Web3 Parallel Computing Track Panorama: The Best Solution for Native Scalability?

The "Blockchain Trilemma"—"Security," "Decentralization," and "Scalability"—reveals the essential trade-offs in the design of blockchain systems, which means that blockchain projects find it difficult to achieve "extreme security, universal participation, and high-speed processing" simultaneously. Regarding the eternal topic of "Scalability," the mainstream blockchain expansion solutions currently on the market are categorized according to paradigms, including:

• Execute enhanced scaling: improve execution capability on the spot, such as parallelism, GPU, multi-core.
• State-isolation scalability: horizontal partitioning of state / Shard, such as sharding, UTXO, multi-subnet
• Off-chain outsourcing scalability: Execute off-chain, such as Rollup, Coprocessor, DA
• Decoupled expansion structure: modular architecture, collaborative operation, such as modular chains, shared sequencers, Rollup Mesh
• Asynchronous Concurrent Scaling: Actor Model, Process Isolation, Message Driven, such as Agents, Multi-threaded Asynchronous Chain

Blockchain scalability solutions include: on-chain parallel computing, Rollup, sharding, DA modules, modular architecture, Actor systems, zk proof compression, Stateless architecture, etc., covering multiple levels of execution, state, data, and structure, forming a "multi-layered collaboration and modular combination" complete scalability system. This article focuses on the scalability method primarily based on parallel computing.

Intra-chain parallelism (, focuses on the parallel execution of transactions/instructions within the block. According to the parallel mechanism, its scaling methods can be divided into five major categories, each representing different performance pursuits, development models, and architectural philosophies. The granularity of parallelism becomes finer, the intensity of parallelism increases, the scheduling complexity also rises, and the programming complexity and implementation difficulty become higher.

• Account-level parallelism: Represents the project Solana
• Object-level parallelism: represents the project Sui
• Transaction-level: Represents the projects Monad, Aptos
• Call-level / MicroVM parallel: represents the project MegaETH
• Instruction-level Parallelism: Represents the project GatlingX

The off-chain asynchronous concurrency model, represented by the Actor system (Agent / Actor Model), belongs to another paradigm of parallel computing. As a cross-chain / asynchronous messaging system (non-block synchronization model), each Agent operates as an independently running "smart agent process," using asynchronous messaging in a parallel manner, event-driven, and without the need for synchronized scheduling. Representative projects include AO, ICP, Cartesi, etc.

The well-known Rollup or sharding scalability solutions belong to system-level concurrency mechanisms, and do not fall under on-chain parallel computation. They achieve scalability by "running multiple chains/execution domains in parallel," rather than increasing the parallelism within a single block/virtual machine. These scalability solutions are not the focus of this article, but we will still use them for comparative analysis of architectural concepts.

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2. EVM System Parallel Enhancement Chain: Breaking Performance Boundaries in Compatibility

The development of Ethereum's serial processing architecture has gone through multiple rounds of scaling attempts, including sharding, Rollup, and modular architecture, but the throughput bottleneck of the execution layer has still not been fundamentally broken through. However, at the same time, EVM and Solidity remain the most developer-friendly and ecologically potent smart contract platforms. Therefore, EVM-based parallel enhancement chains, which balance ecological compatibility and execution performance improvement, are becoming an important direction for the new round of scaling evolution. Monad and MegaETH are the most representative projects in this direction, building EVM parallel processing architectures aimed at high concurrency and high throughput scenarios, respectively, from the perspectives of delayed execution and state decomposition.

Analysis of Monad's Parallel Computing Mechanism )

Monad is a high-performance Layer 1 blockchain redesigned for the Ethereum Virtual Machine (EVM), based on the fundamental parallel concept of pipelining, with asynchronous execution at the consensus layer and optimistic parallel execution at the execution layer. In addition, Monad introduces a high-performance BFT protocol (MonadBFT) and a dedicated database system (MonadDB) at the consensus and storage layers, achieving end-to-end optimization.

Pipelining: Multi-stage pipeline parallel execution mechanism

Pipelining is the fundamental concept of parallel execution in Monads. Its core idea is to break down the execution process of the blockchain into multiple independent stages and to process these stages in parallel, forming a three-dimensional pipeline architecture. Each stage runs on independent threads or cores, achieving cross-block concurrent processing, ultimately improving throughput and reducing latency. These stages include: transaction proposal (Propose), consensus achievement (Consensus), transaction execution (Execution), and block submission (Commit).

Asynchronous Execution: Consensus - Asynchronous Decoupling of Execution

In traditional blockchains, transaction consensus and execution are usually synchronous processes, and this serial model severely limits performance scalability. Monad achieves asynchronous consensus, asynchronous execution, and asynchronous storage by means of "asynchronous execution." This significantly reduces block time and confirmation delay, making the system more resilient, the processing flow more subdivided, and resource utilization more efficient.

Core Design:

• The consensus process (consensus layer) is only responsible for ordering transactions, not executing contract logic.
• The execution process (execution layer) is triggered asynchronously after consensus is reached.
• Enter the consensus process for the next block immediately after consensus is reached, without waiting for execution to complete.

