I use for AMMs on Sui that maximizes parallelism, keeps per-transaction gas predictable, and preserves safety/invariants. I’ll explain the high-level architecture, the trade-offs, concrete Move patterns (code snippets) you can drop into Sui, and an operational/testing checklist so you can deploy with confidence.

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TL;DR

I shard the AMM into many small, independent objects (price-range shards or per-position objects), use per-shard fee accumulators with lazy claiming, and handle multi-shard trades with a prepare/commit (or off-chain sequencer + on-chain settlement) pattern. Keep hot bookkeeping lazy, keep objects small, make worker/relayer flows idempotent, and enforce on-chain invariants (sum of shards ≈ pool total). This gives near-linear parallel throughput because most trades touch only a single object.

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Design principles I follow

1. Minimize objects touched per tx — each common trade should touch 1 or 2 objects only.
2. Make user positions owned objects — they can be operated in parallel when disjoint.
3. Lazy accounting — accumulators (e.g., acc_fee_per_liq) avoid touching every LP on each trade.
4. Localize contention — narrow down shared state to tiny shard objects (price band, tick objects).
5. Safe multi-shard flows — use two-phase prepare/commit or batch settlement to maintain atomicity when multiple shards must be touched.
6. Off-chain routing & sequencers — amortize matching cost for HFT; verify settlement on-chain.
7. Idempotence and short locks — workers/replayers must be idempotent; locks should expire.

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High-level architecture (what I implement)

• Shard objects: each shard covers a price bucket or tick range and holds reserves & accumulators.
• Position objects: each LP position is an owned object (NFT-like) with liquidity and shard reference.
• Accumulators: per-shard acc_fee_per_liq used for lazy fee accrual.
• Trade pathing: normal trades route to a single shard; complex large trades may span shards and use prepare/commit.
• Worker relayers: process multi-shard commits or batched settlements; idempotent and incentivized.
• Policy & governance objects: for fee parameters, routing fees, and shard-splitting criteria.

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Concrete Move patterns (selected snippets)

These are runnable-looking Move sketches (adapt tx_context/object::new to your Sui toolchain).

1) Shard and Position objects

module amm::shards {
    use sui::object::{UID};
    use sui::tx_context::TxContext;
    use std::vector;

    const PRECISION: u128 = 1_000_000_000u128;

    // A price-range shard (small object)
    struct Shard has key {
        id: UID,
        price_min: u128,
        price_max: u128,
        reserve_x: u128,
        reserve_y: u128,
        total_liquidity: u128,
        acc_fee_per_liq: u128, // scaled by PRECISION
    }

    // Per-LP position as an owned object (parallelizable)
    struct Position has key {
        id: UID,
        owner: address,
        shard_id: UID, // reference to the shard
        liquidity: u128,
        last_acc_fee_per_liq: u128,
    }

    public(entry) fun create_shard(min: u128, max: u128, x: u128, y: u128, ctx: &mut TxContext): Shard {
        Shard { id: object::new(ctx), price_min: min, price_max: max, reserve_x: x, reserve_y: y, total_liquidity: 0u128, acc_fee_per_liq: 0u128 }
    }

    public(entry) fun create_position(owner: address, shard: &Shard, liquidity: u128, ctx: &mut TxContext): Position {
        let id = object::new(ctx);
        Position { id, owner, shard_id: shard.id, liquidity, last_acc_fee_per_liq: shard.acc_fee_per_liq }
    }
}

2) Single-shard trade (fast path)

This is the common, fully-parallelizable operation. It only touches one Shard object and the taker.

module amm::trade {
    use amm::shards::{Shard, PRECISION};
    use sui::tx_context::TxContext;

    // very simplified constant product swap on a single shard
    public(entry) fun trade_on_shard(shard: &mut Shard, amount_in: u128, min_out: u128) {
        // compute fee and net input
        let fee = amount_in * 3u128 / 1000u128; // 0.3%
        let net_in = amount_in - fee;

