Parallel execution breaks linear ordering. Sequential blockchains like Ethereum guarantee a single, canonical transaction order. Parallel engines like Aptos' Block-STM or Sui's object-centric model process transactions concurrently, making the final order non-deterministic and dependent on runtime scheduling.
solana-and-the-rise-of-high-performance-chains
Blog
Why Parallel Execution Kills Transaction Ordering Guarantees
An analysis of how Solana's Sealevel and other parallel execution engines sacrifice the strong, global ordering of sequential chains like Ethereum to achieve high throughput, creating a new landscape for MEV and application design.
introduction
THE ORDERING PROBLEM
Introduction
Parallel execution, the key to scaling, fundamentally breaks the linear transaction ordering that underpins blockchain state.
This creates a new MEV surface. Without a guaranteed global sequence, front-running and sandwich attacks morph into scheduling-based MEV. Validators or sequencers can reorder parallelizable transactions to extract maximal value, a problem protocols like Flashbots SUAVE aim to mitigate.
State dependencies dictate the chaos. The severity of the ordering problem is a function of shared state contention. High-contention applications like NFT mints or DEX pools on Uniswap V3 suffer more than isolated transfers, creating unpredictable latency and execution outcomes for users.
key-trends
THE END OF LINEAR BLOCKCHAINS
The New Performance Paradigm
Parallel execution shatters the sequential processing model, delivering massive throughput at the cost of traditional ordering guarantees.
01
The Problem: The Sequential Bottleneck
Legacy blockchains like Ethereum process transactions one-by-one, creating a deterministic but slow global state machine. This guarantees ordering but caps throughput at ~15-30 TPS, causing network congestion and high fees during peak demand.
~15 TPS
Ethereum Cap
$100+
Peak Fees
02
The Solution: Concurrent State Access
Protocols like Solana, Sui, and Aptos execute non-conflicting transactions simultaneously by analyzing read/write sets. This allows for 10,000+ TPS but requires developers to explicitly declare data dependencies, breaking the simple linear model.
10,000+
Peak TPS
-90%
Avg. Cost
03
The Consequence: MEV Reordering Goes Nuclear
Without a strict global order, the battle for transaction positioning shifts to the scheduler. Validators in systems like Solana or block-builders in Ethereum PBS can exploit concurrency to create and capture new, more complex forms of Maximal Extractable Value (MEV).
$1B+
Annual MEV
Complex
New Attack Vectors
04
The Architect's Dilemma: Speed vs. Simplicity
Developers must now choose: accept the complexity of managing concurrency and state conflicts for performance, or revert to slower, serialized execution for predictability. This trade-off defines the new stack, splitting ecosystems into high-performance and developer-friendly camps.
Hard
Dev Complexity
Essential
New Skill
05
Sui's Object-Centric Model
Sui bypasses the ordering problem for simple transactions by making owned objects the atomic unit. Transfers can be executed instantly in parallel without consensus, achieving 120,000 TPS for payments, while shared objects require slower, ordered consensus.
120k TPS
Owned Objects
2-Tier
Execution Model
06
The Future: Intents & Declarative Transactions
The endgame is users submitting desired outcomes (intents) rather than precise transactions. Solvers (like in UniswapX or CowSwap) compete to fulfill them in parallel, abstracting away execution complexity. The network orders results, not commands.
Abstracted
Complexity
Efficient
Outcome
deep-dive
THE CONCURRENCY TRADEOFF
The Anatomy of Weak Ordering
Parallel execution fundamentally breaks the linear, predictable transaction ordering that underpins user experience and application logic.
Parallel execution decouples ordering from execution. Sequential chains like Ethereum process transactions in a single, canonical order. Parallel engines like Solana and Sui separate the tasks: a leader node sequences transactions, but execution occurs across multiple cores without guaranteed inter-core timing.
Weak ordering creates non-deterministic state. Two transactions modifying the same state object execute in order. Transactions touching disjoint state execute concurrently, making their final effect order unpredictable. This breaks applications that rely on implicit ordering, like NFT minting bots or MEV arbitrage.
The conflict is intentional for throughput. Protocols like Aptos and Monad accept this trade-off. Their performance gains—Aptos targets 160k TPS—require sacrificing strong ordering guarantees. The system's state access model (e.g., Sui's objects) defines which transactions can safely parallelize.
