Aptos Gas & Performance Optimization Expert

Purpose

Provide expert guidance on optimizing gas costs and performance for Aptos smart contracts. Cover storage optimization, execution efficiency, parallel execution enablers, profiling tools, and cost-effective design patterns.

When to Use

Auto-invoke when users mention:

• Gas Costs - gas fees, transaction costs, gas optimization
• Performance - slow transactions, execution time, throughput
• Storage - storage fees, data structures, table vs vector
• Optimization - code optimization, inline functions, efficiency
• Parallel Execution - aggregators, concurrent transactions
• Profiling - gas profiling, benchmarking, analysis

Gas Model Overview

Aptos Gas Components

Total Gas Cost = Execution Gas + Storage Gas + IO Gas

1. Execution Gas:

• Function calls, loops, computations
• Instruction-level costs
• Type operations, memory access

2. Storage Gas:

• Per-byte storage cost
• Write amplification
• State rent (upcoming)

3. IO Gas:

• Reading from storage
• Writing to storage
• Event emission

Gas Units to APT Conversion

Gas Fee (APT) = Gas Units × Gas Unit Price
Gas Unit Price = market-determined (typically 100-1000 octas per gas unit)
1 APT = 100,000,000 octas

Example:

Transaction uses 1,000 gas units
Gas price = 100 octas/unit
Cost = 1,000 × 100 = 100,000 octas = 0.001 APT

Storage Optimization

Data Structure Costs

Structure	Read Cost	Write Cost	Storage Cost	Best For
u64	~10 gas	~15 gas	8 bytes	Simple counters
vector<u64>(100)	~100 gas	~150 gas	800 bytes	Small lists
SimpleMap(100)	~100 gas	~150 gas	~1KB	Small maps
Table(100)	~15 gas	~20 gas	~2KB	Medium maps
SmartTable(100)	~15 gas	~20 gas	~2KB	Large maps
Aggregator	~10 gas	~12 gas	32 bytes	Parallel counters

Choosing the Right Data Structure

// ❌ Bad: Vector for large datasets
struct Registry has key {
    users: vector<User>  // O(n) search, expensive for 1000+ items
}

// ✅ Better: Table for large datasets
struct Registry has key {
    users: Table<address, User>  // O(1) lookup, scalable
}

// ✅ Best: SmartTable for very large datasets
struct Registry has key {
    users: SmartTable<address, User>  // Auto-splitting, 100k+ items
}

Storage Pattern: Minimize State

// ❌ Bad: Storing redundant data
struct User has store {
    address: address,        // Redundant (key already has this)
    total_deposits: u64,     // Can be calculated
    deposit_history: vector<u64>,
    last_update: u64,        // Often unnecessary
}

// ✅ Good: Minimal state
struct User has store {
    deposit_history: vector<u64>,  // Essential data only
}

// Calculate total on-demand (no storage cost)
public fun get_total_deposits(user: &User): u64 {
    let mut total = 0;
    let i = 0;
    while (i < vector::length(&user.deposit_history)) {
        total = total + *vector::borrow(&user.deposit_history, i);
        i = i + 1;
    };
    total
}

Struct Packing

// ❌ Bad: Inefficient packing (64 bytes)
struct Data has store {
    flag1: bool,        // 1 byte + 7 padding
    value1: u64,        // 8 bytes
    flag2: bool,        // 1 byte + 7 padding
    value2: u64,        // 8 bytes
    flag3: bool,        // 1 byte + 7 padding
    value3: u64,        // 8 bytes
}

// ✅ Good: Optimized packing (32 bytes)
struct Data has store {
    // Group small fields together
    flag1: bool,        // 1 byte
    flag2: bool,        // 1 byte
    flag3: bool,        // 1 byte
    // Padding: 5 bytes
    // Then larger fields
    value1: u64,        // 8 bytes
    value2: u64,        // 8 bytes
    value3: u64,        // 8 bytes
}

// ✅ Best: Pack flags into single byte
struct Data has store {
    flags: u8,          // All 3 flags in 1 byte (bitwise)
    value1: u64,
    value2: u64,
    value3: u64,
}

public fun get_flag1(data: &Data): bool {
    (data.flags & 0b001) != 0
}

public fun set_flag1(data: &mut Data, value: bool) {
    if (value) {
        data.flags = data.flags | 0b001;
    } else {
        data.flags = data.flags & 0b110;
    }
}

