Optimizing Move Code for Low-Latency Execution on Sui

To optimize your Move contract for low-latency execution on Sui, consider these best practices:

Key Optimization Strategies

1. Minimize Storage Operations

   • Reduce the number of reads/writes to object storage
   • Batch operations where possible
   • Use object wrapping sparingly as it adds access overhead

2. Optimize Data Structures

   • Prefer primitive types over complex structures
   • Use vectors judiciously - fixed-size is better when possible
   • Consider using tables for large datasets

3. Function Design

   • Keep functions small and focused
   • Avoid deep call stacks
   • Minimize branching logic

4. Transaction Optimization

   • Split large transactions into smaller ones when possible
   • Use shared objects carefully (they require consensus)
   • Leverage Sui's parallel execution capabilities

5. Memory Management

   • Reuse objects instead of creating new ones
   • Avoid unnecessary copies
   • Be mindful of Move's ownership system

Sui-Specific Optimizations

1. Leverage Dynamic Fields for flexible data access patterns
2. Use Event Streaming instead of polling for real-time updates
3. Optimize Object Design to match Sui's object-centric model
4. Consider Package Upgrades rather than full redeployments when possible

Optimizing Move code for low-latency execution on Sui requires careful consideration of both the Move language itself and how the Sui platform handles transactions, resources, and execution. Below are several best practices and techniques you can apply to optimize your Move contracts and reduce latency for real-time use cases.

1. Minimize Gas Usage and Computational Complexity

• Limit expensive operations: Avoid computationally expensive operations like loops, sorting, or complex arithmetic, especially in frequently called functions. Use pre-calculated values when possible.
• Simplify logic: Simplify conditional checks and logic to reduce processing time. Focus on making functions as straightforward and minimal as possible.
• Avoid redundant checks: Only check conditions that are absolutely necessary, and avoid repeating expensive checks multiple times.
• Optimize storage access: Accessing the state of an object is one of the most costly operations in Move. Limit the number of object lookups, and avoid querying objects that won’t change in a single transaction.

Example: Instead of a loop or a complex calculation in Move:

public fun update_balance(account: &mut Account, amount: u64) {
    let balance = Account::balance(account);
    Account::set_balance(account, balance + amount);
}

Better approach: Optimize away unnecessary lookups or calculations:

public fun update_balance(account: &mut Account, amount: u64) {
    Account::set_balance(account, Account::balance(account) + amount);
}

2. Efficient Data Structures

• Minimize storage use: Use simple data structures like arrays or sets (from the standard Move library) that are optimized for performance, rather than complex custom data structures.

• Avoid large objects: Storing large objects, like arrays or maps, can increase the execution time due to state management overhead. Consider breaking large objects into smaller, more manageable pieces.

• Use Move's built-in collections: Sui’s standard Move collections, such as Vector or Set, are optimized for performance compared to custom collection types. Always prefer them unless you have a very specific reason for implementing custom collections.

3. Minimize Object Creation and Destruction

• Avoid unnecessary object creation: Creating and destroying objects can be slow and inefficient. Avoid creating new objects when it’s not strictly necessary.
• Reuse objects: If your contract design allows, reuse existing objects instead of creating new ones. This helps reduce overhead.

Example: Instead of creating a new object each time a value is updated, mutate an existing object:

public fun update_object(existing_obj: &mut SomeObject, new_value: u64) {
    existing_obj.value = new_value;
}

4. Transaction Batching

• Batch multiple operations: If your contract performs multiple operations on objects (like creating, updating, and deleting), try to batch them into a single transaction. This minimizes the number of state changes and avoids the overhead of multiple transactions.

Example: Instead of calling insert and update separately:

public fun batch_update(data: &mut Data, new_item: Item) {
    Data::insert(data, new_item);
    Data::update(data, new_item);
}

5. Pre-calculation and Caching

• Cache results where possible: For frequently accessed data or calculations, consider caching the results within the transaction to avoid repeated computation.
• Pre-calculate values off-chain: If certain values can be pre-calculated off-chain (e.g., aggregate data or statistics), do so, and pass them directly into the contract to save processing time during execution.