Evidence: The Solana experience. Solana's frequent failed transactions and jito bundles demonstrate weak ordering in practice. Users compete via priority fees, but execution race conditions still cause unpredictable failures, a direct consequence of its parallel architecture.
EXECUTION MODELS
Architectural Trade-Offs: Sequential vs. Parallel
Compares how transaction ordering and state access are managed under sequential and parallel execution models, explaining the fundamental trade-off between performance and determinism.
| Core Feature / Constraint | Sequential Execution (e.g., EVM, Solana) | Parallel Execution (e.g., Aptos Move, Sui Move) | Hybrid / Optimistic (e.g., Monad, Fuel) |
|---|---|---|---|
Transaction Ordering Guarantee | Strictly Enforced (Block Order) | Not Guaranteed (Runtime Order) | Conditionally Guaranteed (Re-execution) |
State Access Pattern | Implicit (All State) | Explicitly Declared (via Move) | Declared & Speculative |
Concurrency Detection Method | N/A (Single-threaded) | Static Analysis (Read/Write Sets) | Runtime Analysis & Rollback |
Theoretical Max TPS (Ideal) | ~1k - 10k | 100k+ (scales with cores) | 10k - 100k+ |
Worst-Case Latency Determinism | < 1 sec (predictable) | Variable (contention-dependent) | Bounded by re-execution window |
Developer Burden for Safety | Low (EVM semantics) | High (Must declare dependencies) | Medium (Tooling-assisted) |
MEV Resistance (Base Layer) | Low (Order-sensitive) | Higher (Order-insensitive blocks) | Medium (Depends on finalization) |
Example of Failed Optimization | N/A | Ethereum's sharding complexity | Optimistic Rollup challenge periods |
counter-argument
THE ORDERING GUARANTEE
The Rebuttal: Isn't This Just a Localized Problem?
Parallel execution fundamentally breaks the deterministic transaction ordering that DeFi and cross-chain protocols rely on.
Parallel execution breaks determinism. Sequential chains guarantee a single, canonical order of transactions. Parallel engines process transactions out-of-order, making the final state path-dependent and non-deterministic for external observers.
This cripples cross-chain composability. Protocols like Across and LayerZero depend on predictable state transitions to finalize cross-chain messages. Non-deterministic ordering introduces race conditions that break these atomic guarantees.
The problem is systemic. It's not just about one chain's internal state. The entire interoperability stack, from oracles like Chainlink to intent-based systems like UniswapX, assumes a shared, linear history of events.
Evidence: Solana's parallel runtime, despite its speed, requires explicit dependency declaration for transactions. This is a direct engineering trade-off for performance, sacrificing the implicit ordering guarantee of an EVM sequencer.
protocol-spotlight
THE NEW ORDER
Ecosystem Adaptation
Parallel execution shatters the linear transaction queue, forcing protocols to rebuild their assumptions from first principles.
01
The Problem: MEV Becomes Unpredictable
Sequential chains create a predictable, auctionable queue. Parallel execution scatters transactions across threads, making front-running and sandwich attacks non-deterministic. This breaks the economic models of existing searchers and builders.
- Searchers can't guarantee atomic arbitrage across pools.
- Builders lose the ability to construct optimal, contiguous blocks.
- Protocols face new, chaotic forms of latency-based MEV.
~0s
Atomic Window
Chaotic
MEV Landscape
02
The Solution: Intent-Based Architectures
Protocols like UniswapX and CowSwap abstract execution away from users. Instead of submitting a vulnerable transaction, users submit a signed intent ("I want this outcome"). Solvers compete off-chain to fulfill it optimally, then settle on-chain.
- Removes on-chain ordering dependency.
- Enables cross-domain bundling (e.g., Across, LayerZero).
- Shifts competition to solver efficiency, not network latency.
100%
User Protection
Off-Chain
Competition
03
The Problem: State Contention Deadlocks
Two parallel transactions touching the same state (e.g., the same AMM pool) cannot execute simultaneously. Naive parallelization causes aborts and retries, crippling throughput for high-contention assets. This creates a performance cliff for popular DeFi primitives.
- Throughput plummets for hot markets (e.g., a new meme coin launch).
- Gas fees become unpredictable due to re-execution.
- User Experience degrades with failed transactions.
