1. Introduction: Capacity expansion is an eternal proposition, and parallelism is the ultimate battlefield
Since the birth of Bitcoin, the blockchain system has always faced an unavoidable core problem: expansion. Bitcoin processes less than 10 transactions per second, and Ethereum has difficulty breaking through the performance bottleneck of dozens of TPS (transactions per second), which is particularly cumbersome compared to the traditional Web2 world with tens of thousands of TPS. More importantly, this is not a problem that can be solved by simply adding servers, but a systematic limitation deeply embedded in the underlying consensus and structural design of the blockchain - that is, the blockchain impossible triangle where decentralization, security, and scalability cannot be achieved at the same time.
Over the past decade, we have witnessed the ups and downs of countless expansion attempts. From the Bitcoin expansion war to the Ethereum sharding vision, from state channels, Plasma to Rollup and modular blockchains, from Layer 2 off-chain execution to the structural reconstruction of Data Availability, the entire industry has embarked on a path of expansion full of engineering imagination. As the most widely accepted expansion paradigm, Rollup has achieved the goal of significantly increasing TPS while reducing the execution burden of the main chain and retaining the security of Ethereum. However, it has not touched the true limit of the single-chain performance at the bottom of the blockchain, especially at the execution level - that is, the throughput capacity of the block itself - it is still limited by the old processing paradigm of serial computing within the chain.
For this reason, intra-chain parallel computing has gradually entered the industrys field of vision. Unlike off-chain expansion 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. Guided by the ideas of modern operating systems and CPU design, the blockchain is upgraded from a single-threaded mode of serial execution of transactions one by one to a high-concurrency computing system of multi-threading + pipeline + dependent scheduling. Such a path may not only achieve hundreds of times of throughput improvement, but may also 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 an endless stream of optimization models such as parallel programming, asynchronous scheduling, thread pools, and microservices. Blockchain, as a more primitive, more conservative computing system with extremely high requirements for determinism and verifiability, has never been able to fully utilize these parallel computing ideas. This is both a limitation and an opportunity. New chains such as Solana, Sui, and Aptos have introduced parallelism at the architectural level, taking the lead in this exploration; and emerging projects such as Monad and MegaETH have further promoted intra-chain parallelism to breakthroughs in deep mechanisms such as pipeline execution, optimistic concurrency, and asynchronous message-driven, showing characteristics that are getting closer and closer 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 blockchain execution model paradigm. It challenges the fundamental mode of smart contract execution and redefines the basic logic of transaction packaging, state access, call relationship and storage layout. If Rollup is moving transactions to off-chain execution, then on-chain parallelism is building a supercomputing kernel on the chain. Its goal is not to simply 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 networking, etc.
As the Rollup track gradually becomes homogenized, intra-chain parallelism is quietly becoming a decisive variable in the new cycle of Layer 1 competition. Performance is no longer just faster, but whether it can support the possibility of an entire world of heterogeneous applications. This is not only a technical competition, but also a battle for paradigms. The next generation of sovereign execution platforms in the Web3 world is likely to be born from this intra-chain parallel struggle.
II. Panorama of Capacity Expansion Paradigms: Five Routes, Each with Its Own Focus
As one of the most important, continuous and difficult topics in the evolution of public chain technology, capacity expansion has spawned the emergence and evolution of almost all mainstream technology paths in the past decade. Starting from the dispute over the block size of Bitcoin, this technical competition on how to make the chain run faster eventually differentiated 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 expansion, and representative practices include increasing block size, shortening block time, or improving processing power by optimizing data structure and consensus mechanism. This approach has become the focus of the Bitcoin expansion debate, giving rise to big block forks such as BCH and BSV, and also affecting the design ideas of early high-performance public chains such as EOS and NEO. The advantage of this route is that it retains the simplicity of single-chain consistency and 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. Therefore, in todays design, it is no longer the mainstream core solution, but more of an auxiliary combination of other mechanisms.
The second route is off-chain expansion, represented by state channels and sidechains. The basic idea of this path is to transfer most transaction activities to the off-chain, and only write the final results to the main chain, which serves as the final clearing and settlement layer. In terms of technical philosophy, it is close to the asynchronous architecture idea of Web2 - try to keep the heavy transaction processing on the periphery, and the main chain does the minimum trusted verification. Although this idea can theoretically expand the throughput infinitely, the trust model, fund security, and interaction complexity of off-chain transactions limit its application. A typical example is that although the Lightning Network has a clear financial scenario positioning, the ecological scale has never exploded; and many side-chain-based designs, such as Polygon POS, have high throughput while also exposing the disadvantage of difficult inheritance of the main chain security.
