Parallelization or parallel transaction execution is an efficient approach to blockchain transaction processing. It involves processing multiple transactions simultaneously instead of executing them one after the other which is how early blockchain networks process transactions.
Legacy blockchains like Ethereum face inherent scalability challenges, resulting in slow processing speed and high transaction costs, especially during periods of high demand.
A key factor behind this limitation is the sequential execution of transactions. In early blockchains transactions are queued and processed serially while validators prioritize transactions with higher gas fees.
Source: shoalresearch
Though this approach worked effectively in the early days of blockchain, the rapid growth in adoption and the increase in on-chain activities have rendered it inefficient.
To address these challenges, modern blockchains are adopting various models of parallelization. These innovations are unlocking faster, more scalable, and cost-effective networks, paving the way for blockchain’s continued evolution.
What is Parallel Transaction Execution?
In blockchain, parallelization is the practice of parallel transaction execution where multiple transactions are processed concurrently (at the same time).
Instead of sequentially processing transactions one after the other, the blockchain is structured to process multiple transactions at the same time. This improves transaction processing speed, lowers gas costs, and makes the network more scalable as transactions are executed efficiently.
Depending on the parallelization model, transactions are either concurrently processed and sorted later, or non-dependent transactions are separated from dependent transactions and processed simultaneously.
An analogy of sequential vs parallel transaction execution
Source: Seinetwork
To fully understand parallel execution let’s quickly highlight dependent and independent transactions.
Parallel Execution in Blockchain: Dependent vs. Independent Transactions
Independent Transactions: These transactions do not interact with or rely on other transactions, making them independent of each other. They do not modify the same blockchain state, and their execution paths do not overlap as such their outcomes are unaffected by the order in which they are processed.
For example: Mark sends ETH to Joe and in another separate event Janet sends ETH to Sean.
Both of these transactions are non-dependent and do not affect each other.
Dependent transactions: These transactions interact with the same blockchain state, such as modifying the same storage, and balances or accessing the same address. These actions create dependencies, meaning the outcome of a transaction relies on the result of another.
For instance, in a lending protocol, one transaction might deposit funds into a pool, while another might withdraw from it. These transactions are dependent as the withdrawal transactions rely on the previous deposit transaction to update the pool’s state.
Another example is in an AMM. If one transaction adds liquidity to a pool and another swaps tokens from the pool, the latter’s outcome will depend on the updated liquidity balance.
Blockchains use one of two parallelization models to process transactions in parallel. Let’s explore these approaches to parallelization.
Models of Parallel Transaction Execution
There are two models of Parallelization in blockchain
- State access
- Optimistic access
How they process dependent transactions is what sets these models apart, let’s highlight each model:
- State Access – The state access parallel execution model organizes transactions based on their impact on the network’s state. These transactions are categorized into dependent and independent transactions. Independent transactions are then executed simultaneously, ensuring efficient processing.
- Optimistic Parallelization – In the optimistic model, all transactions are initially assumed to be independent (unrelated). As a result, the network bypasses the initial sorting of transactions and processes them simultaneously. After processing, the blockchain retrospectively verifies this assumption, identifying any dependent transactions. If dependencies are found, the network adjusts these transactions to ensure the accuracy and consistency of the data.
Although state access models require additional effort from developers, especially on deterministic networks like Solana, they allow the network to sort and identify dependent transactions before execution. This results in a more optimized system with predictable outcomes and eliminates latency caused by retroactive verification.
On the other hand, optimistic parallelization enables faster transaction execution but comes at the cost of potential re-execution. If two related transactions are identified, the network re-executes them, either in parallel or sequentially, to avoid conflicts and ensure consistency.
Levels of Parrallization in Blockchain
Parallel transaction execution can be applied across there different levels, they are;
- Block-level parallelization
- Smart contract parallelization
- Transaction-level parallelization
Block-level Parallelization
Here blockchains concurrently create and validate multiple blocks with the aim to increase transaction throughput. Block-level parallel processing requires careful design to maintain blockchain security and it demands higher computational resources from network nodes, potentially increasing hardware requirements of validators.
Smart Contract Parallelization
Parallel processing at the smart contract level involves the concurrent execution of multiple smart contracts, significantly enhancing the performance and scalability of decentralized applications (DApps).
Mechanisms like optimistic rollups further optimize this process by executing transactions off-chain and aggregating them before finalizing on-chain. This reduces the load on the main blockchain, improving scalability and efficiency.
Transaction Level Parallelization
Transaction-level parallelization enables the simultaneous processing of multiple transactions, significantly boosting the network’s throughput and increasing transactions per second (TPS). By handling transactions concurrently, this approach reduces latency and enhances overall performance, leading to a more efficient and scalable network.
As highlighted above one of the major challenges in this level of parallel execution is separating dependent and independent transactions.
Implementations of Parallel Execution
Solana was the first blockchain designed specifically for parallel execution, leveraging Sealevel technology to process transactions across multiple nodes simultaneously. Since then, several other blockchains have adopted parallel transaction processing, including:
- Movement
- Sei
- Aptos
- Sui
- Neon
Additionally, Parallel EVM provides a framework that EVM-compatible chains can adopt to enable parallel transaction execution within the Ethereum Virtual Machine on their networks.
Closing Thoughts
The concept of parallel computing involves splitting a complex task into smaller parts that can be processed simultaneously. It has been around for decades. In blockchain, parallel execution builds on this principle to tackle issues like slow transaction speeds and high costs.
By processing multiple transactions or tasks at once, blockchain networks can become faster, more scalable, and more cost-effective. These improvements are essential for driving wider adoption and potentially making blockchain affordable for everyday tasks and minor payments.
[Author’s Note: This article does not represent financial advice, everything written here is strictly for educational and informational purposes. Please do your own research before investing.]
Author: Godwin Okhaifo
Also read: Parallel EVM Explained: Enhancing Ethereum’s Efficiency and Scalability