6. Optimize for Object Serialization

• Avoid heavy object serialization: Ensure that objects are serialized efficiently and minimize the number of serialized objects within a transaction. Serialization is a computationally expensive operation, and too much data being passed around can slow down transaction execution.

Practical approach:

• When dealing with large data, avoid serializing objects that are not necessary for the specific logic you're executing.
• Use smaller chunks of data or simplified representations when passing data between functions.

7. Avoid Overuse of Shared Objects

• Minimize shared objects: While shared objects are useful in multi-user environments, they can introduce contention, which causes latency issues due to the need for locks and handling simultaneous access by multiple transactions.
• Use owned objects instead, whenever possible. Ensure that your contract minimizes access to shared objects, as they can lead to transaction conflicts and delays.

8. Improve Transaction Synchronization

• Avoid contention: When many users interact with the same objects, Sui can face transaction conflicts and contention. These issues add latency. Consider partitioning your data into smaller objects or shards, each of which can be processed independently to avoid locking contention on shared resources.

Example: Instead of having one large object for user states, split it into smaller objects:

struct UserState has store {
    id: u64,
    balance: u64,
}

struct GameState has store {
    users: vector<UserState>,
}

By doing this, different users' data can be processed in parallel without locking conflicts.

9. Optimize for Off-Chain Execution

• Leverage off-chain computation: For use cases requiring frequent state updates or real-time processing, consider moving compute-heavy operations off-chain. Your contract could receive pre-calculated data from off-chain systems and focus on handling the minimal state changes needed on-chain.

• Use Oracles for real-time data: For data that needs to be updated frequently in real-time (e.g., market data or game state), consider using oracles that push updated data to the blockchain periodically, without requiring your contract to perform constant updates.

10. Use of Sui's Parallel Execution

• Maximize parallel execution: Sui’s design allows multiple transactions to be processed concurrently, provided there are no conflicts between the objects being modified. Ensure your contract is designed in such a way that transactions operate on different objects or resources in parallel.
• Minimize dependencies between transactions: The more independent your transaction logic is, the better Sui will perform in parallel execution. Transactions that only modify a single resource without dependencies on other transactions will benefit the most from parallelism.

Testing for Low-Latency Execution

• Test with Sui CLI: Use the Sui CLI to test the contract on the Testnet and Mainnet. Measure the latency and gas usage of your contract under various loads.

• Stress Testing: Simulate high transaction loads by sending multiple concurrent transactions to the contract. This helps identify performance bottlenecks.

• Monitor with Sui Explorer: Use Sui Explorer to inspect gas usage, transaction times, and performance data for your contract.

• Benchmarking: You can benchmark your contract’s performance using custom scripts to measure transaction throughput and latency.

Conclusion

To optimize your Move code for low-latency execution on Sui, you need to:

• Minimize gas usage and avoid expensive computations.
• Use efficient data structures like built-in Vector and Set.
• Reduce contention by splitting large objects into smaller sub-objects.
• Leverage off-chain computation where possible.
• Ensure minimal object access in concurrent transactions to avoid conflicts.

By adhering to these best practices and carefully monitoring your contract’s performance, you can significantly reduce latency and improve the real-time responsiveness of your dApp on the Sui network.

Optimize Move code for low latency with these key techniques:

1. Minimize Storage Writes

// ❌ Slow: Multiple writes
vector::push_back(&mut vec, item1);
vector::push_back(&mut vec, item2);

// ✅ Faster: Batch writes
let temp_vec = vector[item1, item2];
vector::append(&mut vec, temp_vec);

2. Use &mut References Wisely

// ❌ Slow: Unnecessary copies
fun process(data: Data) { ... }

// ✅ Faster: Borrow mutable reference
fun process(data: &mut Data) { ... }

3. Avoid Deep Object Nesting

// ❌ Slow: Deeply nested access
let value = object.field1.field2.field3;

