Web3 Parallel Computing Depth Research Report: The Ultimate Path to Native Scalability

I. Introduction: Scalability is an eternal proposition, and parallelism is the ultimate battlefield.

From the inception of Bitcoin, the blockchain system has always faced an unavoidable core issue: scalability. Bitcoin processes less than 10 transactions per second, and Ethereum also struggles to break through the performance bottleneck of several dozen TPS (transactions per second), which appears particularly cumbersome compared to the tens of thousands of TPS in the traditional Web2 world. More importantly, this is not a problem that can be solved simply by “adding servers”; rather, it is a systemic limitation deeply embedded in the underlying consensus and structural design of blockchain—namely, the blockchain trilemma that states that “decentralization, security, and scalability” cannot all be achieved simultaneously.

Over the past decade, we’ve seen countless expansion attempts rise and fall. From the Bitcoin scaling war to the Ethereum sharding vision, from state channels and plasma to rollups and modular blockchains, from off-chain execution in Layer 2 to structural refactoring of Data Availability, the entire industry has embarked on a path of scaling full of engineering imagination. As the most widely accepted scaling paradigm, Rollup has achieved the goal of significantly increasing TPS while reducing the execution burden of the main chain and preserving the security of Ethereum. But it doesn’t touch the real limits of the blockchain’s underlying “single-chain performance”, especially at the execution level – that is, the throughput of the block itself – which is still limited by the ancient processing paradigm of on-chain serial computation.

Because of this, intra-chain parallel computing has gradually entered the industry’s field of vision. Different from off-chain scaling and cross-chain distribution, intra-chain parallelism attempts to completely reconstruct the execution engine while maintaining the atomicity and integrated structure of a single chain, and upgrades the blockchain from a single-threaded mode of “serial execution of one transaction by one” to a high-concurrency computing system of “multi-threading + pipeline + dependency scheduling” under the guidance of modern operating system and CPU design. Such a path may not only achieve a hundred-fold increase in throughput, but also may become a key prerequisite for the explosion of smart contract applications.

In fact, in the Web2 computing paradigm, single-threaded computing has long been eliminated by modern hardware architectures, replaced by a plethora of optimization models such as parallel programming, asynchronous scheduling, thread pools, and microservices. However, blockchain, as a more primitive and conservative computing system with extremely high requirements for determinism and verifiability, has never fully utilized these parallel computing concepts. This is both a limitation and an opportunity. New chains like Solana, Sui, and Aptos have introduced parallelism at the architectural level, pioneering this exploration; while emerging projects like Monad and MegaETH have further elevated in-chain parallelism to breakthroughs in pipeline execution, optimistic concurrency, and asynchronous message-driven mechanisms, exhibiting characteristics increasingly akin to modern operating systems.

It can be said that parallel computing is not only a “performance optimization method” but also a turning point in the paradigm of blockchain execution models. It challenges the fundamental model of smart contract execution and redefines the basic logic of transaction packaging, state access, call relationships, and storage layout. If Rollup is to “execute transactions off-chain,” then on-chain parallelism is to “build supercomputing kernels on-chain,” with the goal not simply to improve throughput but to provide truly sustainable infrastructure support for future Web3 native applications—high-frequency trading, game engines, AI model execution, on-chain social interactions, and more.

As the Rollup track gradually becomes homogenized, on-chain parallelism is quietly becoming a decisive variable in the competition for the new cycle of Layer 1. Performance is no longer just about being “faster,” but about the possibility of supporting an entire heterogeneous application world. This is not just a technical competition; it is also a battle for paradigms. The next-generation sovereign execution platform in the Web3 world is likely to emerge from this struggle of on-chain parallelism.

II. Overview of Expansion Paradigms: Five Types of Routes, Each with Its Focus

Capacity expansion, as one of the most important, sustained and difficult topics in the evolution of public chain technology, has given birth to the emergence and evolution of almost all mainstream technology paths in the past decade. Starting from the battle over the block size of Bitcoin, this technical competition on “how to make the chain run faster” finally divided into five basic routes, each of which cuts into the bottleneck from a different angle, with its own technical philosophy, landing difficulty, risk model and applicable scenarios.