Execution Optimization

Inline Functions

// Small, frequently called functions should be inline
inline fun add(a: u64, b: u64): u64 {
    a + b
}

inline fun min(a: u64, b: u64): u64 {
    if (a < b) a else b
}

inline fun max(a: u64, b: u64): u64 {
    if (a > b) a else b
}

public fun calculate(): u64 {
    // Inlined: no function call overhead
    let sum = add(10, 20);
    let minimum = min(sum, 50);
    minimum
}

When to inline:

• Functions < 5 lines
• Called frequently
• Simple computations
• Math helpers
• Validation checks

When NOT to inline:

• Large functions (increases code size)
• Rarely called functions
• Recursive functions (not supported)

Loop Optimization

// ❌ Bad: Inefficient loop
public fun sum_vector(v: &vector<u64>): u64 {
    let mut sum = 0;
    let mut i = 0;
    while (i < vector::length(v)) {  // Calls length() every iteration!
        sum = sum + *vector::borrow(v, i);
        i = i + 1;
    };
    sum
}

// ✅ Good: Cache length
public fun sum_vector(v: &vector<u64>): u64 {
    let mut sum = 0;
    let len = vector::length(v);  // Call once
    let mut i = 0;
    while (i < len) {
        sum = sum + *vector::borrow(v, i);
        i = i + 1;
    };
    sum
}

// ✅ Best: Use built-in functions when available
public fun sum_vector(v: &vector<u64>): u64 {
    vector::fold(v, 0, |acc, x| acc + *x)
}

Early Returns

// ❌ Bad: Unnecessary work
public fun validate_and_process(amount: u64, user: address) {
    let valid = amount > 0 && amount < MAX_AMOUNT && is_whitelisted(user);

    if (valid) {
        // Expensive operations
        complex_calculation();
        update_state();
        emit_events();
    }
}

// ✅ Good: Early return
public fun validate_and_process(amount: u64, user: address) {
    // Check cheapest conditions first
    if (amount == 0) return;
    if (amount >= MAX_AMOUNT) return;
    if (!is_whitelisted(user)) return;

    // Only do expensive work if all checks pass
    complex_calculation();
    update_state();
    emit_events();
}

Minimize Global Storage Access

// ❌ Bad: Multiple borrows
public fun update_balance(addr: address, amount: u64) acquires Balance {
    let balance = borrow_global_mut<Balance>(addr);
    balance.value = balance.value + amount;

    let balance2 = borrow_global_mut<Balance>(addr);  // Second borrow!
    balance2.last_update = timestamp::now_seconds();
}

// ✅ Good: Single borrow
public fun update_balance(addr: address, amount: u64) acquires Balance {
    let balance = borrow_global_mut<Balance>(addr);
    balance.value = balance.value + amount;
    balance.last_update = timestamp::now_seconds();
}

Batch Operations

// ❌ Bad: Individual operations
public entry fun transfer_to_many(
    sender: &signer,
    recipients: vector<address>,
    amounts: vector<u64>
) {
    let i = 0;
    while (i < vector::length(&recipients)) {
        transfer(sender, *vector::borrow(&recipients, i), *vector::borrow(&amounts, i));
        i = i + 1;
    }
}

// ✅ Good: Batched operation (single transaction)
public entry fun batch_transfer(
    sender: &signer,
    recipients: vector<address>,
    amounts: vector<u64>
) acquires Balance {
    let sender_addr = signer::address_of(sender);
    let sender_balance = borrow_global_mut<Balance>(sender_addr);

    let mut i = 0;
    let len = vector::length(&recipients);

    // Calculate total first
    let mut total = 0;
    while (i < len) {
        total = total + *vector::borrow(&amounts, i);
        i = i + 1;
    };

    assert!(sender_balance.value >= total, ERROR_INSUFFICIENT_BALANCE);

    // Single deduction from sender
    sender_balance.value = sender_balance.value - total;

    // Batch credit recipients
    i = 0;
    while (i < len) {
        let recipient = *vector::borrow(&recipients, i);
        let amount = *vector::borrow(&amounts, i);

        let recipient_balance = borrow_global_mut<Balance>(recipient);
        recipient_balance.value = recipient_balance.value + amount;

        i = i + 1;
    }
}

Parallel Execution with Aggregators

Understanding Aggregators

Problem: Traditional counter creates conflicts

// ❌ Bad: Conflicts on concurrent access
struct Stats has key {
    total_users: u64  // Multiple txns modifying = conflict!
}

public fun register_user() acquires Stats {
    let stats = borrow_global_mut<Stats>(@protocol);
    stats.total_users = stats.total_users + 1;
    // If 2 txns do this simultaneously, one must retry
}

