AbyssBook Technical Architecture

Core Components

1. Sharded Orderbook

The orderbook is horizontally sharded by price levels, enabling parallel processing and reducing contention:

pub const ShardedOrderbook = struct {
    shards: []OrderMap,
    bid_levels: []PriceLevelMap,
    ask_levels: []PriceLevelMap,
    shard_count: usize,
};

Key features:

Price-based sharding for optimal distribution
Independent shard processing
Lock-free operations within shards
Dynamic shard rebalancing

2. SIMD-Optimized Matching Engine

Utilizes CPU vector instructions for bulk order processing:

const VECTOR_WIDTH = if (builtin.cpu.arch == .x86_64) 
    @as(usize, 8) else @as(usize, 4);

const VectorizedMatch = struct {
    prices: [VECTOR_WIDTH]u64 align(32),
    amounts: [VECTOR_WIDTH]u64 align(32),
    results: [VECTOR_WIDTH]MatchResult align(32),
};

Optimizations:

Vectorized price comparisons
Batch order matching
SIMD-friendly memory layout
Automatic CPU feature detection

3. Cache-Optimized Data Structures

All critical data structures are cache-aligned and optimized:

const CACHE_LINE_SIZE = 64;
const MAX_ORDERS_PER_BATCH = 128;
const PREFETCH_DISTANCE = 8;

const PriceLevelCache = struct {
    levels: [PRICE_LEVEL_CACHE_SIZE]PriceLevel align(CACHE_LINE_SIZE),
    head: usize align(CACHE_LINE_SIZE),
    tail: usize align(CACHE_LINE_SIZE),
};

Features:

Cache line alignment
Predictive prefetching
Circular buffers
Zero-copy operations

4. Advanced Order Types

TWAP Orders

Time-Weighted Average Price execution:

pub const TWAPParams = struct {
    total_amount: u64,
    interval_seconds: u64,
    num_intervals: u64,
    start_time: i64,
    amount_per_interval: u64,
};

Trailing Stop Orders

Dynamic stop price adjustment:

pub const TrailingStopParams = struct {
    distance: u64,
    last_trigger_price: u64,
    current_stop_price: u64,
};

Peg Orders

Reference price tracking:

pub const PegParams = struct {
    peg_type: PegType,
    offset: i64,
    limit_price: ?u64,
};

5. Performance Monitoring

Built-in performance tracking:

pub const SIMDMetrics = struct {
    vector_operations: usize,
    scalar_operations: usize,
    cache_misses: usize,
    start_time: i128,
    end_time: i128,
};

Metrics tracked:

SIMD utilization
Cache efficiency
Latency distribution
Throughput rates

6. Solana Integration

Native Solana program integration:

pub const Processor = struct {
    state: *OrderbookState,
    account_data: []u8,
    clock: i64,
    state_manager: *StateManager,
};

Features:

Account management
Token transfers
Fee collection
Event emission

System Design

1. Memory Management

Zero-copy architecture
Cache-aligned structures
Memory pooling
Prefetching optimization

2. Concurrency Model

Lock-free operations
Atomic updates
SIMD parallelization
Sharded processing

3. Performance Optimization

Vectorized operations
Cache optimization
Batch processing
Predictive loading

4. Market Making Support

Bulk order operations
Sub-tick pricing
Advanced order types
Real-time updates

Implementation Details

1. Order Matching

fn matchOrderOptimized(
    self: *ShardedOrderbook,
    side: OrderSide,
    price: u64,
    amount: u64
) !MatchResult {
    var monitor = MatchingMonitor.init();
    var level_batch = PriceLevelBatch.init();
    
    // Process each shard with SIMD operations
    for (0..self.shard_count) |shard_index| {
        const levels = if (side == .Buy)
            &self.ask_levels[shard_index]
        else
            &self.bid_levels[shard_index];
            
        // Vectorized matching logic
    }
}

2. Price Level Management

fn processLevelBatchSIMD(
    batch: *PriceLevelBatch,
    remaining_amount: *u64,
    total_filled: *u64,
    last_price: *u64,
    side: OrderSide,
    monitor: *MatchingMonitor,
) !void {
    // SIMD-optimized price level processing
}

3. Settlement Processing

fn processSettleFunds(self: *Processor) !void {
    // Get pending matches
    var matches = try self.getPendingMatches();
    
    // Process each match atomically
    for (matches.items) |match| {
        // Calculate amounts
        const base_amount = match.quantity;
        const quote_amount = match.quantity * match.price;
        
        // Transfer tokens and collect fees
    }
}

Performance Considerations

1. SIMD Optimization

Use of CPU vector instructions
Batch processing of orders
Vectorized price comparisons
SIMD-friendly memory layout

2. Cache Optimization

Cache line alignment
Prefetching
Minimal cache misses
Efficient data structures

3. Memory Management

Zero-copy operations
Memory pooling
Efficient allocation
Cache-friendly layout

4. Concurrency

Lock-free algorithms
Atomic operations
Parallel processing
Sharding strategy

Future Optimizations

1. Hardware Acceleration

GPU acceleration
FPGA integration
Custom ASIC support
Network optimization

2. Advanced Features

Cross-chain settlement
Layer 2 integration
Custom order types
Enhanced analytics

3. Performance Enhancements

Dynamic sharding
Adaptive prefetching
ML-based optimization
Custom memory allocators

Security Considerations

1. Order Validation

Price bounds checking
Size validation
Account verification
Duplicate prevention

2. Settlement Safety

Atomic operations
Transaction validation
Balance verification
Error handling

3. System Protection

Rate limiting
DOS prevention
Error recovery
State consistency

Integration Guidelines

1. Initialization

// Initialize orderbook with optimal shard count
var book = try ShardedOrderbook.init(
    allocator,
    getCPUCoreCount() * 2
);

2. Order Placement

// Place order with automatic sharding
try book.placeOrder(
    side: OrderSide,
    price: u64,
    amount: u64,
    order_id: u64
);

3. Market Orders

// Execute market order with SIMD matching
const result = try book.executeMarketOrder(
    side: OrderSide,
    amount: u64
);

4. Advanced Orders

// Place TWAP order
try book.placeTWAPOrder(
    side: OrderSide,
    price: u64,
    total_amount: u64,
    num_intervals: u64,
    interval_seconds: u64
);

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architecture.md

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