A Comprehensive Overview of the Web3 Parallel Computing Track: The Best Solution for Native Scaling?

The "Blockchain Trilemma" reveals the essential trade-offs in the design of blockchain systems, namely that it is difficult for blockchain projects to achieve "ultimate security, universal participation, and high-speed processing" simultaneously. In response to the eternal topic of "scalability," the mainstream blockchain scalability solutions currently on the market are classified according to paradigms, including:

• Execute enhanced scaling: Improve execution capabilities on-site, such as parallel processing, GPU, and multi-core.
• State-isolated scaling: horizontal sharding of state/Shard, such as sharding, UTXO, multi-subnet
• Off-chain outsourcing scaling: executing outside the chain, such as Rollup, Coprocessor, DA
• Decoupled structure expansion: modular architecture, collaborative operation, such as module chains, shared sequencers, Rollup Mesh
• Asynchronous concurrent scaling: Actor model, process isolation, message-driven, such as agents, multi-threaded asynchronous chains

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

Intra-chain parallelism (, focuses on the parallel execution of transactions/instructions within the block. According to the parallelism mechanism, its scaling methods can be divided into five 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 Sui project
• Transaction-level: Represents the projects Monad, Aptos
• Call-level / MicroVM parallelism: 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 parallel computing paradigm. As a cross-chain/asynchronous messaging system (non-block synchronization model), each Agent operates as an independent "intelligent agent process", asynchronously messaging in parallel, event-driven, with no need for synchronized scheduling. Representative projects include AO, ICP, Cartesi, etc.

The well-known Rollup or sharding scaling solutions belong to system-level concurrency mechanisms and do not pertain to on-chain parallel computing. They achieve scaling by "running multiple chains/execution domains in parallel" rather than increasing the parallelism within a single block/virtual machine. Such scaling 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-based Parallel Enhanced 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 at the execution layer has still not achieved a fundamental breakthrough. Meanwhile, EVM and Solidity remain the most developer-friendly and ecologically potent smart contract platforms today. As such, EVM-based parallel-enhanced chains are becoming a crucial path for balancing ecological compatibility and improving execution performance, and are emerging as an important direction for the next round of scaling evolution. Monad and MegaETH are the most representative projects in this direction, each building an EVM parallel processing architecture aimed at high concurrency and high throughput scenarios, starting from delayed execution and state decomposition, respectively.

Analysis of Monad's Parallel Computing Mechanism

Monad is a high-performance Layer 1 blockchain re-designed 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. Additionally, at the consensus and storage layers, Monad introduces a high-performance BFT protocol (MonadBFT) and a specialized database system (MonadDB), 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 blockchain execution process 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, enabling cross-block concurrent processing, ultimately achieving the effect of increasing throughput and reducing latency. These stages include: transaction proposal (Propose), consensus achievement (Consensus), transaction execution (Execution), and block commitment (Commit).

Asynchronous Execution: Consensus - Asynchronous Decoupling

In traditional blockchains, transaction consensus and execution are usually synchronous processes, and this serial model severely limits performance scalability. Monad achieves asynchronous consensus layer, asynchronous execution layer, and asynchronous storage through "asynchronous execution." This significantly reduces block time and confirmation delay, making the system more resilient, streamlining processing flows, and increasing resource utilization.

Core Design:

• The consensus process (consensus layer) is only responsible for ordering transactions and does not execute contract logic.
• The execution process (execution layer) is triggered asynchronously after the consensus is completed.
• Immediately enter the consensus process for the next block 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 adopts an "optimistic parallel execution" strategy, significantly increasing 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 (such as 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: to minimize changes to EVM rules, achieving parallel execution by delaying state writes and dynamically detecting conflicts, resembling a performance version of Ethereum. Its maturity allows for easier EVM ecosystem migration, acting 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 serve as an independent L1 public chain or as an execution enhancement layer on Ethereum or a modular component. Its core design goal is to deconstruct the 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 innovation proposed by MegaETH lies in the combination of the Micro-VM architecture + State Dependency DAG (Directed Acyclic Graph of State Dependencies) and a modular synchronization mechanism, collectively constructing a parallel execution system oriented towards "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 minimal isolation unit for parallel scheduling. These VMs communicate with each other through asynchronous messaging instead of synchronous calls, allowing a large number of VMs to execute independently and store data independently, inherently parallel.

State Dependency DAG: Dependency Graph Driven Scheduling Mechanism

MegaETH has built a DAG scheduling system based on account state access relationships. The system maintains a global Dependency Graph in real time, modeling all account modifications and reads from transactions as dependency relationships. Non-conflicting transactions can be executed in parallel directly, while transactions with dependencies will be scheduled and sorted serially or deferred according to topological order. The dependency graph ensures consistency of state 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 by implementing micro virtual machine encapsulation on an account basis, scheduling transactions through a state dependency graph, and replacing synchronous call stacks with an asynchronous messaging mechanism. It is a parallel computing platform redesigned from the "account structure → scheduling architecture → execution process" perspective, providing a paradigm-level new approach for building the next generation of high-performance on-chain systems.

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

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Monad and MegaETH have significantly different design philosophies compared to sharding: sharding horizontally divides a 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 scalability; whereas Monad and MegaETH maintain the integrity of a single chain, only expanding horizontally at the execution layer, optimizing for extreme parallel execution within the single chain to break through performance limits. The two represent vertical strengthening and horizontal expansion as two directions in the blockchain scalability path.

Projects like Monad and MegaETH focus primarily on throughput optimization paths, with the core goal of enhancing on-chain TPS. They achieve transaction-level or account-level parallel processing through Deferred Execution and Micro-VM architecture. Pharos Network, on the other hand, is a modular, full-stack parallel L1 blockchain network, with its core parallel computing mechanism known as "Rollup Mesh." This architecture supports multi-virtual machine environments (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, storage) and employs 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 both EVM and WASM virtual machine environments, allowing developers to choose the appropriate execution environment based on their needs. This dual VM architecture not only enhances the flexibility of the system but also improves transaction processing capabilities through parallel execution.
3. Special Processing Networks (SPNs): SPNs are key components in the Pharos architecture, akin to modular sub-networks specifically designed to handle certain types of tasks or applications. Through SPNs, Pharos can achieve dynamic resource allocation and parallel processing of tasks, further enhancing the system's scalability and performance.
4. Modular Consensus & Restaking: Pharos introduces a flexible consensus mechanism that supports multiple consensus models (such as PBFT, PoS, PoA) and achieves the mainnet and SPNs through the restaking protocol.