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

1. Overview of Parallel Computing Technology Route

The "Blockchain Trilemma" reveals the essential trade-offs in blockchain system design, namely that blockchain projects find it challenging to achieve "extreme security, universal participation, and high-speed processing" simultaneously. Regarding the eternal topic of "scalability," the mainstream blockchain scaling solutions currently on the market are categorized by paradigm, including:

• Execute enhanced scaling: Improve execution capability on-site, such as parallel processing, GPU, multi-core.
• State-isolated scaling: horizontal partitioning of state / Shard, such as sharding, UTXO, multiple subnets
• Off-chain outsourcing scalability: executing outside the chain, such as Rollup, Coprocessor, DA
• Decoupled structure expansion: 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 chains.

Blockchain scaling 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-layer collaboration, modular combination" complete scaling system. This article focuses on the mainstream scaling method based on parallel computing.

Intra-chain parallelism (, focusing on the parallel execution of transactions/instructions within the block. According to the parallel mechanism, its scalability can be divided into five major categories, each representing different performance pursuits, development models, and architectural philosophies, with the granularity of parallelism becoming finer, parallel intensity increasing, scheduling complexity rising, and programming complexity and implementation difficulty also increasing.

• Account-level parallelism: Represents the Solana project
• Object-level parallelism: represents the project Sui
• Transaction-level: Represents the projects Monad, Aptos
• Call-level / MicroVM: 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 "agent process," asynchronously messaging and event-driven in a parallel manner without the need for synchronized scheduling. Representative projects include AO, ICP, Cartesi, and others.

The well-known Rollup or sharding scalability solutions belong to system-level concurrency mechanisms and do not fall under on-chain parallel computing. They achieve scalability by "running multiple chains/execution domains in parallel" rather than increasing the parallelism within a single block/virtual machine. Such 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 through Compatibility

The development of Ethereum's serial processing architecture has gone through multiple rounds of expansion attempts, including sharding, Rollup, and modular architecture, but the throughput bottleneck of the execution layer has still not achieved a fundamental breakthrough. At the same time, EVM and Solidity remain the most developer-friendly and ecosystem-empowered smart contract platforms. Therefore, EVM-based parallel-enhanced chains, which balance ecological compatibility and execution performance improvement, are becoming an important direction for the next round of expansion 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 from the perspectives of delayed execution and state decomposition, respectively.

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. Additionally, at the consensus and storage layers, Monad introduces a high-performance BFT protocol (MonadBFT) and a dedicated database system (MonadDB), achieving end-to-end optimization.

Pipelining: Multi-stage pipeline parallel execution mechanism

Pipelining is the fundamental concept of Monad parallel execution. Its core idea is to split 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 resulting in improved throughput and reduced latency. These stages include: transaction proposal (Propose), consensus achievement (Consensus), transaction execution (Execution), and block submission (Commit).

Asynchronous Execution: Consensus - Executing Asynchronous Decoupling

In traditional chains, 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". It significantly reduces block time and confirmation delay, making the system more resilient, processing flows more granular, and resource utilization higher.

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 completed, no need to wait for execution to finish.

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.

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, achieving parallelism through delayed state writing and dynamic conflict detection during execution, resembling a performance version of Ethereum. Its maturity makes EVM ecosystem migration easier, acting 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 positions itself as a high-performance parallel execution layer compatible with EVM, which can serve 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, collectively building 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 with each other via asynchronous messaging, rather than synchronous calls, allowing a large number of VMs to execute independently and store independently, achieving natural parallelism.

State Dependency DAG: Dependency Graph Driven Scheduling Mechanism

MegaETH has built a DAG scheduling system based on account state access relationships, which maintains a global Dependency Graph in real-time. Every transaction that modifies or reads certain accounts is modeled as a dependency. Non-conflicting transactions can be executed in parallel, 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 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 that is 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 reconstruction path: completely abstracting accounts and contracts into an independent VM, releasing extreme parallel potential through asynchronous execution scheduling. Theoretically, MegaETH's parallel upper limit is higher, but it is also more challenging 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 splits 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 horizontally scaling 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 scaling path: vertical strengthening and horizontal expansion.

Projects like Monad and MegaETH focus 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, features a 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 main network 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 employs asynchronous processing, 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 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,