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 simultaneously achieve "extreme security, universal participation, and high processing speed". Regarding the eternal topic of "scalability", the mainstream blockchain scaling solutions currently available on the market are categorized according to paradigms, including:

• Execute enhanced scalability: improve execution capabilities on-site, such as parallel processing, GPU, and multi-core.
• State-isolated scaling: horizontal partitioning of state / Shard, such as sharding, UTXO, multiple subnets
• Off-chain outsourced 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 Scalability: 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 of execution, state, data, and structure, forming a complete scalability system of "multi-layer collaboration and modular combination." This article focuses on the mainstream scalability method based on parallel computing.

Intra-chain parallelism ( refers to the parallel execution of transactions / instructions within a block. According to the parallelism mechanisms, its scalability can be divided into five major 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 also escalate.

• 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 "agent process," asynchronously messaging in a parallel manner, event-driven, without the need for synchronized scheduling. Representative projects include AO, ICP, Cartesi, etc.

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 enhancing the degree of parallelism within a single block / virtual machine. These 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 in Compatibility

The development of Ethereum's serial processing architecture has gone through multiple rounds of scalability attempts, including sharding, Rollups, and modular architecture, but the throughput bottleneck at the execution layer has yet to be fundamentally broken. At the same time, EVM and Solidity remain the most developer-friendly and ecologically potent smart contract platforms. Therefore, the EVM-based parallel enhancement chain, which balances ecological compatibility and execution performance improvement, is becoming an important direction for the new round of scalability 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, from the perspectives of 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. It features asynchronous execution at the consensus layer and optimistic parallel execution at the execution layer. Additionally, Monad introduces a high-performance BFT protocol (MonadBFT) and a dedicated database system (MonadDB) at the consensus and storage layers, 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 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 improving throughput and reducing 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 blockchains, transaction consensus and execution are usually synchronous processes, and this serial model severely limits performance scalability. Monad achieves asynchronous consensus, asynchronous execution, and asynchronous storage through "asynchronous execution". This significantly reduces block time and confirmation latency, making the system more resilient, processes more granular, and resource utilization more efficient.

Core Design:

• The consensus process (consensus layer) is responsible only 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 the consensus is completed, without waiting 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 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: minimizing changes to EVM rules, achieving parallelism during execution by delaying state writes and dynamically detecting conflicts, resembling a performance version of Ethereum. Its maturity facilitates 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 function 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 deconstruct the 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: Micro-VM architecture + State Dependency DAG (Directed Acyclic Graph of State Dependencies) and modular synchronization mechanisms, together constructing a parallel execution system oriented towards "in-chain threading."

Micro-VM architecture: Account is a thread

MegaETH introduces an execution model of "one Micro-VM per account," which "threads" the execution environment, providing the smallest 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 independently, resulting in natural parallelism.

State Dependency DAG: A scheduling mechanism driven by dependency graphs

MegaETH has built a DAG scheduling system based on account state access relationships, which maintains a global Dependency Graph in real-time. Each transaction modifies which accounts and reads from which accounts, all modeled as dependency relationships. Non-conflicting transactions can be executed in parallel, while transactions with dependencies will be scheduled in a topological order serially or delayed. 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, encapsulating micro virtual machines 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 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 has a higher parallel limit, but it is also harder 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 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 and expanding at the network layer; while both Monad and MegaETH maintain the integrity of a single chain and only horizontally scale 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 in the blockchain scaling path.

Monad and MegaETH and other parallel computing projects mainly focus on throughput optimization paths, with the core goal of improving 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 its core parallel computing mechanism known as "Rollup Mesh." This architecture supports multi-virtual machine environments (EVM and Wasm) through the cooperative 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 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 method, 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 capabilities through parallel execution.
3. Special Processing Networks (SPNs): SPNs are key components of the Pharos architecture, similar to modular subnetworks, specifically designed for handling 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 implements the mainnet and SPNs through the Restaking protocol.