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
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
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
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,
};
Dynamic stop price adjustment:
pub const TrailingStopParams = struct {
distance: u64,
last_trigger_price: u64,
current_stop_price: u64,
};
Reference price tracking:
pub const PegParams = struct {
peg_type: PegType,
offset: i64,
limit_price: ?u64,
};
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
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
- Zero-copy architecture
- Cache-aligned structures
- Memory pooling
- Prefetching optimization
- Lock-free operations
- Atomic updates
- SIMD parallelization
- Sharded processing
- Vectorized operations
- Cache optimization
- Batch processing
- Predictive loading
- Bulk order operations
- Sub-tick pricing
- Advanced order types
- Real-time updates
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
}
}
fn processLevelBatchSIMD(
batch: *PriceLevelBatch,
remaining_amount: *u64,
total_filled: *u64,
last_price: *u64,
side: OrderSide,
monitor: *MatchingMonitor,
) !void {
// SIMD-optimized price level 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
}
}
- Use of CPU vector instructions
- Batch processing of orders
- Vectorized price comparisons
- SIMD-friendly memory layout
- Cache line alignment
- Prefetching
- Minimal cache misses
- Efficient data structures
- Zero-copy operations
- Memory pooling
- Efficient allocation
- Cache-friendly layout
- Lock-free algorithms
- Atomic operations
- Parallel processing
- Sharding strategy
- GPU acceleration
- FPGA integration
- Custom ASIC support
- Network optimization
- Cross-chain settlement
- Layer 2 integration
- Custom order types
- Enhanced analytics
- Dynamic sharding
- Adaptive prefetching
- ML-based optimization
- Custom memory allocators
- Price bounds checking
- Size validation
- Account verification
- Duplicate prevention
- Atomic operations
- Transaction validation
- Balance verification
- Error handling
- Rate limiting
- DOS prevention
- Error recovery
- State consistency
// Initialize orderbook with optimal shard count
var book = try ShardedOrderbook.init(
allocator,
getCPUCoreCount() * 2
);
// Place order with automatic sharding
try book.placeOrder(
side: OrderSide,
price: u64,
amount: u64,
order_id: u64
);
// Execute market order with SIMD matching
const result = try book.executeMarketOrder(
side: OrderSide,
amount: u64
);
// Place TWAP order
try book.placeTWAPOrder(
side: OrderSide,
price: u64,
total_amount: u64,
num_intervals: u64,
interval_seconds: u64
);