        // update reserves with constant product
        let new_x = shard.reserve_x + net_in;
        let k = shard.reserve_x * shard.reserve_y;
        let new_y = k / new_x;
        let amount_out = shard.reserve_y - new_y;
        assert!(amount_out >= min_out, 1);

        shard.reserve_x = new_x;
        shard.reserve_y = new_y;

        // accumulate fee per liquidity
        if (shard.total_liquidity > 0) {
            shard.acc_fee_per_liq = shard.acc_fee_per_liq + (fee * PRECISION / shard.total_liquidity);
        }
    }
}

3) Lazy fee claiming for an LP position

LPs claim fees lazily by reading acc_fee_per_liq from the shard and comparing to their last_acc_fee_per_liq.

module amm::fees {
    use amm::shards::{Shard, Position, PRECISION};
    use sui::tx_context::TxContext;

    public(entry) fun claim_fees(pos: &mut Position, shard: &Shard) {
        let delta = shard.acc_fee_per_liq - pos.last_acc_fee_per_liq;
        let owed = pos.liquidity * delta / PRECISION;
        pos.last_acc_fee_per_liq = shard.acc_fee_per_liq;
        // transfer `owed` to pos.owner (implementation specific)
    }
}

4) Two-phase prepare/commit for multi-shard trades

When a trade spans multiple shards (e.g., large swap crossing ticks), I use a PreparedTrade object that reserves amounts and must be committed in a second tx (or auto-committed by a worker). This avoids holding many shards locked in ad-hoc ways.

module amm::atomic {
    use sui::object::{UID};
    use sui::tx_context::TxContext;
    use std::vector;

    struct PreparedTrade has key {
        id: UID,
        shard_ids: vector<UID>,
        reserved: vector<u128>, // reserved amounts per shard
        deadline_epoch: u64,
        committed: bool,
    }

    public(entry) fun prepare(shard_ids: vector<UID>, reserved: vector<u128>, deadline: u64, ctx: &mut TxContext): PreparedTrade {
        PreparedTrade { id: object::new(ctx), shard_ids, reserved, deadline_epoch: deadline, committed: false }
    }

    public(entry) fun commit(prep: &mut PreparedTrade, now_epoch: u64) {
        assert!(!prep.committed, 1);
        assert!(now_epoch <= prep.deadline_epoch, 2);
        // perform final ledger updates on each shard (worker will load shards & do this)
        prep.committed = true;
    }

    public(entry) fun abort(prep: &mut PreparedTrade, now_epoch: u64) {
        assert!(!prep.committed, 1);
        assert!(now_epoch > prep.deadline_epoch, 3);
        // release reservations
        object::delete(prep);
    }
}

In practice I rarely require on-chain prepare/commit for casual trades — it’s used for large cross-shard rebalances or batched settlements.

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Routing & pathfinding (on-chain vs off-chain)

• Cheap read-only estimators on-chain: provide a view function to estimate cost across shards (read-only, safe). This can be executed in parallel with no locks.
• Off-chain router: the UI/aggregator computes best route across shards and returns a single-shard route when possible. For complex multi-shard routes, the client can build a PreparedTrade + worker commit flow.

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Hotspot handling & shard splitting

• Monitor tx/sec, total_liquidity, trade_count per shard.
• When a shard is hot, split it into two child shards and migrate positions in small batches (use migration TXs that touch only a handful of objects).
• Maintain a ShardDirectory (small object) or deterministic mapping to route clients.

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Security considerations I enforce

1. Invariant checks: periodic reconcile() to assert sum(reserve_x across shards) == pool_total_x and similar for y.
2. Idempotent worker: processing prepared trades or batch settlements must check committed flag before applying changes.
3. Limit prepare window: short deadlines to avoid stale reservations causing long lock windows.
4. Slippage & sanity checks: require taker-specified min_out or max_in and check price impact.
5. Bounded loops: avoid loops proportional to total_positions in any hot path.
6. Fee & dust handling: aggregate small rounding dust and distribute periodically.

---

Testing & audit checklist (what I run)

• Concurrency fuzzing: many parallel single-shard trades and random multi-shard flows.
• Hotspot stress tests: intentionally overload a shard to exercise split/migration code.
• Invariants: contract asserts and off-chain reconciler to detect state drift.
• Replay & idempotency: simulate worker retries and ensure no double-apply.
• Economic audits: slippage, sandwich risk, front-running windows if off-chain sequencers are used.
• Security review: ensure prepare/commit can’t be front-ran to steal liquidity or bypass fees.