The first route is the most direct on-chain scaling, which means increasing the block size, shortening the block time, or improving the processing power by optimizing the data structure and consensus mechanism. This approach has been the focus of the Bitcoin scaling debate, giving rise to the forks of “big block” factions such as BCH and BSV, and also influencing the design ideas of early high-performance public chains such as EOS and NEO. The advantage of this kind of route is that it retains the simplicity of single-chain consistency, which is easy to understand and deploy, but it is also very easy to touch the systemic upper limits such as centralization risks, rising node operating costs, and increased synchronization difficulties, so it is no longer the mainstream core solution in today’s design, but has become more of an auxiliary collocation of other mechanisms.

The second type of route is off-chain scaling, which is represented by state channels and sidechains. The basic idea of this type of path is to move most of the transaction activity off-chain, and only write the final result to the main chain, which acts as the final settlement layer. In terms of technical philosophy, it is close to the asynchronous architecture of Web2 - try to leave heavy transaction processing at the periphery, and the main chain does minimal trusted verification. Although this idea can theoretically be infinitely scalable, the trust model, fund security, and interaction complexity of off-chain transactions limit its application. For example, although Lightning Network has a clear positioning of financial scenarios, the scale of the ecosystem has never exploded. However, multiple sidechain-based designs, such as Polygon POS, not only have high throughput, but also expose the disadvantages of difficult inheritance of the security of the main chain.

The third type of route is the most popular and widely deployed Layer 2 rollup route. This method does not directly change the main chain itself, but scales through the mechanism of off-chain execution and on-chain verification. Optimistic Rollup and ZK Rollup have their own advantages: the former is fast to implement and highly compatible, but it has the problems of challenge period delay and fraud proof mechanism; The latter has strong security and good data compression capabilities, but it is complex to develop and lacks EVM compatibility. Regardless of the type of rollup, its essence is to outsource the execution power, while keeping the data and verification on the main chain, to achieve a relative balance between decentralization and high performance. The rapid growth of projects such as Arbitrum, Optimism, zkSync, and StarkNet proves the feasibility of this path, but it also exposes medium-term bottlenecks such as excessive reliance on data availability (DA), high costs, and fragmented development experience.

The fourth type of route is the modular blockchain architecture that has emerged in recent years, such as Celestia, Avail, EigenLayer, etc. The modular paradigm advocates the complete decoupling of the core functions of the blockchain - execution, consensus, data availability, and settlement - by multiple specialized chains to complete different functions, and then combine them into a scalable network with a cross-chain protocol. This direction is strongly influenced by the modular architecture of the operating system and the concept of cloud computing composability, which has the advantage of being able to flexibly replace system components and greatly improve efficiency in specific areas such as DA. However, the challenges are also very obvious: the cost of synchronization, verification, and mutual trust between systems after module decoupling is extremely high, the developer ecosystem is extremely fragmented, and the requirements for medium- and long-term protocol standards and cross-chain security are much higher than those of traditional chain design. In essence, this model no longer builds a “chain”, but builds a “chain network”, which puts forward an unprecedented threshold for the overall architecture understanding and operation and maintenance.

The last type of route, which is the focus of the subsequent analysis in this paper, is the intra-chain parallel computing optimization path. Unlike the first four types of “horizontal splitting”, which mainly carry out “horizontal splitting” from the structural level, parallel computing emphasizes “vertical upgrade”, that is, the concurrent processing of atomic transactions is realized by changing the architecture of the execution engine within a single chain. This requires rewriting the VM scheduling logic and introducing a complete set of modern computer system scheduling mechanisms, such as transaction dependency analysis, state conflict prediction, parallelism control, and asynchronous calling. Solana is the first project to implement the concept of parallel VM into a chain-level system, which realizes multi-core parallel execution through transaction conflict judgment based on the account model. The new generation of projects, such as Monad, Sei, Fuel, MegaETH, etc., further try to introduce cutting-edge ideas such as pipeline execution, optimistic concurrency, storage partitioning, and parallel decoupling to build high-performance execution cores similar to modern CPUs. The core advantage of this direction is that it does not need to rely on the multi-chain architecture to achieve a breakthrough in the throughput limit, and at the same time provides sufficient computing flexibility for the execution of complex smart contracts, which is an important technical prerequisite for future application scenarios such as AI Agent, large-scale chain games, and high-frequency derivatives.