// ✅ Faster: Flatten structures
struct FlatObject { field3: u64 }

4. Precompute Values

// ❌ Slow: Recomputing in loop
while (i < len) {
    let result = heavy_computation(x);
}

// ✅ Faster: Precompute
let precomputed = heavy_computation(x);
while (i < len) { ... }

5. Use Fixed-Size Arrays

// ✅ Faster than vectors for small, fixed data
let arr: array<u64, 4> = [0, 0, 0, 0];

Key Sui-Specific Optimizations

• Split Shared Objects: Reduce contention by sharding (e.g., per-user sub-objects).
• Batch TXs: Use PTBs to group operations.
• Gas Profiling: Run sui move test --gas-chart to identify bottlenecks.

To optimize Move code for low-latency execution on Sui, minimize storage reads and writes by using efficient data structures like Table or Bag for large collections. Avoid deep nesting and redundant computations. Structure transactions to operate on owned objects instead of shared ones to reduce consensus overhead. Use programmable transaction blocks (PTBs) to batch operations and reduce round trips. Limit event emissions to only essential data. Finally, access on-chain time via the Clock module efficiently, as it is a singleton and can become a bottleneck if overused.

To optimize Move for low latency:

1. Minimize Storage Writes – Batch updates into fewer operations.
2. Use Flat Structs – Avoid deep nested objects (slow serialization).
3. Leverage PTBs – Combine transactions to reduce round trips.
4. Prefer vector Over custom collections (native optimizations).
5. Avoid Loops – Unroll small loops or use fixed-size arrays.

Key Gains:
✔ Faster execution (Sui’s parallel processing helps).
✔ Lower gas costs.

Watch For:

• Storage bottlenecks (hot objects).
• Generic type overhead.

(Test with sui move test --gas to profile.)

To optimize Move code for low-latency execution on Sui, minimize the number of objects read or written in a transaction to reduce dependency resolution time. Avoid shared objects when possible, as they introduce synchronization overhead and serialization. Use simple data structures like u64 or vector instead of deeply nested structs to reduce parsing and execution time. Reduce dynamic field operations, since these often require additional storage reads.

Avoid loops or recursion where the iteration count depends on input size, as this can introduce unpredictable delays. Structure your modules to minimize function call depth and avoid unnecessary abstraction that adds execution cost. Use the Sui dry-run and profiling tools to identify performance bottlenecks in logic and storage access. Optimize gas usage by limiting computation-heavy operations, which correlate with execution latency. For critical paths, prefer immutable or read-only access where possible to avoid lock contention. Test under simulated load using realistic transaction scenarios to ensure latency remains within target bounds.

To optimize Move code for low-latency execution on Sui:

Minimize Storage Access: Avoid deep reads/writes; batch updates and use smaller data structures.

Use object::borrow Efficiently: Prefer &mut over copy to reduce cloning and serialization costs.

Avoid Nested Loops & Recursion: Keep logic simple and flat to minimize execution steps.

Precompute Off-Chain: Move heavy computation or filtering logic off-chain.

Limit Dynamic Collection Size: Use bounded vectors instead of large, dynamic Table or Vec.

Profile & Benchmark: Use Sui DevInspect tool to identify bottlenecks.

Prioritize minimizing computational steps and storage mutations.

Optimizing Move Code for Low-Latency Execution on Sui

When building real-time applications on Sui, these optimization techniques can dramatically reduce your Move contract's execution time:

Core Optimization Strategies

1. Memory Access Patterns

// BAD: O(n) lookup
fun find_item(items: &vector<Item>, id: u64): &Item {
    let i = 0;
    while (i < vector::length(items)) {
        let item = vector::borrow(items, i);
        if (item.id == id) return item;
        i = i + 1;
    };
    abort E_NOT_FOUND
}

// GOOD: O(1) with Table
fun get_item(items: &Table<u64, Item>, id: u64): &Item {
    table::borrow(items, id)
}

2. Gas-Critical Section Optimization

// Optimized for minimal storage writes
public entry fun update_state(
    player: &mut Player,
    delta: u64,
    ctx: &mut TxContext
) {
    // 1. Do all computations first
    let new_score = player.score + delta;
    let new_level = calculate_level(new_score);
    
