Parallel Execution in Blockchains, Explained

Parallel execution is a computational method that allows blockchain networks to process multiple transactions at the same time, rather than one after another. By utilizing multiple processing cores simultaneously, this architecture increases network throughput and reduces the congestion that traditionally leads to high transaction fees.

Historically, the dominant model for smart contract execution has been sequential. The Ethereum Virtual Machine, which powers Ethereum and numerous compatible networks, processes transactions one by one in a strictly ordered queue. While this guarantees predictable outcomes and security, it creates a severe bottleneck during periods of high network activity. Parallel execution architectures attempt to break this bottleneck by applying techniques from traditional database management to distributed ledger technology.

The Sequential Bottleneck

In a standard EVM environment, transactions undergo sequential execution. When a block is proposed, the network validates and applies each transaction strictly in the order it was received or prioritized by the validator.

If ten users mint a non-fungible token and ten different users trade assets on a decentralized exchange, a sequential network processes all 20 transactions in a single file line. The decentralized exchange users must wait for the token mints to conclude, even though the two groups of transactions interact with completely different smart contracts and do not affect one another's balances.

This single-threaded approach limits throughput to the processing power of a single CPU core. As network demand increases, users bid higher fees to secure a place near the front of this single line, leading to fee spikes.

The Core Challenge: State Contention

The primary obstacle to executing transactions in parallel is state contention. The "state" of a blockchain is a snapshot of all account balances and smart contract data at a given moment. Contention occurs when two or more transactions attempt to read or modify the exact same piece of state simultaneously.

Consider a numeric example involving state contention. Assume Alice has a wallet balance of 10 tokens. She submits two transactions almost simultaneously:

1. Transaction A: Send 8 tokens to Bob.
2. Transaction B: Send 5 tokens to Charlie.

If a network processes these in sequence, Transaction A succeeds, reducing Alice's balance to 2. Transaction B then fails because 2 is less than the required 5. However, if a network attempts to process both simultaneously on different processor cores without coordination, both cores read Alice's initial balance of 10. Both determine she has sufficient funds, and both execute. The network would then incorrectly record that Alice spent 13 tokens out of a 10-token balance, creating a severe accounting error.

To safely execute transactions in parallel, a blockchain must have a mechanism to identify which transactions contend for the same state and force them to process sequentially, while allowing non-contentious transactions to proceed simultaneously.

Explicit State-Access Lists

One approach to managing state contention relies on developers declaring transaction dependencies in advance, using state-access lists. Networks like Solana ($SOL) and Sui ($SUI) utilize this method to schedule execution.

Under this model, every transaction must explicitly state which accounts or memory locations it will read from or write to before it is executed. When transactions enter the network, the scheduling engine compares these lists. If Transaction X touches Account 1 and Transaction Y touches Account 2, the network maps them to different processor cores and executes them at the exact same time.

If Transaction Z also needs to touch Account 1, the network recognizes the overlap. It schedules Transaction Z to execute sequentially after Transaction X completes.

This explicit declaration makes the routing of transactions highly efficient. It eliminates the need for the network to guess whether transactions will conflict. However, it places a heavier burden on developers, who must accurately predict and encode the exact state elements their software will interact with.

Optimistic Parallel Execution

The alternative approach requires no advance declarations from developers, maintaining full compatibility with existing EVM applications. Networks such as Monad, Aptos ($APT), and Sei ($SEI) utilize optimistic concurrency control.

In an optimistic system, the network assumes that most transactions will not conflict. It distributes incoming transactions across multiple processor cores and executes them all simultaneously in the background. During this initial execution, the network records the state reads and writes of every transaction but does not permanently apply them to the ledger.

After the parallel execution finishes, the network performs a validation phase. It checks whether any transaction read a piece of state that was modified by a transaction preceding it in the block order.

If no conflicts are detected, the results are committed to the blockchain. If a conflict is found—for example, if our earlier example of Alice sending tokens to Bob and Charlie was processed optimistically—the network discards the invalid result. The conflicting transaction is then re-executed sequentially with the updated state.

This method allows developers to deploy standard EVM code without adding access lists. The tradeoff is computational overhead: the network must possess enough processing power to handle the discarded work and re-executions during periods of high state contention.

Localized Fee Markets

Parallel execution naturally enables localized fee markets, altering how users pay for block space. In a sequential network, heavy traffic on a single popular application congests the entire system, driving up fees for everyone.

In a parallel architecture, the network can isolate fee spikes to the specific state being contested. If thousands of users attempt to mint the same token, those specific transactions suffer from state contention and must be ordered sequentially. The network will charge elevated fees for transactions accessing that specific smart contract to manage the localized demand.

Meanwhile, users interacting with entirely unrelated contracts face no congestion. Their transactions are processed in parallel on separate processor cores, and they continue to pay the standard, low base fee.

Common Misconceptions

• Parallel execution inherently means faster block times: Processing transactions in parallel increases the total volume of transactions a network can handle per second (throughput), but it does not necessarily reduce the time it takes for the network to reach consensus and finalize a block (latency).
• Parallel execution eliminates all network congestion: While parallel networks handle uncorrelated transactions efficiently, massive traffic directed at a single smart contract still forces sequential execution for those specific transactions, creating localized bottlenecks.
• Optimistic execution is less secure than sequential execution: The optimistic model still enforces strict deterministic ordering before finalizing state changes. If a conflict occurs, the transaction is re-run, ensuring the final ledger remains mathematically accurate.

What to Watch

The implementation of parallel execution marks a structural shift in smart contract platforms. The industry is currently testing the practical limits of optimistic concurrency control versus explicit access lists in live environments.

Monitor the adoption of parallel EVM networks to see if the ease of porting existing Ethereum applications outweighs the computational overhead of the optimistic model. Additionally, observe how validation hardware requirements scale on parallel networks; requiring validators to run massive multi-core servers to handle throughput and re-executions can impact the geographic and numerical decentralization of node operators.