Looking at the above five types of scaling paths, the division behind them is actually the systematic trade-off between performance, composability, security, and development complexity of the blockchain. Rollup is strong in consensus outsourcing and secure inheritance, modularity highlights structural flexibility and component reuse, off-chain scaling attempts to break through the bottleneck of the main chain but the trust cost is high, and intra-chain parallelism focuses on the fundamental upgrade of the execution layer, trying to approach the performance limit of modern distributed systems without destroying the consistency of the chain. It is impossible for each path to solve all problems, but it is these directions that together form a panorama of the Web3 computing paradigm upgrade, and also provide developers, architects, and investors with extremely rich strategic options.

Just as operating systems have evolved from single-core to multi-core, and databases have progressed from sequential indexing to concurrent transactions, the scaling path of Web3 will ultimately lead to an era of highly parallel execution. In this era, performance is no longer just a competition of chain speed, but a comprehensive reflection of underlying design philosophy, depth of architectural understanding, hardware-software collaboration, and system control capabilities. And in-chain parallelism may be the ultimate battlefield of this long-term war.

Three, Classification Map of Parallel Computing: Five Major Paths from Accounts to Instructions

In the context of the continuous evolution of blockchain scaling technology, parallel computing has gradually become the core path for performance breakthroughs. Different from the horizontal decoupling of the structure layer, the network layer or the data availability layer, parallel computing is a deep mining at the execution layer, which is related to the lowest logic of the operation efficiency of the blockchain, and determines the response speed and processing capacity of a blockchain system in the face of high concurrency and multi-type complex transactions. Starting from the execution model and reviewing the development of this technology lineage, we can sort out a clear classification map of parallel computing, which can be roughly divided into five technical paths: account-level parallelism, object-level parallelism, transaction-level parallelism, virtual machine-level parallelism, and instruction-level parallelism. These five types of paths, from coarse-grained to fine-grained, are not only the continuous refinement process of parallel logic, but also the path of increasing system complexity and scheduling difficulty.

The earliest occurrence of account-level parallelism is the paradigm represented by Solana. This model is based on the account-state decoupling design, and determines whether there is a conflicting relationship through static analysis of the set of accounts involved in the transaction. If the set of accounts accessed by two transactions does not overlap with each other, they can be executed concurrently on multiple cores. This mechanism is ideal for dealing with well-structured transactions with clear inputs and outputs, especially for programs with predictable paths such as DeFi. However, its natural assumption is that account access is predictable and state dependence can be statically inferred, which makes it prone to conservative execution and reduced parallelism in the face of complex smart contracts (such as dynamic behaviors such as chain games and AI agents). In addition, the cross-dependency between accounts also makes parallel returns severely weakened in certain high-frequency trading scenarios. Solana’s runtime is highly optimized in this regard, but its core scheduling strategy is still limited by account granularity.

Further refinement on the basis of the account model, we enter the technical level of object-level parallelism. Object-level parallelism introduces semantic abstraction of resources and modules, with concurrent scheduling in units of more fine-grained “state objects”. Aptos and Sui are important explorators in this direction, especially the latter, which defines the ownership and variability of resources at compile time through the Move language’s linear type system, allowing the runtime to precisely control resource access conflicts. Compared with account-level parallelism, this method is more versatile and scalable, can cover more complex state read and write logic, and naturally serves highly heterogeneous scenarios such as games, social networking, and AI. However, object-level parallelism also introduces a higher language threshold and development complexity, and Move is not a direct replacement for Solidity, and the high cost of ecological switching limits the popularization of its parallel paradigm.

