Skill: analyze-gas-optimization

Analyze and optimize Aptos Move contracts for gas efficiency, identifying expensive operations and suggesting optimizations.

When to Use This Skill

Trigger phrases:

• "optimize gas", "reduce gas costs", "gas analysis"
• "make contract cheaper", "gas efficiency"
• "analyze gas usage", "gas optimization"
• "reduce transaction costs"

Use cases:

• Before mainnet deployment
• When transaction costs are high
• When optimizing for high-frequency operations
• When building DeFi protocols with many transactions

Core Gas Optimization Principles

1. Storage Optimization

• Minimize stored data size
• Use efficient data structures
• Pack struct fields efficiently
• Remove unnecessary fields

2. Computation Optimization

• Avoid loops over large collections
• Cache repeated calculations
• Use bitwise operations when possible
• Minimize vector operations

3. Reference Optimization

• Prefer borrowing over moving when possible
• Use & and &mut efficiently
• Avoid unnecessary copies

Gas Cost Analysis

Expensive Operations

1. Global Storage Operations

// EXPENSIVE: Writing to global storage
move_to(account, large_struct);

// EXPENSIVE: Reading and writing
let data = borrow_global_mut\x3CLargeData>(addr);

// EXPENSIVE: Checking existence
if (exists\x3CResource>(addr)) { ... }

2. Vector Operations

// EXPENSIVE: Growing vectors dynamically
vector::push_back(&mut vec, item); // O(n) worst case

// EXPENSIVE: Searching vectors
vector::contains(&vec, &item); // O(n)

// EXPENSIVE: Removing from middle
vector::remove(&mut vec, index); // O(n)

3. String Operations

// EXPENSIVE: String concatenation
string::append(&mut s1, s2);

// EXPENSIVE: UTF8 validation
string::utf8(bytes);

Optimization Patterns

1. Batch Operations

// BAD: Multiple storage accesses
public fun update_values(account: &signer, updates: vector\x3CUpdate>) {
    let i = 0;
    while (i \x3C vector::length(&updates)) {
        let update = vector::borrow(&updates, i);
        let data = borrow_global_mut\x3CData>(update.address);
        data.value = update.value;
        i = i + 1;
    }
}

// GOOD: Single storage access with batch update
public fun batch_update(account: &signer, updates: vector\x3CUpdate>) {
    let data = borrow_global_mut\x3CData>(signer::address_of(account));
    let i = 0;
    while (i \x3C vector::length(&updates)) {
        let update = vector::borrow(&updates, i);
        // Update in memory
        update_memory_data(data, update);
        i = i + 1;
    }
}

2. Storage Packing

// BAD: Wasteful storage
struct UserData has key {
    active: bool,      // 1 byte used, 7 wasted
    level: u8,         // 1 byte used, 7 wasted
    score: u64,        // 8 bytes
    timestamp: u64,    // 8 bytes
    // Total: 32 bytes (50% wasted)
}

// GOOD: Packed storage
struct UserData has key {
    // Pack small fields together
    flags: u8,         // Bits: [active, reserved...]
    level: u8,
    reserved: u16,     // Future use
    score: u64,
    timestamp: u64,
    // Total: 20 bytes (37.5% saved)
}

3. Lazy Evaluation

// BAD: Always compute expensive value
struct Pool has key {
    total_shares: u64,
    total_assets: u64,
    // Computed on every update
    share_price: u64,
}

// GOOD: Compute only when needed
struct Pool has key {
    total_shares: u64,
    total_assets: u64,
    // Don't store computed values
}

public fun get_share_price(pool_addr: address): u64 {
    let pool = borrow_global\x3CPool>(pool_addr);
    if (pool.total_shares == 0) {
        INITIAL_SHARE_PRICE
    } else {
        pool.total_assets * PRECISION / pool.total_shares
    }
}

4. Event Optimization

// BAD: Large event data
struct TradeEvent has drop, store {
    pool: Object\x3CPool>,
    trader: address,
    token_in: Object\x3CToken>,
    token_out: Object\x3CToken>,
    amount_in: u64,
    amount_out: u64,
    fees: u64,
    timestamp: u64,
    metadata: vector\x3Cu8>, // Large metadata
}

// GOOD: Minimal event data
struct TradeEvent has drop, store {
    pool_id: u64,        // Use ID instead of Object
    trader: address,
    amounts: u128,       // Pack amount_in and amount_out
    fees: u64,
    // Compute other data from state
}

5. Collection Optimization

// BAD: Linear search
public fun find_item(items: &vector\x3CItem>, id: u64): Option\x3CItem> {
    let i = 0;
    while (i \x3C vector::length(items)) {
        let item = vector::borrow(items, i);
        if (item.id == id) {
            return option::some(*item)
        };
        i = i + 1;
    }
    option::none()
}

// GOOD: Use Table for O(1) lookup
struct Storage has key {
    items: Table\x3Cu64, Item>,
}

public fun find_item(storage: &Storage, id: u64): Option\x3CItem> {
    if (table::contains(&storage.items, id)) {
        option::some(*table::borrow(&storage.items, id))
    } else {
        option::none()
    }
}

