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

1. Overview of the Blockchain Scalability Track

The "Blockchain Trilemma" of security, decentralization, and scalability reveals the essential trade-offs in the design of blockchain systems, meaning that blockchain projects struggle 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 can be categorized according to paradigms, including:

• Execute enhanced scalability: improve execution capabilities on the spot, such as parallelism, GPU, multi-core
• State-isolated scaling: Horizontal partitioning of state / Shard, such as sharding, UTXO, multi-subnet
• Off-chain outsourcing expansion: execute outside the chain, such as Rollup, Coprocessor, DA
• Decoupled architecture 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 scalability solutions include: on-chain parallel computing, Rollup, sharding, DA modules, modular structures, Actor systems, zk proof compression, Stateless architecture, etc., covering multiple levels such as execution, state, data, and structure, forming a "multi-layer collaboration, modular combination" complete scalability system. This article focuses on the mainstream scalability method based on parallel computing.

Intra-chain parallelism ( focuses on the parallel execution of transactions/instructions within the block. Classified by parallel mechanisms, its scalability methods can be divided into five major categories, each representing different performance pursuits, development models, and architectural philosophies. The granularity of parallelism becomes finer in sequence, the intensity of parallelism increases, the complexity of scheduling becomes higher, and the complexity of programming and implementation difficulty also increase.

• Account-level parallelism: Represents the project Solana
• Object-level parallelism: represents the Sui project
• Transaction-level: Represents 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-blockchain synchronization model), each Agent operates as an independent "smart agent process" that communicates asynchronously in parallel, event-driven, and requires no synchronized scheduling. Representative projects include AO, ICP, Cartesi, etc.

The well-known Rollup or sharding scaling solutions belong to system-level concurrency mechanisms, not to on-chain parallel computation. 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 System 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. However, the throughput bottleneck of the execution layer has still not been fundamentally overcome. At the same time, EVM and Solidity remain the most developer-friendly and ecologically potent smart contract platforms today. Therefore, EVM-based parallel enhancement chains are becoming a key path that balances ecological compatibility and execution performance improvement, emerging as an important direction for the new round of scaling evolution. Monad and MegaETH are the most representative projects in this direction, each constructing an EVM parallel processing architecture aimed at high concurrency and high throughput scenarios, with a focus on 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 execution process of the blockchain into multiple independent stages and process these stages in parallel, forming a three-dimensional pipeline architecture. Each stage runs on independent threads or cores, achieving concurrent processing across blocks, ultimately enhancing 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 layer, asynchronous execution layer, and asynchronous storage through "asynchronous execution." This significantly reduces block time and confirmation delays, making the system more resilient, with more granular processing flows and higher 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 consensus is reached.
• After the consensus is reached, immediately enter the consensus process for the next block without waiting for execution to complete.

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 enhancing transaction processing speed.

Implementation Mechanism:

• Monad will optimistically execute all transactions in parallel, assuming that there are no state conflicts between most transactions.
• Run a "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, achieving parallelism by delaying state writes and dynamically detecting conflicts during execution. It resembles a performance version of Ethereum, with good maturity that facilitates EVM ecosystem migration, serving as a parallel accelerator in the EVM world.

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) Analysis of the Parallel Computing Mechanism of MegaETH

Unlike the L1 positioning of Monad, MegaETH is positioned as a modular high-performance parallel execution layer that is EVM compatible, capable of functioning 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 isolation into independently schedulable minimal units to achieve high concurrent execution and low-latency response capabilities within the chain. The key innovation proposed by MegaETH lies in the combination of Micro-VM architecture + State Dependency DAG (Directed Acyclic Graph of State Dependencies) and modular synchronization mechanisms, collectively constructing a parallel execution system aimed at "on-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 minimum isolation unit for parallel scheduling. These VMs communicate through asynchronous messaging rather than synchronous calls, allowing a large number of VMs to execute and store independently, inherently parallel.

State Dependency DAG: A Scheduling Mechanism Driven by Dependency Graphs

MegaETH has built a DAG scheduling system based on account state access relations, which maintains a global dependency graph in real-time. Each transaction models the accounts modified and the accounts read as dependency relations. Non-conflicting transactions can be executed in parallel directly, while transactions with dependencies will be scheduled in a serial or delayed manner according to topological order. 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-thread state machine model by 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 has been redesigned in all dimensions from "account structure → scheduling architecture → execution process", providing a paradigm-level new idea for building the next generation of high-performance on-chain systems.

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

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Monad and MegaETH have significantly different design philosophies compared to 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 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 two directions in the blockchain scalability path: vertical enhancement and horizontal expansion.

Projects like Monad and MegaETH focus primarily on optimizing throughput paths to enhance on-chain TPS as their core goal. They achieve transaction-level or account-level parallel processing through Deferred Execution and a 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), and integrates 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 adopts an asynchronous processing approach, allowing each stage to be carried out 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 flexibility of the system but also improves transaction processing capacity through parallel execution.
3. Special Processing Networks (SPNs): SPNs are key components in the Pharos architecture, similar to modular sub-networks, 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 multiple consensus models (such as PBFT, PoS)