Further transaction-level parallelism is the direction explored by the new generation of high-performance chains represented by Monad, Sei, and Fuel. The path no longer treats states or accounts as the smallest unit of parallelism, but instead builds a dependency graph around the entire transaction itself. It treats transactions as atomic units of operation, builds transaction graphs (Transaction DAGs) through static or dynamic analysis, and relies on schedulers for concurrent flow execution. This design allows the system to maximize mining parallelism without having to fully understand the underlying state structure. Monad is particularly eye-catching, combining modern database engine technologies such as Optimistic Concurrency Control (OCC), parallel pipeline scheduling, and out-of-order execution, bringing chain execution closer to the “GPU scheduler” paradigm. In practice, this mechanism requires extremely complex dependency managers and conflict detectors, and the scheduler itself may also become a bottleneck, but its potential throughput capacity is much higher than that of the account or object model, making it the most theoretical force in the current parallel computing track.

Virtual machine-level parallelism, on the other hand, embeds concurrent execution capabilities directly into the underlying instruction scheduling logic of the VM, striving to completely break through the inherent limitations of EVM sequence execution. As a “super virtual machine experiment” within the Ethereum ecosystem, MegaETH is trying to redesign the EVM to support multi-threaded concurrent execution of smart contract code. The underlying layer allows each contract to run independently in different execution contexts through mechanisms such as segmented execution, state segmentation, and asynchronous invocation, and ensures eventual consistency with the help of a parallel synchronization layer. The most difficult part of this approach is that it must be fully compatible with the existing EVM behavior semantics, and at the same time transform the entire execution environment and gas mechanism in order to smoothly migrate the Solidity ecosystem to a parallel framework. The challenge is not only the depth of the technology stack, but also the acceptance of significant protocol changes to Ethereum’s L1 political structure. But if successful, MegaETH promises to be a “multi-core processor revolution” in the EVM space.

The last type of path is instruction-level parallelism, which is the most fine-grained and has the highest technical threshold. The idea is derived from the Out-of-Order Execution and Instruction Pipelines in modern CPU design. This paradigm argues that since every smart contract is eventually compiled into bytecode instructions, it is entirely possible to schedule and rearrange each operation in the same way that a CPU executes an x86 instruction set. The Fuel team has initially introduced an instruction-level reorderable execution model in its FuelVM, and in the long run, once the blockchain execution engine realizes the predictive execution and dynamic rearrangement of instruction dependents, its parallelism will reach the theoretical limit. This approach may even take blockchain-hardware co-design to a whole new level, making the chain a true “decentralized computer” rather than just a “distributed ledger”. Of course, this path is still in the theoretical and experimental stage, and the relevant schedulers and security verification mechanisms are not yet mature, but it points to the ultimate boundary of the future of parallel computing.

In summary, the five paths of account, object, transaction, VM, and instruction constitute the development spectrum of intra-chain parallel computing, from static data structure to dynamic scheduling mechanism, from state access prediction to instruction-level rearrangement, each step of parallel technology means a significant increase in system complexity and development threshold. But at the same time, they also mark a paradigm shift in blockchain computing models, from the traditional full-sequence consensus ledger to a high-performance, predictable, and dispatchable distributed execution environment. This is not only a catch-up with the efficiency of Web2 cloud computing, but also a deep conception of the ultimate form of “blockchain computer”. The selection of parallel paths for different public chains will also determine the bearer limit of their future application ecosystems, as well as their core competitiveness in scenarios such as AI Agent, chain games, and on-chain high-frequency trading.

4. In-depth Analysis of Two Major Tracks: Monad vs MegaETH

Among the multiple paths of parallel computing evolution, the two main technical routes with the most focus, the highest voice, and the most complete narrative in the current market are undoubtedly the “building parallel computing chain from scratch” represented by Monad and the “parallel revolution within EVM” represented by MegaETH. These two are not only the most intensive R&D directions for current cryptographic primitive engineers, but also the most definitive polar symbols in the current Web3 computer performance race. The difference between the two lies not only in the starting point and style of the technical architecture, but also in the ecological objects they serve, the migration cost, the execution philosophy and the future strategic path behind them. They represent a parallel paradigm competition between “reconstructionism” and “compatibilityism”, and have profoundly influenced the market’s imagination of the final form of high-performance chains.