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Operational notes & UX

• Incentivize relayers/workers via small per-CrossMsg bounties or gas reimbursements so prepared trades are committed quickly.
• Expose per-shard metrics publicly (TVL, tx/sec) so clients route appropriately.
• Provide client SDK helpers to build single-shard vs prepared multi-shard flows automatically.
• For high-frequency markets, prefer off-chain matching + on-chain settlement (sequencer posts signed root + proof).

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When to use which pattern

• Small retail trades: single-shard trade (fast, parallel).
• LP management (add/remove liquidity): per-position objects — deposits/withdrawals only touch the LP and shard.
• Large trades crossing price ranges: prepare/commit or sequencer-batched settlement.
• Ultra HFT / orderbook hybrid: combine orderbook off-chain matching with on-chain settlement and per-position owned objects.

---

Short worked example (flow)

1. LP creates a Position object in Shard A — this is independent and only touches Position and Shard A (parallel).
2. Trader issues trade_on_shard(shardA, amount_in, min_out) — touches only Shard A (parallel with other shard ops).
3. Fee accrues to shardA.acc_fee_per_liq. LP later calls claim_fees(position, shardA) to get owed fees.
4. If a swap must cross shards, client creates PreparedTrade in TX1; worker picks and calls commit in TX2 to atomically apply deltas across the involved shards.

The usual bottleneck in AMMs is global state (one pool per pair). On Sui, I flip the model: instead of one monolithic pool, I design sharded pool objects.

Each pool pair (TokenA, TokenB) isn’t a single global object but is subdivided into independent pool shards (like "liquidity buckets"). Each swap interacts with only one shard, so parallel execution scales linearly with number of shards.

Core strategies I use:

1. Shard by price bands: Each shard manages a bounded price range (similar to Uniswap v3 ticks). Swaps route through as many shards as needed.
2. Shard by liquidity providers: LPs can spin up independent pool shards; trades select shards dynamically.
3. Ephemeral routing objects: A swap transaction creates a routing object that touches only shards involved, leaving the rest of liquidity untouched → enabling parallelism.

Move snippet (sketch):

struct PoolShard<TokenA, TokenB> has key {
    id: UID,
    reserve_a: u64,
    reserve_b: u64,
    price_lower: u64,
    price_upper: u64,
}

public entry fun swap_a_for_b<TokenA, TokenB>(
    shard: &mut PoolShard<TokenA, TokenB>, 
    input: Coin<TokenA>, 
    ctx: &mut TxContext
): Coin<TokenB> {
    let amount_in = coin::value(&input);
    let output = get_amount_out(amount_in, shard.reserve_a, shard.reserve_b);
    shard.reserve_a = shard.reserve_a + amount_in;
    shard.reserve_b = shard.reserve_b - output;
    coin::destroy(input);
    coin::mint<TokenB>(output, ctx)
}

With object-level sharding, each PoolShard is a disjoint Sui object. Multiple swaps across different shards run in parallel, maximizing throughput.

This is how I exploit object parallelism: design AMMs as a family of shard objects instead of global liquidity locks.

As a professional Web3 developer specializing in Sui and Move, I'll outline my approach to building highly scalable AMMs that leverage Sui's object-based parallelism.

Core Architecture Design

My implementation centers around four key components that work together to maximize parallel execution benefits:

1. Object-Oriented Asset Management```move struct Pool { // Object-based storage for efficient parallel access assets: map<address, Asset>, lp_tokens: map<address, LPToken>, metadata: PoolMetadata, }

struct Asset { id: ID, balance: uint128, price_oracle: OracleRef, }

2. **Parallel Transaction Handler**```move
public entry fun execute_pool_operation(
    pool_id: ID,
    operation: Operation,
    ctx: &mut TxContext
) {
    let pool = borrow_global_mut<Pool>(pool_id);
    
