    usage: number;
    load: number;
  };
  network: {
    bandwidth: number;
    latency: number;
  };
  encryption: {
    throughput: number;
    latency: number;
  };
}

2. Alerting¶

interface AlertConfig {
  memoryThreshold: number;
  cpuThreshold: number;
  latencyThreshold: number;
  actions: AlertAction[];
}

3. Optimization Triggers¶

interface OptimizationTrigger {
  metric: keyof PerformanceMetrics;
  threshold: number;
  action: OptimizationAction;
}

Future Improvements¶

1. Processing Optimizations¶

Enhanced parallel processing
Improved memory management
Advanced caching strategies
Better resource utilization
Hardware acceleration

2. Encryption Optimizations¶

FHE parameter optimization
ZK circuit optimization
Improved key management
Enhanced batch processing
Hardware acceleration support

3. Network Optimizations¶

Advanced protocol support
Improved compression
Better connection management
Enhanced error recovery
Performance monitoring tools