A “computational fundamentalist” through and through, Monad’s design philosophy is not designed to be compatible with existing EVMs, but rather to redefine the way blockchain execution engines run under the hood, drawing inspiration from modern databases and high-performance multi-core systems. Its core technology system relies on mature mechanisms in the database field such as Optimistic Concurrency Control, Transaction DAG Scheduling, Out-of-Order Execution, and Pipelined Execution, aiming to increase the transaction processing performance of the chain to the order of millions of TPS. In the Monad architecture, the execution and ordering of transactions are completely decoupled, and the system first builds a transaction dependency graph, and then hands it over to the scheduler for parallel execution. All transactions are treated as atomic units of transactions, with explicit sets of reads and writes and snapshots of state, and schedulers execute optimistically based on dependency graphs, rolling back and re-executing when conflicts occur. This mechanism is extremely complex in terms of technical implementation, requiring the construction of an execution stack similar to that of a modern database transaction manager, as well as the introduction of mechanisms such as multi-level caching, prefetching, parallel validation, etc., to compress the latency of final state commit, but it can theoretically push the throughput limit to heights that are not imagined by the current chain.

More importantly, Monad has not given up on interoperability with the EVM. Through an intermediate layer similar to “Solidity-Compatible Intermediate Language”, it supports developers to write contracts in Solidity syntax, and at the same time performs intermediate language optimization and parallelization scheduling in the execution engine. This design strategy of “surface compatibility and bottom refactoring” not only retains its friendliness to Ethereum ecological developers, but also liberates the underlying execution potential to the greatest extent, which is a typical technical strategy of “swallowing the EVM and then deconstructing it”. This also means that once Monad is launched, it will not only become a sovereign chain with extreme performance, but also an ideal execution layer for Layer 2 rollup networks, and even a “pluggable high-performance core” for other chain execution modules in the long run. From this point of view, Monad is not only a technical route, but also a new logic of system sovereignty design, which advocates the “modularization-performance-reusability” of the execution layer, so as to create a new standard for inter-chain collaborative computing.

Unlike Monad’s “new world builder” stance, MegaETH is a completely opposite type of project, which chooses to start from the existing world of Ethereum and achieve a significant increase in execution efficiency with minimal change costs. Rather than overturning the EVM specification, MegaETH seeks to build the power of parallel computing into the execution engine of the existing EVM to create a future version of the “multi-core EVM”. The rationale lies in a complete refactoring of the current EVM instruction execution model with capabilities such as thread-level isolation, contract-level asynchronous execution, and state access conflict detection, allowing multiple smart contracts to run simultaneously in the same block and eventually merge state changes. This model requires developers to achieve significant performance gains from the same contract deployed on the MegaETH chain without changing existing Solidity contracts, using new languages or toolchains. This “conservative revolution” path is extremely attractive, especially for the Ethereum L2 ecosystem, as it provides an ideal pathway to painless performance upgrades without the need to migrate syntax.

The core breakthrough of MegaETH lies in its VM multi-threaded scheduling mechanism. Traditional EVMs use a stacked, single-threaded execution model, where each instruction is executed linearly and state updates must occur synchronously. MegaETH breaks this pattern and introduces an asynchronous call stack and execution context isolation mechanism, so as to achieve simultaneous execution of “concurrent EVM contexts”. Each contract can invoke its own logic in a separate thread, and all threads will uniformly detect and converge the state through the Parallel Commit Layer when the state is finally submitted. This mechanism is very similar to the JavaScript multithreading model of modern browsers (Web Workers + Shared Memory + Lock-Free Data), which retains the determinism of the behavior of the main thread and introduces a high-performance scheduling mechanism that is asynchronous in the background. In practice, this design is also extremely friendly to block builders and searchers, and can optimize Mempool sorting and MEV capture paths according to parallel strategies, forming a closed loop of economic advantages at the execution layer.

What’s more, MegaETH chooses to be deeply bound to the Ethereum ecosystem, and its main location in the future is likely to be an EVM L2 Rollup network, such as Optimism, Base, or Arbitrum Orbit chain. Once adopted on a large scale, it can achieve nearly 100 times performance improvement on top of the existing Ethereum technology stack without changing contract semantics, state model, gas logic, invocation methods, etc., which makes it an attractive technology upgrade direction for EVM conservatives. The MegaETH paradigm is: as long as you’re still doing things on Ethereum, then I’ll let your computing performance skyrocket. From the perspective of realism and engineering, it is easier to implement than Monad, and it is more in line with the iterative path of mainstream DeFi and NFT projects, making it a candidate for ecological support in the short term.