>90%
Abort Rate Spike
Cliff
Performance
04
The Solution: Conflict-Aware Sharding
Systems like Aptos Block-STM and Sui's object model pre-analyze transactions for read/write sets. They shard non-conflicting transactions across cores and use optimistic concurrency control with re-execution for conflicts. This requires a fundamental shift in smart contract design toward owned objects.
- Maximizes core utilization despite conflicts.
- Requires new programming paradigms (move, objects).
- Delivers ~100k TPS for non-conflicting workloads.
100k+
Optimal TPS
Move
Paradigm Shift
05
The Problem: Oracle Latency Arbitrage
In a sequential chain, oracle updates (e.g., Chainlink) are a single point in the block. With parallel execution, transactions reading the same price feed across different threads may see stale values, enabling instantaneous arbitrage against the protocol. This breaks the security assumption of synchronous price data.
- Lending protocols become vulnerable to undercollateralized loans.
- Derivatives and perpetuals face new pricing attacks.
- Oracle update frequency becomes a critical bottleneck.
~400ms
Update Gap
Instant
Arbitrage Window
06
The Solution: Timelocked Commit-Reveal & TWAPs
Protocols must move away from spot price reliance. Uniswap V3 TWAP oracles provide a time-weighted average that smooths out intra-block volatility. More advanced systems use commit-reveal schemes where oracles commit to a price, then reveal it after a delay, making parallelized front-running impossible.
- Eliminates single-point price attacks.
- Integrates natively with AMMs.
- Adds 1-2 block latency for finality.
TWAP
Primary Oracle
1-2 Blocks
Safety Delay
takeaways
WHY ORDERING BREAKS
Architectural Implications
Parallel execution fundamentally redefines transaction processing, shattering the sequential guarantees that underpin traditional blockchain state.
01
The Problem: Sequential Execution as a State Lock
Legacy chains like Ethereum process transactions in a single, global queue. This creates a total order that acts as a global lock, guaranteeing state transitions are deterministic but capping throughput at ~15-45 TPS. Every node must replay the same sequence, making execution the primary bottleneck.
1x
Sequential
~15 TPS
Ethereum Cap
02
The Solution: Concurrent State Access (a la Solana, Sui, Aptos)
By analyzing transaction dependencies upfront, independent transactions can be processed simultaneously across multiple cores. This requires a runtime scheduler (e.g., Solana's Sealevel, Sui's Bullshark) that maps non-conflicting transactions to parallel execution lanes. The result is 10,000-100,000+ TPS for simple payments and swaps.
- Key Benefit: Linear scaling with CPU cores.
- Key Benefit: Eliminates idle compute waiting for unrelated transactions.
1000x+
Throughput
~50ms
Latency
03
The Consequence: MEV & Front-Running Re-Architected
Without a guaranteed global order, classic front-running (inserting a transaction before another) is impossible for independent transactions. MEV shifts from ordering games to dependency discovery and bundle construction. This gives rise to new infrastructure like Jito (Solana) for bundle auctions and Sui's sponsored transactions for gas-less user ops.
- Key Consequence: New forms of parallel MEV emerge around resource contention.
Paradigm Shift
MEV
Bundle Auctions
New Primitive
04
The New Guarantee: Causal Consistency, Not Total Order
Parallel systems provide causal consistency: transactions that touch the same state object (e.g., an NFT, a liquidity pool) are ordered linearly; independent transactions have no guaranteed relative order. This is the core trade-off: higher throughput for weaker ordering semantics. Protocols must be designed for this model, as seen in Aptos' Block-STM and its abort/retry logic for conflicts.
Causal
Consistency
Abort/Retry
Conflict Handle
05
The Infrastructure Gap: Indexers & Oracles Must Adapt
Applications relying on a single, canonical transaction sequence for event indexing (e.g., The Graph) or price feeds (e.g., Chainlink) face new challenges. The final block ordering is non-deterministic across nodes. This forces a move to state-based indexing and requires oracles to agree on a finalized view of state, not a tx log, increasing reliance on light clients and state proofs.
State-Based
New Indexing
Light Clients
Critical
06
The Verdict: A Necessary Trade-Off for Scale
Killing total order is not a bug; it's the price of web-scale throughput. The architectural shift moves complexity from the execution layer to the scheduler and application logic. Successful L1s (Solana) and L2s (Monad, Sei) embrace this. The future is optimistic parallelization with conflict resolution, not global locks.
Web-Scale
Goal
App-Layer
Complexity Shift
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