    // Split operations into parallelizable chunks
    match operation {
        AddLiquidity => {
            parallel_execute!(
                update_asset_balances,
                mint_lp_tokens,
                update_pool_metadata
            )
        }
        Swap => {
            parallel_execute!(
                validate_input_amount,
                calculate_output,
                transfer_assets
            )
        }
    }
}

flowchart TD
    classDef core fill:#FF9999,stroke:#CC0000,color:#000
    classDef parallel fill:#99FF99,stroke:#00CC00,color:#000
    classDef storage fill:#9999FF,stroke:#0000CC,color:#000
    classDef oracle fill:#FFFF99,stroke:#CCCC00,color:#000

    subgraph Core["Core Components"]
        Pool["Pool Manager"]:::core
        PTB["Programmable Transaction Blocks"]:::core
    end

    subgraph Parallel["Parallel Execution Layer"]
        PE["Execution Engine"]:::parallel
        MEV["MEV Protection"]:::parallel
        DAG["DAG-based Consensus"]:::parallel
    end

    subgraph Storage["Object Storage"]
        Assets["Asset Objects"]:::storage
        LP["LP Tokens"]:::storage
        Metadata["Pool Metadata"]:::storage
    end

    subgraph Oracle["Price Feeds"]
        PO["Price Oracles"]:::oracle
        Cache["Price Cache"]:::oracle
    end

    Pool --> PTB
    PTB --> PE
    PE --> MEV
    PE --> DAG
    
    Pool --> Assets
    Pool --> LP
    Pool --> Metadata
    
    Assets <--> PO
    PO --> Cache

    %% Legend
    subgraph Legend["Legend"]
        C1["Core Components"]:::core
        P1["Parallel Processing"]:::parallel
        S1["Storage Layer"]:::storage
        O1["Oracle System"]:::oracle
    end

The diagram above illustrates my AMM architecture, where:

• Red components represent core system elements handling pool operations
• Green sections show the parallel processing infrastructure
• Blue represents object storage for assets and metadata
• Yellow indicates external price feed integration

Implementation Details

1. Parallel Execution Optimization```move public fun execute_parallel_operations( ops: vector, ctx: &mut TxContext ): (vector, vector) { let results = parallel::execute_independent(ops);

   // Process results in parallel let success_rates = parallel::map(results, |r| r.success); let gas_costs = parallel::map(results, |r| r.gas_used);

   return (success_rates, gas_costs); }

2. **State Management**```move
struct PoolMetadata {
    total_liquidity: uint128,
    fee_rate: uint128,
    last_update_timestamp: u64,
}

// Update multiple pools in parallel
public entry fun batch_update_pools(
    pool_ids: vector<ID>,
    updates: vector<Update>,
    ctx: &mut TxContext
) {
    parallel::for_each(pool_ids, |pool_id| {
        let pool = borrow_global_mut<Pool>(pool_id);
        apply_update(&mut pool, updates.get(pool_id));
    });
}

Security Considerations

1. Protection Mechanisms

• Implement flash loan prevention through temporal locks
• Use Move's formal verification for critical paths
• Maintain separate price feeds for different asset pairs

2. Gas Efficiency

• Batch similar operations within transaction blocks
• Utilize sponsored transactions for common operations
• Implement lazy state updates where possible

Performance Optimization

1. Scaling Strategy

• Split large liquidity additions across multiple blocks
• Implement dynamic fee adjustment based on network congestion
• Cache frequently accessed data in memory

2. Monitoring and Maintenance

• Track pool utilization metrics
• Monitor gas costs per operation
• Adjust parallelization parameters based on performance data

By implementing this architecture, I achieve significant scaling benefits while maintaining security and minimizing gas costs. The object-based parallelism allows for efficient processing of multiple operations simultaneously, while the modular design ensures easy maintenance and upgrades.

Thoroughly test the parallel execution patterns under various load conditions before deployment, as the optimal configuration may vary depending on your specific use case and expected volume.

Focusing on a more modular and event-driven architecture that maximizes parallel execution benefits while maintaining security and scalability.