The third route is the Layer 2 Rollup route, which is currently the most popular and widely deployed. This approach does not directly change the main chain itself, but achieves expansion through off-chain execution and on-chain verification mechanisms. Optimistic Rollup and ZK Rollup each have their own advantages: the former is fast to implement and highly compatible, but there are problems with challenge period delays and fraud proof mechanisms; the latter has strong security and good data compression capabilities, but is complex to develop and lacks EVM compatibility. Regardless of which type of Rollup, its essence is to outsource the execution right 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 has proved the feasibility of this path, but it has also exposed mid-term bottlenecks such as excessive reliance on data availability (DA), high fees, and fragmented development experience.
The fourth route is the modular blockchain architecture that has emerged in recent years, represented by 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 having multiple specialized chains perform different functions, and then combining them into a scalable network with cross-chain protocols. This direction is deeply influenced by the modular architecture of the operating system and the composable concept of cloud computing. Its advantage is that it can flexibly replace system components and greatly improve efficiency in specific links (such as DA). However, its challenges are also very obvious: after the module is decoupled, the synchronization, verification, and mutual trust costs between systems are 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 traditional chain designs. This model essentially no longer builds a chain, but a chain network, which has unprecedented thresholds for overall architecture understanding and operation and maintenance.
The last route, which is also the focus of this article, is the optimization path of parallel computing within the chain. Unlike the first four categories, which mainly conduct horizontal splitting from the structural level, parallel computing emphasizes vertical upgrading, that is, by changing the execution engine architecture within a single chain, the concurrent processing of atomic transactions can be realized. This requires rewriting the VM scheduling logic and introducing a whole set of modern computer system scheduling mechanisms such as transaction dependency analysis, state conflict prediction, parallelism control, and asynchronous calls. Solana is the first project to implement the concept of parallel VM in the chain-level system, and 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, parallel decoupling, etc., to build a high-performance execution kernel similar to modern CPU. The core advantage of this direction is that it does not need to rely on a multi-chain architecture to achieve a breakthrough in throughput limits, while providing sufficient computing flexibility for the execution of complex smart contracts. It is an important technical prerequisite for future application scenarios such as AI Agent, large-scale chain games, and high-frequency derivatives.
Looking at the five types of expansion paths mentioned above, the differences behind them are actually the systematic trade-offs between blockchain performance, composability, security and development complexity. Rollup is strong in consensus outsourcing and security inheritance, modularization highlights structural flexibility and component reuse, off-chain expansion 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 within the chain. Each path cannot solve all problems, but it is these directions that together constitute a panoramic view of the upgrade of the Web3 computing paradigm, 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 evolved from sequential indexes to concurrent transactions, Web3s expansion path will eventually move towards a highly parallel execution era. In this era, performance is no longer just a competition of chain speed, but a comprehensive reflection of the underlying design philosophy, the depth of architectural understanding, the coordination of software and hardware, and the control of the system. And intra-chain parallelism may be the ultimate battlefield of this long-term war.
3. Parallel Computing Classification Map: Five Paths from Account to Instruction
In the context of the continuous evolution of blockchain expansion technology, parallel computing has gradually become the core path for performance breakthroughs. Unlike the horizontal decoupling of the structure layer, network layer or data availability layer, parallel computing is a deep excavation at the execution layer. It is related to the bottom-level logic of blockchain operation efficiency and determines the reaction speed and processing ability of a blockchain system when facing 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 paths, from coarse-grained to fine-grained, are not only the continuous refinement of parallel logic, but also the path of increasing system complexity and scheduling difficulty.
The earliest account-level parallelism is represented by Solana. This model is based on the decoupling design of account-state, and determines whether there is a conflict relationship by statically analyzing the account sets involved in the transaction. If the account sets accessed by two transactions do not overlap, they can be executed concurrently on multiple cores. This mechanism is very suitable for processing transactions with clear structure and clear input and output, especially programs with predictable paths such as DeFi. However, its natural assumption is that account access is predictable and state dependencies can be statically reasoned, which makes it prone to conservative execution and reduced parallelism when facing complex smart contracts (such as dynamic behaviors such as blockchain games and AI agents). In addition, cross-dependencies between accounts also severely weaken the benefits of parallelism in some high-frequency trading scenarios. Solanas runtime has been highly optimized in this regard, but its core scheduling strategy is still limited by account granularity.