Optimistic Parallel Execution: Optimistic Parallel Execution

Traditional Ethereum uses a strict serial model for transaction execution to avoid state conflicts. In contrast, Monad employs an "optimistic parallel execution" strategy, significantly enhancing transaction processing speed.

Execution mechanism:

• Monad will optimistically execute all transactions in parallel, assuming that most transactions have no state conflicts.
• Run a "Conflict Detector (Conflict Detector###)" simultaneously to monitor whether transactions access the same state (e.g., read/write conflicts).
• If a conflict is detected, the conflicting transactions will be serialized and re-executed to ensure state correctness.

Monad has chosen a compatible path: minimizing changes to EVM rules, implementing parallelism through delayed state writes and dynamic conflict detection during execution, resembling a performance-enhanced Ethereum. With good maturity, it facilitates the migration of the EVM ecosystem and acts as a parallel accelerator in the EVM world.

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) Analysis of MegaETH's Parallel Computing Mechanism

Unlike the L1 positioning of Monad, MegaETH is positioned as a modular high-performance parallel execution layer that is EVM-compatible. It can function as an independent L1 public chain or as an execution enhancement layer on Ethereum, or as a modular component. Its core design goal is to deconstruct account logic, execution environment, and state into independently schedulable minimal units, to achieve high concurrency execution and low latency response capabilities within the chain. The key innovations proposed by MegaETH are: Micro-VM architecture + State Dependency DAG (Directed Acyclic Graph of State Dependencies) and a modular synchronization mechanism, which together construct a parallel execution system aimed at "in-chain threading."

Micro-VM Architecture: Account as Thread

MegaETH introduces an execution model of "one micro virtual machine (Micro-VM) per account," which "threads" the execution environment, providing the smallest isolation unit for parallel scheduling. These VMs communicate through asynchronous messaging instead of synchronous calls, allowing a large number of VMs to execute independently and store independently, naturally achieving parallelism.

State Dependency DAG: A scheduling mechanism driven by dependency graphs

MegaETH has built a DAG scheduling system based on account state access relationships, which maintains a global dependency graph in real-time. Each transaction models which accounts are modified and which accounts are read as dependency relationships. Non-conflicting transactions can be executed in parallel, while transactions with dependencies will be scheduled in a serial or deferred order according to topological sorting. The dependency graph ensures state consistency and non-repetitive writing during the parallel execution process.

Asynchronous Execution and Callback Mechanism

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In summary, MegaETH breaks the traditional EVM single-threaded state machine model, implementing micro virtual machine encapsulation at the account level, scheduling transactions through a state dependency graph, and replacing synchronous call stacks with an asynchronous messaging mechanism. It is a parallel computing platform that is redesigned across all dimensions from "account structure → scheduling architecture → execution process," providing a paradigm-level new approach for building the next generation of high-performance on-chain systems.

MegaETH has chosen a reconstruction path: thoroughly abstracting accounts and contracts into independent VMs, and releasing extreme parallel potential through asynchronous execution scheduling. Theoretically, MegaETH's parallel limit is higher, but it is also more difficult to control the complexity, resembling a super-distributed operating system under the Ethereum philosophy.

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The design concepts of Monad and MegaETH differ significantly from sharding: sharding horizontally divides the blockchain into multiple independent sub-chains (shards), with each sub-chain responsible for a portion of transactions and states, breaking the limitations of a single chain for network layer scaling; whereas Monad and MegaETH maintain the integrity of a single chain, only horizontally scaling at the execution layer, achieving optimization breakthroughs in performance through extreme parallel execution within the single chain. The two represent two directions in the blockchain scaling path: vertical reinforcement and horizontal expansion.

Projects like Monad and MegaETH focus primarily on throughput optimization paths, aiming to enhance on-chain TPS as the core goal. They achieve transaction-level or account-level parallel processing through Deferred Execution and Micro-VM architecture. Pharos Network, as a modular, full-stack parallel L1 blockchain network, has its core parallel computing mechanism known as "Rollup Mesh." This architecture supports a multi-virtual machine environment (EVM and Wasm) through the collaborative work of the mainnet and Special Processing Networks (SPNs), integrating advanced technologies such as Zero-Knowledge Proofs (ZK) and Trusted Execution Environments (TEE).

Analysis of the Rollup Mesh Parallel Computing Mechanism:

1. Full Lifecycle Asynchronous Pipelining: Pharos decouples the various stages of a transaction (such as consensus, execution, and storage) and adopts an asynchronous processing method, allowing each stage to operate independently and in parallel, thereby improving overall processing efficiency.
2. Dual VM Parallel Execution: Pharos supports two virtual machine environments, EVM and WASM, allowing developers to choose the appropriate execution environment based on their needs. This dual VM architecture not only enhances the system's flexibility but also improves transaction processing capacity through parallel execution.
3. Special Purpose Networks (SPNs): SPNs are key components of the Pharos architecture, similar to modular subnetworks specifically designed to handle certain types of tasks or applications. Through SPNs, Pharos can achieve dynamic resource allocation and parallel task processing, further enhancing the system's scalability and performance.
4. Modular Consensus & Restaking: Pharos introduces a flexible consensus mechanism that supports various consensus models (such as PBFT, PoS, PoA) and through the restaking protocol (