In this article, I will cover Parallelized EVM Development: How to optimize smart contracts for the Monad and Sei ecosystems.
As blockchain networks develop, continually improving disparate parallel execution is one possible answer to the ongoing scalability problem.
With the construction of concurrency-aware smart contracts, developers will be able to transcend high throughput, low latency, and consistent performance across these new ecosystems.
Overview
The advancement of blockchain ecosystems is characterized by an ongoing struggle between scalability and decentralization.
Ethereum Virtual Machine (EVM) compatibility has become the standard for decentralized applications, but the sequential execution model often throttles performance.
Monad and Sei are two ecosystems reinventing parallelized execution for smart contracts. For developers, the challenge is clear: how do we optimize contracts to thrive in these parallelized environments without losing determinism, security, or composability?
Why Parallelization Matters
The EVM has always processed transactions in a sequential manner due to the need for determinism in the system. This single-threaded approach means that no transactions can be processed in parallel, reducing overall system throughput.
However, if transactions are processed in parallel, system throughput can be greatly increased as long as there are no state conflicts with respect to the transactions being processed. This creates a system in which thousands of transactions per second can be processed, all while maintaining consensus.
Monad aims to be the best system for high-performance parallel execution for blockchains with deterministic finality through the use of conflict resolution and speculative execution.
Sei characterizes itself as the first “parallelized Layer 1 for trading” as it specializes in low latency order matching and high frequency trading.
From a developer’s perspective, this means that smart contracts must be designed for parallel execution which means anticipating conflicts, minimizing the use of the shared state, and structuring the control flow to avoid state conflicts.
Core Principles of Parallelized Smart Contract Design
State Isolation Optimizing for MonadContracts should avoid relying on global state. Employ modular storage structures where each user or asset has isolated state variables.
For example, instead of having a single mapping for balances, shard balances into different sub-mappings keyed by user groups.
Conflict Minimization
Try to avoid designs where a large number of transactions modify the same variable. Instead of using counters or global accumulators, use event logs or aggregations done off chain. In trading protocols, using batch updates can help reduce contention on the order books.
Speculative Execution Awareness Because of Monads speculative execution, transactions can be executed in a tentative manner, and if there are no conflicts, the transactions are finalized. Therefore, try to construct your contracts in a way where there are overlapping writes.
Deterministic Parallelism Make certain that the logic of the contract always results in the same outcome, regardless of the order in which the transactions are executed. Try not to depend on the randomness of blocks or the sequential nature of the transactions.
Optimizing for Monad
With Monad’s architecture, parallel execution for transactions and pre-defined conflict resolution are key. Given that. developers should:
Implement more granular storage keys: Instead of updating one global variable, update multiple storage keys in order to alleviate, or reduce, contention.
Prepare for rollback-based design: Contracts need to handle speculative execution in a way that aborted transactions do not leave behind a partial state change.
Utilize batch processing: The execution of related actions in one atomic batch lowers the potential conflict.
As an instance, in Monad, a decentralized exchange should not a single monolithic order book. Rather, it should partition liquidity pools by trade pair to enable parallel execution via independent updates.
Optimizing for Sei
Sei has been designed for rapid, fair, parallelized trading and financial applications.
Order Matching Parallelism: Contracts should design order books so matching can be done across different pairs simultaneously.
Latency-Aware Design: Given Sei’s focus on real-time, low-latency execution, contracts should generally seek to reduce the computational work done per transaction.
Composable Parallelism: Design trading contracts so they can be used alongside other DeFi protocols without creating a bottleneck. This involves designing cross-contract calls to avoid sequential dependencies.
For example, a derivatives protocol on Sei should isolate per-user margin accounts so that liquidation checks can be done in parallel, and one user’s actions do not block the others.
Practical Techniques for Developers
Use Event-Driven Architecture: Instead of counting, update and share the event. Indexers that work off-chain can be used for aggregation without on-chain contention.
Sharding State Variables: Storage can be divided into independent shards that are keyed by user, asset, or trading pair.
Optimistic Concurrency Control: Transactions are assumed to be non-conflicting, but design for fallback strategies for retries if they are.
Avoid Sequential Dependencies: Don’t design transaction ordering to be a mechanism of correctness. Instead, design logic to be order-independent.
Challenges and Trade-offs
As introduced complexities, the main new challenge involving parallelization is:
Testing and Debugging: Developers need to model concurrent execution to pinpoint race conditions.
Security: Due to poorly designed contracts, state conflicts may be handled incorrectly, which could leave the door open for exploits.
Composable: Ensuring all contracts in the system work in concert requires synchronizing the state dependencies of the various contracts.
However, the advantages in latency, throughput, and scalability continue to make the tension worth the hassle.
Onward and Upward
The latest tools, parallelized EVMs like Monad and Sei, push the boundaries of blockchain scalability.
Those tools will force developers to completely rewrite state-based contract design, stepping away from sequential thinking completely, and developing for operational concurrency.
Those ecosystems will be the first of many leading to a wave of applications: real-time gaming, high-frequency trading, decentralized social networks, and many, many more.
The opportunity for developers is virtually limitless. state isolation with no conflicts, and design for deterministic parallelism, smart contracts will be able to fully utilize the power of parallelized execution like never before. The future of blockchain is not just faster, it’s also parallel.
FAQ
Monad uses speculative execution with conflict detection and rollback mechanisms, ensuring transactions run in parallel while maintaining deterministic outcomes.
Sei focuses on trading and financial applications, optimizing for low-latency order matching and parallel execution across multiple trading pairs.
By sharding state variables, using event logs instead of counters, and structuring updates so that multiple transactions rarely touch the same storage key.
Race conditions, reliance on transaction ordering, excessive global variables, and poor rollback handling can undermine performance and security.