In a sense, the two routes of Monad and MegaETH are not only two implementations of parallel technology paths, but also a classic confrontation between “refactoring” and “compatibility” in the blockchain development route: the former pursues a paradigm breakthrough and reconstructs all the logic from virtual machines to underlying state management to achieve ultimate performance and architectural plasticity; The latter pursues incremental optimization, pushing traditional systems to the limit while respecting existing ecological constraints, thereby minimizing migration costs. There are no absolute advantages or disadvantages between the two, but they serve different developer groups and ecological visions. Monad is more suitable for building new systems from scratch, chain games that pursue extreme throughput, AI agents, and modular execution chains. MegaETH, on the other hand, is more suitable for L2 projects, DeFi projects, and infrastructure protocols that want to achieve performance upgrades with minimal development changes.

One resembles a high-speed train on a brand new track, redefining everything from the rails, power grid, to the train body, all to achieve unprecedented speed and experience; the other is like installing turbines on existing highways, improving lane scheduling and engine structure, allowing vehicles to run faster without leaving the familiar road network. Ultimately, these two may converge: in the next stage of modular blockchain architecture, Monad could become the ‘execution as a service’ module for Rollups, while MegaETH could serve as a performance acceleration plugin for mainstream L2s. The two may eventually merge, forming the resonant wings of a high-performance distributed execution engine in the future Web3 world.

5. Future Opportunities and Challenges of Parallel Computing

As parallel computing moves from paper-based design to on-chain implementation, the potential it unlocks is becoming more concrete and measurable. On the one hand, we have seen that new development paradigms and business models have begun to redefine “on-chain high performance”: more complex chain game logic, more realistic AI agent life cycle, more real-time data exchange protocol, more immersive interactive experience, and even the on-chain collaborative Super App operating system are all changing from “whether it can be done” to “how well it can be done”. On the other hand, what really drives the transition to parallel computing is not only the linear improvement of system performance, but also the structural change of developers’ cognitive boundaries and ecological migration costs. Just as the introduction of the Turing-complete contract mechanism by Ethereum gave birth to the multi-dimensional explosion of DeFi, NFT and DAO, the “asynchronous reconstruction between state and instruction” brought about by parallel computing is also giving birth to a new on-chain world model, which is not only a revolution in execution efficiency, but also a hotbed of fission innovation in product structure.

First of all, from the perspective of opportunities, the most direct benefit is the “lifting of the application ceiling”. Most of the current DeFi, gaming, and social applications are limited by state bottlenecks, gas costs, and latency, and cannot truly carry high-frequency interactions on the chain on a large scale. Taking chain games as an example, GameFi with real motion feedback, high-frequency behavior synchronization, and real-time combat logic is almost non-existent, because the linear execution of traditional EVM cannot support the broadcast confirmation of dozens of state changes per second. With the support of parallel computing, through mechanisms such as transaction DAGs and contract-level asynchronous contexts, high-concurrency chains can be constructed, and deterministic execution results can be guaranteed through snapshot consistency, so as to achieve a structural breakthrough in the “on-chain game engine”. Similarly, the deployment and operation of AI agents will also be substantially improved by parallel computing. In the past, we tended to run AI Agents off-chain and only upload their behavior results to on-chain contracts, but in the future, on-chain can support asynchronous collaboration and state sharing between multiple AI entities through parallel transaction scheduling, so as to truly realize the real-time autonomous logic of Agent on-chain. Parallel computing will be the infrastructure for this “behavior-driven contract”, driving Web3 from “transactions as assets” to a new world of “interactions as agents”.

