Execution Model Architecture

Transaction Processing Pipeline

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Mempool       │───▶│  Execution      │───▶│   State         │
│   Manager       │    │  Dispatcher     │    │   Updates       │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         ▼                       ▼                       ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│ Transaction     │    │ SIMD            │    │ Storage         │
│ Validation      │    │ Optimization    │    │ Persistence     │
└─────────────────┘    └─────────────────┘    └─────────────────┘

Execution Dispatcher

Core Architecture

pub struct ExecutionDispatcher {
    pub score_cache: Arc<Mutex<ScoreCache>>,     // Thread-safe score cache
    pub adaptive_weights: AdaptiveWeights,        // Dynamic scheduling
    pub simd_processor: SimdProcessor,           // SIMD optimization
    pub config: DispatcherConfig,               // Configuration
}

Scheduling Algorithm

impl ExecutionDispatcher {
    pub fn schedule_transactions(
        &mut self,
        mempool_txs: Vec<MempoolTx>,
        signed_txs: Vec<SignedTx>,
    ) -> (Vec<MempoolTx>, Vec<SignedTx>) {
        // 1. Score cache lookup for known transactions
        let cached_scores = self.lookup_cached_scores(&mempool_txs);
        
        // 2. SIMD processing for uncached transactions
        let computed_scores = self.simd_processor.compute_scores_batch(&uncached_txs);
        
        // 3. Combine cached and computed scores
        let all_scores = self.combine_scores(cached_scores, computed_scores);
        
        // 4. Apply adaptive weight scheduling
        let weighted_scores = self.adaptive_weights.apply_weights(all_scores);
        
        // 5. Sort and select transactions
        self.select_optimal_transactions(mempool_txs, weighted_scores)
    }
}

SIMD Optimization

Vectorized Transaction Scoring

#[cfg(target_arch = "x86_64")]
#[target_feature(enable = "avx2", enable = "fma")]
unsafe fn compute_score_simd_batch(
    fees: &[f64],
    classes: &[TxClass],
) -> Vec<f64> {
    assert_eq!(fees.len(), classes.len(), 
        "SIMD input arrays must have same length: fees={}, classes={}", 
        fees.len(), classes.len());
    
    if fees.len() < SIMD_THRESHOLD {
        return compute_score_scalar_batch(fees, classes);
    }
    
    let mut results = vec![0.0; fees.len()];
    let chunks = fees.len().simd_chunks();
    
    for (chunk_idx, (fee_chunk, class_chunk)) in chunks.enumerate() {
        let base_idx = chunk_idx * 8;
        
        // Load 8 transactions into SIMD registers
        let fee_vec = _mm256_loadu_pd(fee_chunk.as_ptr() as *const f64);
        let class_vec = _mm256_loadu_pd(class_chunk.as_ptr() as *const f64);
        
        // Compute class priorities
        let priorities = compute_class_priorities_simd(class_vec);
        
        // Calculate scores: fee * class_priority
        let scores = _mm256_mul_pd(fee_vec, priorities);
        
        // Store results
        _mm256_storeu_pd(results.as_mut_ptr().add(base_idx) as *mut f64, scores);
    }
    
    // Handle remaining elements with scalar fallback
    let remaining_start = chunks.remainder_len_start();
    for i in remaining_start..fees.len() {
        results[i] = compute_score_scalar(fees[i], classes[i]);
    }
    
    results
}

Performance Characteristics

• SIMD Threshold: 32 transactions minimum
• Vector Width: 4 transactions per AVX2 register
• Speedup: 1.28x for large batches
• Fallback: Scalar processing for small batches

Cross-Platform Support

pub fn compute_score_batch(
    &self,
    fees: &[f64],
    classes: &[TxClass],
) -> Vec<f64> {
    #[cfg(target_arch = "x86_64")]
    {
        if is_x86_feature_detected!("avx2") && is_x86_feature_detected!("fma") {
            return unsafe { compute_score_simd_batch(fees, classes) };
        }
    }
    
    #[cfg(target_arch = "aarch64")]
    {
        if is_aarch64_feature_detected!("neon") {
            return unsafe { compute_score_neon_batch(fees, classes) };
        }
    }
    
    // Scalar fallback for all other architectures
    self.compute_score_scalar_batch(fees, classes)
}

Score Cache System

Cache Architecture

pub struct ScoreCache {
    cache: HashMap<(u64, TxClass), CacheEntry>,  // (sender_id, class) → score
    max_size: usize,                             // Maximum cache entries
    ttl: Duration,                               // Time-to-live
    hits: AtomicU64,                             // Cache hit counter
    misses: AtomicU64,                           // Cache miss counter
}

pub struct CacheEntry {
    pub score: f64,                              // Cached score
    pub timestamp: Instant,                      // Cache timestamp
    pub block_height: u64,                       // Height when cached
}