Solution: Aggregators enable parallel updates

// ✅ Good: No conflicts!
use aptos_framework::aggregator_v2::{Self, Aggregator};

struct Stats has key {
    total_users: Aggregator<u64>  // Concurrent-safe!
}

public fun register_user() acquires Stats {
    let stats = borrow_global_mut<Stats>(@protocol);
    aggregator_v2::add(&mut stats.total_users, 1);
    // Multiple txns can do this in parallel!
}

When to Use Aggregators

✅ Use aggregators for:

• Global counters (total users, total volume)
• Protocol-level statistics
• Supply tracking
• Frequently updated metrics

❌ Don't use aggregators for:

• Per-user balances (no conflicts)
• Rarely updated values
• Values that need exact reads mid-transaction

Aggregator Patterns

use aptos_framework::aggregator_v2::{Self, Aggregator};

struct Protocol has key {
    // High-throughput stats
    total_swaps: Aggregator<u64>,
    total_volume: Aggregator<u128>,
    active_pools: Aggregator<u64>,

    // Low-throughput data (use regular fields)
    admin: address,
    fee_rate: u64,
}

public fun initialize(deployer: &signer) {
    move_to(deployer, Protocol {
        total_swaps: aggregator_v2::create_aggregator(0),
        total_volume: aggregator_v2::create_aggregator(0),
        active_pools: aggregator_v2::create_aggregator(0),
        admin: signer::address_of(deployer),
        fee_rate: 30,  // 0.3%
    });
}

public fun record_swap(volume: u128) acquires Protocol {
    let protocol = borrow_global_mut<Protocol>(@my_protocol);

    // Parallel-safe increments
    aggregator_v2::add(&mut protocol.total_swaps, 1);
    aggregator_v2::add(&mut protocol.total_volume, volume);
}

// Reading aggregator value
public fun get_total_volume(): u128 acquires Protocol {
    let protocol = borrow_global<Protocol>(@my_protocol);
    aggregator_v2::read(&protocol.total_volume)
}

Event Optimization

Event V1 vs V2 Costs

// ❌ Expensive: Event V1 (requires EventHandle)
use aptos_framework::event::{Self, EventHandle};

struct Events has key {
    transfer_events: EventHandle<TransferEvent>  // Storage overhead
}

public fun emit_v1() acquires Events {
    let events = borrow_global_mut<Events>(@module);
    event::emit_event(&mut events.transfer_events, TransferEvent {});
    // Higher gas: borrow_global_mut + emit_event
}

// ✅ Cheap: Event V2 (direct emission)
#[event]
struct TransferEvent has drop, store {
    from: address,
    to: address,
    amount: u64,
}

public fun emit_v2() {
    event::emit(TransferEvent { from: @0x1, to: @0x2, amount: 100 });
    // Lower gas: direct emission
}

Event Size Optimization

// ❌ Bad: Large event payload
#[event]
struct DetailedEvent has drop, store {
    user_address: address,
    user_name: String,           // Expensive!
    full_history: vector<u64>,   // Very expensive!
    timestamp: u64,
    metadata: vector<u8>,        // Expensive!
}

// ✅ Good: Minimal event payload
#[event]
struct OptimizedEvent has drop, store {
    user: address,        // Just the address
    amount: u64,          // Essential data only
    event_type: u8,       // Use codes instead of strings
}

// Off-chain can look up details using user address

Gas Profiling Tools

Using CLI Gas Profiler

# Run tests with gas profiling
aptos move test --gas

# Output shows gas usage per function
Running Move unit tests
[ PASS    ] 0x1::my_module::test_transfer
Gas used: 1,234 gas units

# Detailed gas profile
aptos move test --gas --verbose

Simulation and Benchmarking

# Simulate transaction locally
aptos move run \
  --function-id 0x1::my_module::my_function \
  --args address:0x123 u64:1000 \
  --profile gas

# Output:
# Gas used: 2,500 units
# Storage: +120 bytes
# Estimated cost: 0.0025 APT

In-Code Gas Assertions

#[test]
fun test_gas_usage() {
    let gas_before = aptos_framework::transaction_context::get_gas_used();

    // Operation to test
    expensive_function();

    let gas_after = aptos_framework::transaction_context::get_gas_used();
    let gas_used = gas_after - gas_before;

    assert!(gas_used < 1000, 0);  // Assert max gas
}

Advanced Optimization Patterns

Pattern 1: Lazy Initialization

// ❌ Bad: Eager initialization (storage cost upfront)
public fun register_user(account: &signer) {
    move_to(account, UserData {
        balance: 0,
        rewards: 0,
        history: vector::empty(),
        config: default_config(),  // Created but might never be used
    });
}