Gas Measurement

1. Transaction Simulation

# Simulate to get gas estimate
aptos move run-function \
    --function-id 0x1::module::function \
    --args ... \
    --simulate

# Output includes:
# - gas_unit_price
# - max_gas_amount
# - gas_used

2. Gas Profiling

#[test]
public fun test_gas_usage() {
    // Measure gas for operation
    let gas_before = gas::remaining_gas();
    expensive_operation();
    let gas_used = gas_before - gas::remaining_gas();

    // Assert reasonable gas usage
    assert!(gas_used \x3C MAX_ACCEPTABLE_GAS, E_TOO_EXPENSIVE);
}

Optimization Checklist

Storage Checklist

• Pack struct fields to minimize size
• Use appropriate integer sizes (u8, u16, u32, u64)
• Remove unnecessary fields
• Consider off-chain storage for large data
• Use events instead of storage for logs

Computation Checklist

• Cache repeated calculations
• Minimize loops over collections
• Use early returns to skip unnecessary work
• Batch similar operations
• Avoid redundant checks

Collection Checklist

• Use Table/TableWithLength for key-value lookups
• Use SmartTable for large collections
• Limit vector sizes
• Consider pagination for large results
• Use appropriate data structures

Best Practices

• Profile before and after optimization
• Test gas usage in unit tests
• Document gas costs for public functions
• Consider gas costs in contract design
• Monitor mainnet gas usage

Common Gas Optimizations

1. Replace Vectors with Tables

// Before: O(n) search
struct Registry has key {
    users: vector\x3CUser>,
}

// After: O(1) lookup
struct Registry has key {
    users: Table\x3Caddress, User>,
    user_list: vector\x3Caddress>, // If iteration needed
}

2. Minimize Storage Reads

// Before: Multiple reads
public fun transfer(from: &signer, to: address, amount: u64) {
    assert!(get_balance(signer::address_of(from)) >= amount, E_INSUFFICIENT);
    let from_balance = borrow_global_mut\x3CBalance>(signer::address_of(from));
    let to_balance = borrow_global_mut\x3CBalance>(to);
    // ...
}

// After: Single read with validation
public fun transfer(from: &signer, to: address, amount: u64) {
    let from_addr = signer::address_of(from);
    let from_balance = borrow_global_mut\x3CBalance>(from_addr);
    assert!(from_balance.value >= amount, E_INSUFFICIENT);
    // ... rest of logic
}

3. Use Bitwise Flags

// Before: Multiple bool fields (8 bytes each)
struct Settings has copy, drop, store {
    is_active: bool,
    is_paused: bool,
    is_initialized: bool,
    allows_deposits: bool,
}

// After: Single u8 (1 byte)
struct Settings has copy, drop, store {
    flags: u8, // Bit 0: active, 1: paused, 2: initialized, 3: deposits
}

const FLAG_ACTIVE: u8 = 1;        // 0b00000001
const FLAG_PAUSED: u8 = 2;        // 0b00000010
const FLAG_INITIALIZED: u8 = 4;   // 0b00000100
const FLAG_DEPOSITS: u8 = 8;      // 0b00001000

public fun is_active(settings: &Settings): bool {
    (settings.flags & FLAG_ACTIVE) != 0
}

Gas Optimization Report Template

# Gas Optimization Report

## Summary

- Current average gas: X units
- Optimized average gas: Y units
- Savings: Z% reduction

## Optimizations Applied

### 1. Storage Optimization

- Packed struct fields (saved X bytes)
- Replaced vectors with tables (O(n) → O(1))
- Removed redundant fields

### 2. Computation Optimization

- Cached price calculations (saved X operations)
- Batched updates (N calls → 1 call)
- Early returns in validation

### 3. Event Optimization

- Reduced event size from X to Y bytes
- Removed redundant event fields

## Measurements

| Function | Before | After  | Savings |
| -------- | ------ | ------ | ------- |
| mint     | 50,000 | 35,000 | 30%     |
| transfer | 30,000 | 25,000 | 17%     |
| swap     | 80,000 | 60,000 | 25%     |

## Recommendations

1. Consider further optimizations for high-frequency functions
2. Monitor mainnet usage patterns
3. Set up gas usage alerts

Integration Notes

• Works with security-audit to ensure optimizations don't compromise security
• Use with generate-tests to verify optimizations maintain correctness
• Apply before deploy-contracts for mainnet deployments
• Reference STORAGE_OPTIMIZATION.md for detailed patterns

NEVER Rules

• ❌ NEVER optimize away security checks (access control, input validation)
• ❌ NEVER deploy optimized code without re-testing
• ❌ NEVER read .env or ~/.aptos/config.yaml during gas analysis (contain private keys)

References

• Aptos Gas Schedule: https://github.com/aptos-labs/aptos-core/blob/main/aptos-move/aptos-gas-schedule
• Move VM Gas Metering: https://github.com/aptos-labs/aptos-core/tree/main/aptos-move/aptos-vm
• Gas Optimization Patterns: Check daily-move repository for real examples