Further refinement based on the account model, we enter the technical level of object-level parallelism. Object-level parallelism introduces semantic abstraction of resources and modules, and performs concurrent scheduling in units of finer-grained state objects. Aptos and Sui are important explorers in this direction, especially the latter, which defines the ownership and mutability of resources at compile time through the linear type system of the Move language, allowing precise control of resource access conflicts at runtime. This approach is more versatile and extensible than account-level parallelism, can cover more complex state reading and writing logic, and naturally serves highly heterogeneous scenarios such as games, social networking, and AI. However, object-level parallelism also introduces higher language barriers and development complexity. Move is not a direct replacement for Solidity, and the cost of ecological switching is high, which limits the speed of 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. This path no longer regards states or accounts as the smallest parallel units, but builds a dependency graph around the entire transaction itself. It regards transactions as atomic operation units, builds transaction graphs (Transaction DAGs) through static or dynamic analysis, and relies on schedulers for concurrent pipeline execution. This design allows the system to maximize parallelism without fully understanding 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, making chain execution closer to the paradigm of GPU scheduler. In practice, this mechanism requires extremely complex dependency managers and conflict detectors, and the scheduler itself may become a bottleneck, but its potential throughput is much higher than the account or object model, making it the most theoretically ceiling force in the current parallel computing track.
The virtual machine-level parallelism embeds the concurrent execution capability directly into the underlying instruction scheduling logic of the VM, striving to completely break through the inherent limitations of EVM serial 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. Its underlying mechanism uses segmented execution, state separation, asynchronous calls, etc. to allow each contract to run independently in different execution contexts, and uses a parallel synchronization layer to ensure final consistency. The most difficult part of this approach is that it must be fully compatible with the existing EVM behavioral semantics, and at the same time transform the entire execution environment and Gas mechanism to allow the Solidity ecosystem to smoothly migrate to the parallel framework. The challenge is not only the extremely deep technical stack, but also the acceptance of major protocol changes by the Ethereum L1 political structure. But if successful, MegaETH is expected to become a multi-core processor revolution in the EVM field.
The last path is the most fine-grained and technically challenging instruction-level parallelism. Its idea originates from the out-of-order execution and instruction pipeline in modern CPU design. This paradigm believes that since each smart contract is ultimately compiled into bytecode instructions, it is entirely possible to schedule and analyze each operation and reorder it in parallel, just like the CPU executes the x86 instruction set. The Fuel team has initially introduced an instruction-level reorderable execution model in its FuelVM. In the long run, once the blockchain execution engine realizes predictive execution and dynamic reordering of instruction dependencies, its parallelism will reach the theoretical limit. This approach may even push the blockchain and 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 scheduler and security verification mechanism are not yet mature, but it points out the ultimate boundary of parallel computing in the future.
In summary, the five major paths of accounts, objects, transactions, VMs, and instructions constitute the development spectrum of parallel computing within the chain. From static data structures to dynamic scheduling mechanisms, from state access prediction to instruction-level reordering, each step of parallel technology means a significant increase in system complexity and development thresholds. But at the same time, they also mark a paradigm shift in the blockchain computing model, from the traditional full-sequence consensus ledger to a high-performance, predictable, and schedulable 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 computers. The parallel path selection of different public chains will also determine the upper limit of their future application ecology, as well as their core competitiveness in scenarios such as AI Agent, chain games, and high-frequency transactions on the chain.
4. In-depth analysis of the two main tracks: Monad vs MegaETH
Among the multiple paths of parallel computing evolution, the two main technical routes that have received the most market attention, the highest voice, and the most complete narrative are undoubtedly the building a parallel computing chain from scratch represented by Monad, and the EVM internal parallel revolution represented by MegaETH. These two are not only the most intensive research and development directions of current cryptographic primitive engineers, but also the most certain polar symbols in the current Web3 computer performance competition. The difference between the two lies not only in the starting point and style of the technical architecture, but also in the distinct ecological objects, migration costs, execution philosophy, and future strategic paths they serve. They represent a parallel paradigm competition of reconstructionism and compatibilityism, respectively, and have profoundly influenced the markets imagination of the final form of high-performance chains.