    // 2. Single storage update
    player.score = new_score;
    player.level = new_level;
}

Advanced Techniques

1. Hot/Cold Data Separation

struct PlayerProfile has key {
    id: UID,
    // Hot data (frequently accessed)
    hot: PlayerHotState,
    // Cold data (rarely accessed)
    cold: PlayerColdState
}

struct PlayerHotState has store {
    health: u8,
    position: (u64, u64),
    last_active: u64
}

struct PlayerColdState has store {
    creation_date: u64,
    historical_stats: vector<StatRecord>
}

2. Batch Processing

public entry fun process_batch(
    game: &mut GameState,
    updates: vector<PlayerUpdate>
) {
    let i = 0;
    while (i < vector::length(&updates)) {
        let update = vector::borrow(&updates, i);
        apply_update(game, update);
        i = i + 1;
    }
}

Sui-Specific Optimizations

1. Object Field Layout

// BAD: Mixed hot/cold fields
struct Player has key {
    id: UID,
    name: String,       // Rarely changed
    health: u8,        // Frequently changed
    biography: String,  // Rarely changed
    position: (u64, u64) // Frequently changed
}

// GOOD: Split by access pattern
struct Player has key {
    id: UID,
    static_data: PlayerStatic,
    dynamic_data: PlayerDynamic
}

2. Lazy Computation

struct GameState has key {
    id: UID,
    dirty: bool,       // Marks need for recomputation
    cached_value: u64, // Stale until recomputed
    // ... other fields
}

public entry fun update_state(
    state: &mut GameState,
    delta: u64
) {
    state.dirty = true;
    // Defer computation until actually needed
}

Measurement & Profiling

1. Gas Profiling:

   sui client call --gas-budget 10000 --gas-profiling --json <ARGS>
   

2. Critical Path Analysis:

   public entry fun measured_call() {
       let start = sui::clock::now();
       // Critical section
       let duration = sui::clock::now() - start;
       event::emit(LatencyEvent { duration });
   }
   

Optimization Checklist

1. Replace linear searches with Table lookups
2. Minimize storage operations in hot paths
3. Split frequently-accessed fields into separate objects
4. Use batch processing for related operations
5. Defer non-critical computations
6. Profile gas usage at function level
7. Avoid unnecessary copies with & references
8. Precompute values where possible

Real-World Example: Game Tick Optimization

// Before: 150ms tick
public entry fun game_tick(world: &mut World) {
    update_positions(world);
    update_physics(world);
    resolve_collisions(world);
    update_ai(world);
}

// After: 45ms tick
public entry fun optimized_tick(world: &mut World) {
    // Parallelizable systems
    let positions = parallel_update_positions(world);
    let physics = parallel_update_physics(world);
    
    // Sequential systems
    resolve_collisions_with(positions, physics);
    update_ai_batch(world);
}

Key gains:

• 3x speedup by separating parallelizable work
• Reduced contention via batched AI updates
• Smaller critical sections

Remember: Sui's parallel execution engine rewards designs that:

1. Minimize shared object access
2. Maximize independent execution paths
3. Reduce storage operations in hot loops

To optimize Move code for low-latency execution on Sui, start by minimizing access to shared objects since they serialize transaction execution and increase latency. Prefer owned objects where possible, as they allow parallelism and faster execution. Avoid complex loops and recursion in your Move code, since they increase computational cost and runtime unpredictability. Use lightweight, predictable data structures like fixed-size vectors and avoid dynamic field lookups unless necessary.

Reduce the number of Move calls within a single transaction block, and keep your entry functions small and focused. Cache repetitive calculations in local variables instead of recomputing them. Avoid unnecessary object reads and writes—accessing the object storage is expensive and contributes to higher latency. Where applicable, split large transactions into smaller atomic ones that can be executed independently. Use the #[inline] attribute on helper functions that are small and frequently called to reduce call overhead. Finally, use dry runs and performance profiling tools on localnet to identify slow spots and iterate quickly.