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

1. The Eternal Topic of Blockchain Scalability

The "Blockchain Trilemma" of "security", "decentralization", and "scalability" reveals the essential trade-offs in the design of blockchain systems, meaning that it is challenging for blockchain projects to achieve "ultimate security, universal participation, and high-speed processing" simultaneously. Regarding the eternal topic of "scalability", the mainstream blockchain scaling solutions currently on the market can be classified according to paradigms, including:

• Execute enhanced scalability: Improve execution capability on the spot, such as parallel processing, GPU, and multi-core.
• State-isolation scaling: horizontal partitioning of state/Shard, such as sharding, UTXO, multiple subnets
• Off-chain outsourcing expansion: 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, multithreaded asynchronous chains

Blockchain scaling solutions include: on-chain parallel computation, 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 complete scaling system of "multi-layer collaboration and modular combination." This article focuses on the mainstream scaling method based on parallel computation.

Intra-chain parallelism (, focuses on the parallel execution of transactions/instructions within the block. According to the parallel mechanism classification, its scalability can be divided into five major categories, each representing different performance pursuits, development models, and architectural philosophies. In order, 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 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 acts as an independently running "intelligent 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 scaling 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 Enhanced Chain: Breaking Performance Boundaries in Compatibility

The serial processing architecture of Ethereum has evolved 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. Meanwhile, EVM and Solidity remain the most developer-friendly and ecologically potent smart contract platforms. Therefore, EVM-based parallel enhancement chains serve as a key pathway that balances ecological compatibility and improved execution performance, becoming an important direction for the next 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, based on 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, at the consensus and storage layers, Monad introduces a high-performance BFT protocol (MonadBFT) and a dedicated database system (MonadDB) to achieve 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 decompose 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 cross-block concurrent processing, ultimately enhancing 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, 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.
• 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 improving 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: to change EVM rules as little as possible, achieving parallelism through delayed state writing and dynamic conflict detection during execution. It resembles a performance version of Ethereum, with high 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. It can serve as an independent L1 public chain or as an execution enhancement layer on Ethereum (Execution Layer) or a modular component. Its core design goal is to isolate and deconstruct account logic, execution environment, and state 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 a Micro-VM architecture + State Dependency DAG (Directed Acyclic Graph of State Dependencies) and a modular synchronization mechanism, which collectively build 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 minimal isolation unit for parallel scheduling. These VMs communicate through asynchronous messaging rather than synchronous calls, allowing a large number of VMs to execute independently and store independently, resulting in 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. 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 and sorted in a topological order serially or deferred. The dependency graph ensures state consistency and non-repetitive writes 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. It schedules transactions through a state dependency graph and replaces synchronous call stacks with an asynchronous messaging mechanism. It is a parallel computing platform that is redesigned from the "account structure → scheduling architecture → execution flow" across all dimensions, providing a paradigm-level new idea for building the next generation of high-performance on-chain systems.

MegaETH has chosen a path of reconstruction: completely abstracting accounts and contracts into independent VMs, leveraging asynchronous execution scheduling to unleash extreme parallel potential. 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 both Monad and MegaETH maintain the integrity of a single chain and only horizontally scale at the execution layer, achieving performance breakthroughs through extreme parallel execution optimization within the single chain. The two represent vertical reinforcement and horizontal expansion in the blockchain scalability paths.

Parallel computing projects such as Monad and MegaETH primarily focus on optimizing throughput, 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, as a modular, full-stack parallel L1 blockchain network, has a core parallel computing mechanism known as "Rollup Mesh." This architecture supports multi-virtual machine environments (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 adopts an asynchronous processing approach, 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 according to 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 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 multiple consensus models (such as PBFT, PoS, PoA), and through restaking, it...