Web3 Parallel Computing Track Overview: 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-speed processing." Regarding the eternal topic of "scalability," the mainstream blockchain scaling solutions in the market are categorized by 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 of 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, multithreaded asynchronous chain

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 complete scaling system that is "multi-layer collaborative and modular combination." This article focuses on the mainstream scaling method based on parallel computing.

Intra-chain parallelism (, focuses 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 increasingly finer parallel granularity, higher parallel intensity, greater scheduling complexity, and increased programming complexity and implementation difficulty.

• Account-level parallelism: Represents the project Solana
• Object-level parallelism: represents the Sui project
• 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 concurrent model, represented by the Actor 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," with 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 scalability 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 increasing the parallelism within a single block/virtual machine. Such scalability solutions are not the focus of this discussion, 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 Through Compatibility

Ethereum's serial processing architecture has evolved through multiple rounds of scaling attempts, including sharding, Rollup, and modular architecture, yet the throughput bottleneck at the execution layer remains unresolved fundamentally. Meanwhile, EVM and Solidity continue to be the smart contract platforms with the most developer foundation and ecological potential. Therefore, EVM-based parallel-enhanced chains are emerging as a key path that balances ecological compatibility with 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, each building an EVM parallel processing architecture 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, featuring 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, respectively, 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 divide the execution process of the blockchain into multiple independent stages and to 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

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 granular, and resource utilization more efficient.

Core Design:

• The consensus process (consensus layer) is only responsible for ordering transactions, not executing contract logic.
• The execution process (execution layer) is triggered asynchronously after consensus is reached.
• After the consensus is completed, immediately enter the consensus process for the next block without waiting for execution to finish.

Optimistic Parallel Execution: Optimistic Parallel Execution

Traditional Ethereum adopts 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, 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 deferring state writes and dynamically detecting conflicts, 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 the Parallel Computing Mechanism of MegaETH )

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 Layer or modular component on Ethereum. Its core design goal is to 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 innovations proposed by MegaETH include: Micro-VM architecture + State Dependency DAG (Directed Acyclic Graph of State Dependencies) and modular synchronization mechanism, which together construct 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 smallest isolation unit for parallel scheduling. These VMs communicate with each other through asynchronous messaging, rather than synchronous calls, allowing a large number of VMs to execute and store independently, naturally in 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. Each transaction modifies which accounts and reads which accounts, all modeled as dependency relationships. Conflict-free transactions can be executed in parallel, while transactions with dependencies will be scheduled and sorted in serial order or delayed 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-threaded state machine model by implementing micro virtual machine encapsulation at the account level, scheduling transactions through a state dependency graph, and replacing the synchronous call stack with an asynchronous messaging mechanism. It is a parallel computing platform that is redesigned from the full dimension of "account structure → scheduling architecture → execution process", providing a paradigm-level new approach 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, and releasing extreme parallel potential through asynchronous execution scheduling. Theoretically, MegaETH's parallel upper 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; while both Monad and MegaETH maintain the integrity of the single chain, they horizontally scale only 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 paths in blockchain scalability.

Monad and MegaETH, as parallel computing projects, mainly 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, has its 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 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 in the Pharos architecture, akin to modular subnetworks, specifically designed to handle particular 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 utilizes a restaking protocol (