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

The "Blockchain Trilemma" reveals the essential trade-offs in blockchain system design concerning "security", "decentralization", and "scalability". It indicates 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 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 Isolation Scalability: Horizontal partitioning of state/Shard, such as sharding, UTXO, multi-subnet
• Off-chain outsourced scaling: execute outside the chain, such as Rollup, Coprocessor, DA
• Decoupled structure expansion: modular architecture, collaborative operation, such as module chains, shared sorters, 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. These cover multiple levels of 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 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 complexity of scheduling also rises, and the complexity of programming and implementation becomes more challenging.

• Account-level parallelism: Represents the project Solana
• Object-level parallelism: Represents the Sui project
• 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 agent 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 independent "agent process" that communicates asynchronously in a parallel manner, driven by events, 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 computation. They achieve scalability by "running multiple chains/execution domains in parallel," rather than enhancing 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 scalability attempts, including sharding, Rollups, and modular architecture. However, the throughput bottleneck at the execution layer has still not achieved a fundamental breakthrough. 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, which balance ecological compatibility and execution performance improvement, are becoming a key direction in the new round of scalability evolution. Monad and MegaETH are the most representative projects in this direction, respectively building EVM parallel processing architectures aimed at high concurrency and high throughput scenarios, starting from 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. Moreover, in 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 parallel execution in Monads. 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, enabling concurrent processing across blocks, ultimately achieving the effect of increasing throughput and reducing latency. These stages include: transaction proposal (Propose), consensus achievement (Consensus), transaction execution (Execution), and block submission (Commit).

Asynchronous Execution: Consensus - Execution 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, processing flows more segmented, and resource utilization more efficient.

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.
• After the consensus is completed, 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 employs 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 (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 facilitates easy migration of the EVM ecosystem, making it 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 compatible with EVM, which can serve as an independent L1 public chain or as an execution enhancement layer or modular component on Ethereum. Its core design goal is to isolate and deconstruct account logic, execution environment, and state into the smallest independently schedulable units, enabling 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 modular synchronization mechanisms, together constructing a parallel execution system aimed at "in-chain threading."

Micro-VM architecture: Account as a thread

MegaETH introduces an execution model of "one micro virtual machine (Micro-VM) per account" that "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: 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 relationship. Non-conflicting transactions can be executed directly 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-redundant writes 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 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 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 refactoring path: completely abstracting accounts and contracts into independent VMs, releasing extreme parallel potential through asynchronous execution scheduling. Theoretically, MegaETH has a higher parallel ceiling, but it is also more difficult to control complexity, resembling a super distributed operating system under the Ethereum philosophy.

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The design concepts of Monad and MegaETH are quite different from sharding: sharding horizontally divides the blockchain into multiple independent sub-chains (shards), with each sub-chain responsible for part of the transactions and states, breaking the limitations of a single chain for scalability at the network layer; while both Monad and MegaETH maintain the integrity of a single chain, only horizontally scaling at the execution layer, optimizing performance through extreme parallel execution within the single chain. The two represent two directions in the blockchain scaling path: vertical reinforcement and horizontal expansion.

Parallel computing projects like Monad and MegaETH mainly focus on throughput optimization paths, aiming to enhance on-chain TPS as the core goal, and 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 multiple virtual machine environments (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, and storage) and adopts an asynchronous processing method, allowing each stage to proceed 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 capability 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 specific 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 various consensus models (such as PBFT, PoS, PoA) and achieves secure sharing and resource integration between the mainnet and SPNs through the Restaking protocol.

In addition, Pharos has reconstructed the execution model from the underlying storage engine through multi-version Merkle trees, Delta Encoding, Versioned Addressing, and ADS Pushdown technology, launching the native blockchain high-performance storage engine Pharos Store, achieving high throughput, low latency, and strong verifiable on-chain processing capabilities.

Overall, Phar