Monad is a thorough computing fundamentalist. Its design philosophy is not to be compatible with the existing EVM, but to draw inspiration from modern databases and high-performance multi-core systems to redefine the underlying operation mode of the blockchain execution engine. 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 sorting of transactions are completely decoupled. The system first builds a transaction dependency graph and then hands it over to the scheduler for pipeline parallel execution. All transactions are regarded as atomic units of transactions, with clear read-write sets and state snapshots. The scheduler performs optimistic execution based on the dependency graph and rolls back and re-executes when conflicts occur. This mechanism is extremely complex in terms of technical implementation. It requires the construction of an execution stack similar to a modern database transaction manager. It also requires the introduction of multi-level caching, pre-fetching, parallel verification and other mechanisms to compress the final state submission delay. However, in theory, it can push the throughput limit to a height that is unimaginable in the current blockchain circle.
More importantly, Monad has not given up on interoperability with EVM. It supports developers to write contracts in Solidity syntax through an intermediate layer similar to Solidity-Compatible Intermediate Language, while optimizing the intermediate language and parallelizing scheduling in the execution engine. This design strategy of surface compatibility and underlying reconstruction not only retains the friendliness to Ethereum ecosystem developers, but also maximizes the potential of underlying execution. It is a typical technical strategy of swallowing EVM and then reconstructing it. This also means that once Monad is implemented, it will not only become a sovereign chain with extreme performance, but also an ideal execution layer for the Layer 2 Rollup network, and even become a pluggable high-performance kernel for other chain execution modules in the long run. From this perspective, Monad is not only a technical route, but also a new logic of system sovereignty design-it advocates the modularization-high performance-reusability of the execution layer, thereby creating a new standard for inter-chain collaborative computing.
Unlike Monads new world builder attitude, MegaETH is a completely opposite type of project. It chooses to start from the existing world of Ethereum and achieve a significant improvement in execution efficiency with minimal change cost. MegaETH does not overturn the EVM specification, but strives to implant the ability of parallel computing into the execution engine of the existing EVM to create a future version of multi-core EVM. Its basic principle is to completely reconstruct the current EVM instruction execution model so that it has the capabilities of thread-level isolation, contract-level asynchronous execution, and state access conflict detection, thereby allowing multiple smart contracts to run simultaneously in the same block and eventually merge state changes. This model requires developers to obtain significant performance benefits by deploying the same contract on the MegaETH chain without changing the existing Solidity contract or using a new language or tool chain. This conservative revolution path is very attractive, especially for the Ethereum L2 ecosystem, as it provides an ideal path for painless performance upgrades without migrating syntax.
The core breakthrough of MegaETH lies in its VM multi-threaded scheduling mechanism. The traditional EVM adopts a stack-based single-threaded execution model, where each instruction is executed linearly and state updates must occur synchronously. MegaETH breaks this model and introduces an asynchronous call stack and execution context isolation mechanism to achieve simultaneous execution of concurrent EVM contexts. Each contract can call its own logic in an independent thread, and when all threads finally submit the state, they uniformly perform conflict detection and convergence on the state through the Parallel Commit Layer. This mechanism is very similar to the JavaScript multi-threaded model (Web Workers + Shared Memory + Lock-Free Data) of modern browsers, which not only retains the determinism of the main thread behavior, but also introduces a high-performance scheduling mechanism for background asynchronous. In practice, this design is also very friendly to block builders and searchers, and can optimize the Mempool sorting and MEV capture path according to the parallel strategy, forming an economic advantage closed loop on the execution layer.
More importantly, MegaETH chooses to be deeply bound to the Ethereum ecosystem, and its main landing point 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 the existing Ethereum technology stack without changing the contract semantics, state model, Gas logic, calling method, etc., which makes it an attractive technical upgrade direction for EVM conservatives. The paradigm of MegaETH is: as long as you are still doing things in Ethereum, then I will let your computing performance soar in place. From the perspective of realism and engineering, it is easier to land than Monad, and is more in line with the iterative path of mainstream DeFi and NFT projects, making it a candidate solution that is more likely to gain ecological support in the short term.
In a sense, the two routes of Monad and MegaETH are not only two ways to implement parallel technical paths, but also the classic confrontation between the reconstructionists and the compatibilityists in the development of blockchain: the former pursues paradigm breakthroughs and rebuilds all the logic from virtual machines to underlying state management to achieve extreme performance and architectural plasticity; the latter pursues gradual optimization, pushing traditional systems to the limit on the basis of respecting existing ecological constraints, thereby minimizing migration costs. There is no absolute advantage or disadvantage 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; while MegaETH is more suitable for L2 project parties, DeFi projects, and infrastructure protocols that hope to achieve performance upgrades with minimal development changes.