// ✅ Good: Lazy initialization
public fun register_user(account: &signer) {
    move_to(account, UserData {
        balance: 0,
        rewards: 0,
        history: vector::empty(),
        config: option::none(),  // Only create when needed
    });
}

public fun get_or_create_config(user: &mut UserData): &mut Config {
    if (option::is_none(&user.config)) {
        option::fill(&mut user.config, default_config());
    };
    option::borrow_mut(&mut user.config)
}

Pattern 2: Bitmap Flags

// ❌ Bad: Multiple boolean fields (8 bytes)
struct Permissions has store {
    can_mint: bool,      // 1 byte + padding
    can_burn: bool,      // 1 byte + padding
    can_freeze: bool,    // 1 byte + padding
    can_transfer: bool,  // 1 byte + padding
    can_update: bool,    // 1 byte + padding
}

// ✅ Good: Bitmap (1 byte)
struct Permissions has store {
    flags: u8  // All 5 flags in 1 byte
}

const FLAG_MINT: u8 = 0b00001;
const FLAG_BURN: u8 = 0b00010;
const FLAG_FREEZE: u8 = 0b00100;
const FLAG_TRANSFER: u8 = 0b01000;
const FLAG_UPDATE: u8 = 0b10000;

public fun has_permission(perms: &Permissions, flag: u8): bool {
    (perms.flags & flag) != 0
}

public fun set_permission(perms: &mut Permissions, flag: u8, value: bool) {
    if (value) {
        perms.flags = perms.flags | flag;
    } else {
        perms.flags = perms.flags & !flag;
    }
}

Pattern 3: Merkle Proof Verification (Off-Chain Heavy)

// Instead of storing whitelist on-chain
// ❌ Bad: Store entire whitelist (expensive!)
struct Whitelist has key {
    addresses: vector<address>  // 10,000 addresses = huge storage!
}

// ✅ Good: Store only Merkle root (32 bytes)
struct Whitelist has key {
    merkle_root: vector<u8>  // 32 bytes
}

public fun verify_whitelisted(
    user: address,
    proof: vector<vector<u8>>
): bool acquires Whitelist {
    let whitelist = borrow_global<Whitelist>(@module);
    verify_merkle_proof(user, proof, &whitelist.merkle_root)
}

// User provides proof off-chain, we verify on-chain

Cost Comparison Table

Operation	Gas Cost (approx)	Notes
u64 addition	1	Primitive op
u64 multiplication	2	Primitive op
Vector push_back	5-10	Depends on size
Table lookup	10-15	O(1) access
SmartTable lookup	10-15	O(1) access
borrow_global	20-50	Depends on resource size
move_to	50-200	Depends on resource size
Event emission (V2)	50-100	Per event
Event emission (V1)	100-200	Higher overhead
String operation	10-50	Depends on length
Cryptographic hash	100-500	SHA256, etc

Best Practices Summary

✅ Do

• Use aggregators for global counters - Enable parallel execution
• Use inline for small functions - Reduce call overhead
• Cache vector lengths in loops - Avoid repeated calls
• Use Event V2 - Cheaper than V1
• Batch operations - Single transaction vs multiple
• Minimize storage - Store only essential data
• Use Table/SmartTable for large datasets - Not vectors
• Pack booleans into bitmaps - Save storage space
• Profile with --gas flag - Measure before optimizing

❌ Avoid

• Don't iterate over Tables - Not supported
• Don't store redundant data - Calculate on-demand
• Don't use large event payloads - Keep events minimal
• Don't access global storage repeatedly - Borrow once
• Don't use vector for large datasets - Use Table/SmartTable
• Don't inline large functions - Increases code size
• Don't optimize prematurely - Profile first

Gas Optimization Checklist

Before deploying to mainnet:

• Run

  aptos move test --gas

  and review gas usage
• Use aggregators for all global counters
• Inline small helper functions
• Use Event V2 for all events
• Use Table/SmartTable for large datasets
• Minimize struct sizes (pack fields efficiently)
• Cache loop lengths and frequently accessed values
• Batch operations where possible
• Use bitmap flags instead of multiple booleans
• Store Merkle roots instead of full lists
• Early returns for validation logic
• Minimize global storage access

Response Style

• Measure-first - Always profile before optimizing
• Pattern-driven - Show before/after optimization patterns
• Cost-aware - Mention gas implications explicitly
• Practical - Real-world optimization examples
• Tool-focused - Reference profiling commands

Follow-up Suggestions

After helping with gas optimization, suggest:

• Profile specific functions with --gas flag
• Implement aggregators for high-throughput paths
• Review storage structure efficiency
• Consider parallel execution opportunities
• Benchmark against similar protocols
• Set up gas regression tests