Alternative Architecture

Instead of a monolithic pool structure, I recommend implementing a modular design that separates concerns and maximizes parallel execution opportunities:

struct AssetManager {
    assets: map<address, Asset>,
    price_feeds: map<address, PriceFeed>,
    event_emitters: EventEmitter,
}

struct LiquidityManager {
    lp_tokens: map<address, LPToken>,
    fee_collector: FeeCollector,
    event_listeners: EventListener,
}

struct TradingEngine {
    order_book: OrderBook,
    matching_engine: MatchingEngine,
    risk_manager: RiskManager,
}

flowchart TD
    classDef core fill:#FF9999,stroke:#CC0000,color:#000
    classDef parallel fill:#99FF99,stroke:#00CC00,color:#000
    classDef storage fill:#9999FF,stroke:#0000CC,color:#000
    classDef oracle fill:#FFFF99,stroke:#CCCC00,color:#000

    subgraph Core["Core Components"]
        Pool["Pool Manager"]:::core
        PTB["Programmable Transaction Blocks"]:::core
    end

    subgraph Parallel["Parallel Execution Layer"]
        PE["Execution Engine"]:::parallel
        MEV["MEV Protection"]:::parallel
        DAG["DAG-based Consensus"]:::parallel
    end

    subgraph Storage["Object Storage"]
        Assets["Asset Objects"]:::storage
        LP["LP Tokens"]:::storage
        Metadata["Pool Metadata"]:::storage
    end

    subgraph Oracle["Price Feeds"]
        PO["Price Oracles"]:::oracle
        Cache["Price Cache"]:::oracle
    end

    Pool --> PTB
    PTB --> PE
    PE --> MEV
    PE --> DAG
    
    Pool --> Assets
    Pool --> LP
    Pool --> Metadata
    
    Assets <--> PO
    PO --> Cache

    %% Legend
    subgraph Legend["Legend"]
        C1["Core Components"]:::core
        P1["Parallel Processing"]:::parallel
        S1["Storage Layer"]:::storage
        O1["Oracle System"]:::oracle
    end

The diagram above illustrates the modular architecture, where:

• Red components represent core system elements handling pool operations
• Green sections show the parallel processing infrastructure
• Blue represents object storage for assets and metadata
• Yellow indicates external price feed integration

Implementation Strategy

1. Event-Driven Architecture```move public entry fun handle_liquidity_event( event: LiquidityEvent, ctx: &mut TxContext ) { match event { AddLiquidity => { parallel::execute_independent( update_asset_balances, mint_lp_tokens, emit_event ) } RemoveLiquidity => { parallel::execute_independent( burn_lp_tokens, transfer_assets, update_pool_stats ) } } }

2. **Risk Management**```move
struct RiskManager {
    slippage_tolerance: uint128,
    price_impact_limits: map<address, uint128>,
    liquidity_protection: LiquidityProtection,
}

public fun validate_trade(
    trade: Trade,
    ctx: &mut TxContext
): bool {
    // Parallel validation of multiple risk factors
    let price_valid = validate_price_impact(trade);
    let liquidity_valid = validate_liquidity_depth(trade);
    let slippage_valid = validate_slippage(trade);
    
    return price_valid && liquidity_valid && slippage_valid;
}

Security Considerations

1. Protection Mechanisms

• Implement flash loan prevention through temporal locks
• Use Move's formal verification for critical paths
• Maintain separate price feeds for different asset pairs

2. Gas Efficiency

• Batch similar operations within transaction blocks
• Utilize sponsored transactions for common operations
• Implement lazy state updates where possible

Performance Optimization

1. Scaling Strategy

• Split large liquidity additions across multiple blocks
• Implement dynamic fee adjustment based on network congestion
• Cache frequently accessed data in memory

2. Monitoring and Maintenance

• Track pool utilization metrics
• Monitor gas costs per operation
• Adjust parallelization parameters based on performance data

This alternative approach provides several advantages:

• Better separation of concerns for easier maintenance
• More efficient parallel execution through independent components
• Enhanced security through modular risk management
• Improved scalability through event-driven architecture

Thoroughly test the parallel execution patterns under various load conditions before deployment, as the optimal configuration may vary depending on your specific use case and expected volume.