Cache Operations

impl ScoreCache {
    pub fn get_cached_score(&self, sender_id: u64, class: TxClass) -> Option<f64> {
        let key = (sender_id, class);
        
        if let Some(entry) = self.cache.get(&key) {
            // Check TTL expiration
            if entry.timestamp.elapsed() < self.ttl {
                self.hits.fetch_add(1, Ordering::Relaxed);
                return Some(entry.score);
            } else {
                // Remove expired entry
                self.cache.remove(&key);
            }
        }
        
        self.misses.fetch_add(1, Ordering::Relaxed);
        None
    }
    
    pub fn cache_score(&mut self, sender_id: u64, class: TxClass, score: f64, block_height: u64) {
        let key = (sender_id, class);
        let entry = CacheEntry {
            score,
            timestamp: Instant::now(),
            block_height,
        };
        
        // LRU eviction if cache is full
        if self.cache.len() >= self.max_size {
            self.evict_oldest();
        }
        
        self.cache.insert(key, entry);
    }
}

Cache Performance Metrics

pub struct CacheStats {
    pub hits: u64,                               // Cache hits
    pub misses: u64,                             // Cache misses
    pub hit_rate: f64,                           // Hit rate (hits / total)
    pub size: usize,                              // Current cache size
    pub max_size: usize,                          // Maximum cache size
}

impl ScoreCache {
    pub fn get_stats(&self) -> CacheStats {
        let hits = self.hits.load(Ordering::Relaxed);
        let misses = self.misses.load(Ordering::Relaxed);
        let total = hits + misses;
        
        CacheStats {
            hits,
            misses,
            hit_rate: if total > 0 { hits as f64 / total as f64 } else { 0.0 },
            size: self.cache.len(),
            max_size: self.max_size,
        }
    }
}

Adaptive Weight Scheduling

Weight Calculation Algorithm

pub struct AdaptiveWeights {
    pub fee_weight: f64,                         // Fee importance (0.0-1.0)
    pub priority_weight: f64,                    // Priority importance (0.0-1.0)
    pub update_interval: u64,                    // Update frequency (blocks)
    pub last_update: u64,                        // Last update height
}

impl AdaptiveWeights {
    pub fn calculate_adaptive_weights(&mut self, mempool_state: &MempoolState) {
        // Analyze mempool conditions
        let fee_pressure = self.calculate_fee_pressure(mempool_state);
        let congestion_level = self.calculate_congestion(mempool_state);
        
        // Adjust weights based on conditions
        if fee_pressure > HIGH_FEE_THRESHOLD {
            self.fee_weight = (self.fee_weight + 0.1).min(0.8);
            self.priority_weight = (1.0 - self.fee_weight).max(0.2);
        } else if congestion_level > HIGH_CONGESTION_THRESHOLD {
            self.priority_weight = (self.priority_weight + 0.1).min(0.8);
            self.fee_weight = (1.0 - self.priority_weight).max(0.2);
        }
    }
    
    pub fn apply_weights(&self, scores: Vec<f64>) -> Vec<f64> {
        scores.iter()
            .map(|&score| score * self.fee_weight + score * self.priority_weight)
            .collect()
    }
}

Mempool State Analysis

pub struct MempoolState {
    pub total_txs: usize,                        // Total transactions
    pub avg_fee_rate: f64,                       // Average fee rate
    pub size_bytes: usize,                       // Mempool size in bytes
    pub oldest_tx_age: Duration,                 // Oldest transaction age
    pub class_distribution: BTreeMap<TxClass, usize>, // Class distribution
}

Transaction Execution

Execution Pipeline

pub struct TransactionExecutor {
    pub runtime: Runtime,                        // Contract runtime
    pub gas_meter: GasMeter,                     // Gas measurement
    pub state_manager: StateManager,              // State management
}

impl TransactionExecutor {
    pub fn execute_transaction(
        &mut self,
        tx: &SignedTx,
        current_state: &StateDB,
    ) -> Result<ExecutionResult, ExecutionError> {
        // 1. Validate transaction
        self.validate_transaction(tx, current_state)?;
        
        // 2. Setup execution context
        let context = ExecutionContext::new(tx, current_state);
        
        // 3. Execute with gas metering
        let result = self.runtime.execute_with_gas_limit(
            &context,
            tx.call_data.clone(),
            self.gas_meter.remaining(),
        )?;
        
        // 4. Update state
        self.state_manager.apply_state_changes(result.state_changes)?;
        
        // 5. Consume gas
        self.gas_meter.consume_gas(result.gas_used);
        