Second, the developer toolchain and the virtual machine abstraction layer have also been structurally reshaped due to parallelization. The traditional Solidity development paradigm is based on a serial thinking model, where developers are accustomed to designing logic as a single-threaded state change, but in parallel computing architectures, developers will be forced to think about read/write set conflicts, state isolation policies, transaction atomicity, and even introduce architectural patterns based on message queues or state pipelines. This leap in cognitive structure has also given birth to the rapid rise of a new generation of tool chains. For example, a parallel smart contract framework that supports transactional dependency declarations, an IR-based optimization compiler, and a concurrent debugger that supports transaction snapshot simulation will all become a hotbed for infrastructure explosion in the new cycle. At the same time, the continuous evolution of modular blockchains has also brought an excellent landing path for parallel computing: Monad can be inserted into L2 Rollup as an execution module, MegaETH can be deployed as an EVM replacement for mainstream chains, Celestia provides data availability layer support, and EigenLayer provides a decentralized validator network, thus forming a high-performance integrated architecture from the underlying data to the execution logic.

However, the advancement of parallel computing is not an easy road, and the challenges are even more structural and difficult than the opportunities. On the one hand, the core technical difficulties lie in the “consistency guarantee of state concurrency” and the “transaction conflict handling strategy”. Unlike off-chain databases, on-chain cannot tolerate arbitrary degree of transaction rollback or state retraction, and any execution conflicts need to be modeled in advance or precisely controlled during the event. This means that the parallel scheduler must have strong dependency graph construction and conflict prediction capabilities, and at the same time, it must also design an efficient optimistic execution fault tolerance mechanism, otherwise the system is prone to “concurrent failure retry storm” under high load, which not only increases but decreases, and even causes chain instability. Moreover, the current security model of the multi-threaded execution environment has not yet been fully established, such as the precision of the state isolation mechanism between threads, the new utilization of re-entrancy attacks in asynchronous contexts, and the gas explosion of cross-threaded contract calls, which are all new problems that need to be solved.

More insidious challenges arise from ecological and psychological aspects. Whether developers are willing to migrate to the new paradigm, whether they can master the design methods of parallel models, and whether they are willing to give up some readability and contract auditability for performance benefits are the key to determining whether parallel computing can form ecological potential energy. In the past few years, we have seen a number of chains with superior performance but lack developer support gradually fall silent, such as NEAR, Avalanche, and even some Cosmos SDK chains with far better performance than EVM, and their experience reminds us that without developers, there is no ecosystem; Without ecology, no matter how good the performance is, it is just a castle in the air. Therefore, parallel computing projects should not only make the strongest engine, but also make the most gentle ecological transition path, so that “performance is the out-of-the-box” rather than “performance is the cognitive threshold”.

Ultimately, the future of parallel computing is both a triumph for systems engineering and a test for eco-design. It will force us to re-examine “what is the essence of the chain”: is it a decentralized settlement machine, or a globally distributed, real-time state orchestrator? If the latter is the case, then the capabilities of state throughput, transaction concurrency, and contract responsiveness, which were previously regarded as “technical details of the chain”, will eventually become the primary indicators that define the value of the chain. The parallel computing paradigm that truly completes this transition will also become the most core and most compounding infrastructure primitives in this new cycle, and its impact will go far beyond a technical module, and may constitute a turning point in the overall computing paradigm of Web3.

6. Conclusion: Is parallel computing the best path for native scalability in Web3?

Of all the paths to explore the boundaries of Web3 performance, parallel computing is not the easiest to implement, but it may be the closest to the essence of blockchain. It does not migrate off-chain, nor does it sacrifice decentralization in exchange for throughput, but tries to reconstruct the execution model itself in the atomicity and determinism of the chain, from the transaction layer, contract layer, and virtual machine layer to the root of the performance bottleneck. This “native to the chain” scaling method not only retains the core trust model of the blockchain, but also reserves sustainable performance soil for more complex on-chain applications in the future. Its difficulty lies in the structure, and its charm lies in the structure. If the modular reconstruction is the “architecture of the chain”, then the reconstruction of parallel computing is the “soul of the chain”. This may not be a shortcut to the customs clearance, but it is likely to be the only sustainable positive solution in the long-term evolution of Web3. We’re witnessing a similar architectural transition from single-core CPUs to multi-core/threaded OSs, and the appearance of Web3-native operating systems may be hidden in these in-chain parallel experiments.