        Ok(result)
    }
}

Contract Execution Model

pub struct Runtime {
    pub call_stack: Vec<CallFrame>,               // Call stack
    pub overlay_state: BTreeMap<[u8; 32], Account>, // Overlay state
    pub event_system: EventSystem,               // Event handling
    pub depth: u8,                               // Current call depth
}

impl Runtime {
    pub fn execute_contract(
        &mut self,
        contract_address: [u8; 32],
        caller: [u8; 32],
        value: u128,
        calldata: Vec<u8],
    ) -> Result<Vec<u8>, ContractError> {
        // Check call depth limit
        if self.depth >= MAX_CALL_DEPTH {
            return Err(ContractError::ExecutionError(
                "Maximum call depth exceeded".to_string()
            ));
        }
        
        // Get contract info
        let contract = self.get_contract(contract_address)?;
        
        // Create call frame
        let frame = CallFrame {
            contract_address,
            caller,
            value,
            calldata,
            return_data: Vec::new(),
            gas_remaining: self.gas_meter.remaining(),
            depth: self.depth,
            storage_snapshot: self.compute_storage_root(),
        };
        
        // Push frame and execute
        self.call_stack.push(frame);
        self.depth += 1;
        
        let result = self.execute_contract_code(&contract.code);
        
        // Pop frame
        self.call_stack.pop();
        self.depth -= 1;
        
        result
    }
}

Performance Optimization

Batch Processing

pub struct BatchProcessor {
    pub batch_size: usize,                       // Batch size
    pub parallel_workers: usize,                 // Parallel workers
    pub processing_queue: VecDeque<TransactionBatch>, // Processing queue
}

impl BatchProcessor {
    pub fn process_batch_parallel(
        &mut self,
        batch: TransactionBatch,
    ) -> Vec<ExecutionResult> {
        use rayon::prelude::*;
        
        batch.transactions
            .par_iter()
            .map(|tx| self.execute_single_transaction(tx))
            .collect()
    }
}

Memory Management

pub struct MemoryPool {
    pub transaction_pool: Vec<Transaction>,      // Transaction pool
    pub state_buffer: Vec<u8>,                  // State buffer
    pub result_buffer: Vec<ExecutionResult>,     // Result buffer
    pub max_memory: usize,                       // Maximum memory usage
}

impl MemoryPool {
    pub fn allocate_transaction(&mut self) -> Option<Transaction> {
        if self.transaction_pool.len() < self.max_memory / std::mem::size_of::<Transaction>() {
            self.transaction_pool.pop()
        } else {
            None
        }
    }
    
    pub fn reset(&mut self) {
        self.transaction_pool.clear();
        self.state_buffer.clear();
        self.result_buffer.clear();
    }
}

Error Handling

Execution Errors

pub enum ExecutionError {
    InsufficientGas { required: u64, available: u64 },
    InvalidNonce { expected: u64, actual: u64 },
    InsufficientBalance { required: u128, available: u128 },
    ContractNotFound { address: [u8; 32] },
    ContractExecutionFailed(String),
    StackOverflow { depth: u8, max_depth: u8 },
    OutOfMemory { requested: usize, available: usize },
}

Recovery Strategies

impl ExecutionDispatcher {
    pub fn handle_execution_error(&mut self, error: ExecutionError) -> RecoveryAction {
        match error {
            ExecutionError::InsufficientGas { .. } => RecoveryAction::SkipTransaction,
            ExecutionError::InvalidNonce { .. } => RecoveryAction::ReorderMempool,
            ExecutionError::InsufficientBalance { .. } => RecoveryAction::RemoveTransaction,
            ExecutionError::ContractNotFound { .. } => RecoveryAction::SkipTransaction,
            ExecutionError::ContractExecutionFailed(_) => RecoveryAction::RetryLater,
            ExecutionError::StackOverflow { .. } => RecoveryAction::AbortBatch,
            ExecutionError::OutOfMemory { .. } => RecoveryAction::ReduceBatchSize,
        }
    }
}

Performance Metrics

Execution Metrics

pub struct ExecutionMetrics {
    pub transactions_processed: u64,             // Total processed
    pub average_execution_time: Duration,        // Average execution time
    pub cache_hit_rate: f64,                     // Cache hit rate
    pub simd_utilization: f64,                   // SIMD utilization rate
    pub gas_consumed: u64,                       // Total gas consumed
    pub errors_encountered: u64,                 // Error count
}

Performance Targets

• Transaction Throughput: 10,000+ TPS
• Average Execution Time: < 100μs per transaction
• Cache Hit Rate: ≥80%
• SIMD Utilization: ≥70% for large batches
• Error Rate: <0.1%

This execution model provides a highly optimized, scalable, and reliable transaction processing system for Savitri Network.