One of them is like a high-speed rail on a brand new track, redefining everything from the track, power grid to the car body, just to achieve unprecedented speed and experience; the other is like installing turbines on existing highways, improving lane scheduling and engine structure, so that vehicles can run faster but not leave the familiar road network. The two may eventually end up in the same place: in the next stage of modular blockchain architecture, Monad can become the execution as a service module of Rollup, and MegaETH can become a performance acceleration plug-in for mainstream L2. The two may eventually merge and form the two-wing resonance of high-performance distributed execution engines in the future Web3 world.
5. Future Opportunities and Challenges of Parallel Computing
As parallel computing gradually moves from paper design to on-chain implementation, the potential it releases is becoming more concrete and measurable. On the one hand, we have seen new development paradigms and business models begin to be redefined around 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 on-chain collaborative Super App operating system, are all shifting from can it be done to how well it can be done. On the other hand, what really drives the transition of parallel computing is not only the linear improvement of system performance, but also the structural change of the cognitive boundaries of developers and the cost of ecological migration. 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 instructions brought by parallel computing is also nurturing 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 removal of the application ceiling. Most of the current DeFi, games, and social applications are limited by state bottlenecks, gas costs, and latency issues, and cannot truly carry high-frequency interactions on the chain on a large scale. Taking chain games as an example, there is almost no GameFi that truly has action feedback, high-frequency behavior synchronization, and real-time combat logic, 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 DAG and contract-level asynchronous context, a high-concurrency behavior chain can be built, and the deterministic execution results can be guaranteed through snapshot consistency, thereby achieving a structural breakthrough in the on-chain game engine. Similarly, the deployment and operation of AI Agents will also be substantially improved due to parallel computing. In the past, we often ran AI Agents off-chain and only uploaded their behavior results to on-chain contracts, but in the future, the chain can support asynchronous collaboration and state sharing between multiple AI entities through parallel transaction scheduling, thereby truly realizing the real-time autonomous logic of Agent on-chain. Parallel computing will become the infrastructure of this behavior-driven contract, pushing Web3 from transactions as assets to a new world of interactions as intelligent entities.
Secondly, the developer toolchain and virtual machine abstraction layer have also undergone structural reshaping due to parallelization. The traditional Solidity development paradigm is based on a serial thinking model, and developers are accustomed to designing logic as single-threaded state changes. However, under the parallel computing architecture, developers will be forced to think about read-write set conflicts, state isolation strategies, and transaction atomicity, and even introduce architectural patterns based on message queues or state pipelines. This cognitive structure transition has also led to the rapid rise of a new generation of toolchains. For example, parallel smart contract frameworks that support transaction dependency declarations, IR-based optimization compilers, and concurrent debuggers that support transaction snapshot simulations will all become hotbeds for infrastructure outbreaks 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 substitute by mainstream chains, Celestia provides data availability layer support, and EigenLayer provides a decentralized validator network, thus forming a high-performance integrated architecture from underlying data to execution logic.
However, the advancement of parallel computing is not a smooth road. The challenges it faces are even more structural and difficult than opportunities. On the one hand, the core technical difficulties lie in consistency guarantee of state concurrency and strategy for handling transaction conflicts. On-chain is different from off-chain databases. It cannot tolerate any degree of transaction rollback or state rollback. Any execution conflict requires pre-modeling or precise control in the process. This means that the parallel scheduler must have a strong dependency graph construction and conflict prediction ability, and at the same time, it is necessary to design an efficient optimistic execution fault-tolerant mechanism. Otherwise, the system is prone to concurrency failure retry storm under high load, which will not only reduce throughput, but even cause chain instability. Moreover, the current security model of multi-threaded execution environment has not been fully established. For example, the accuracy of the state isolation mechanism between threads, the new way of using reentrancy attacks in asynchronous contexts, and the gas explosion of cross-thread contract calls are all new problems to be solved.
The more hidden challenges come from the ecological and psychological levels. 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 gains, these soft issues are the key to whether parallel computing can form ecological potential. In the past few years, we have seen many chains with superior performance but lack of developer support gradually become silent, such as NEAR, Avalanche, and even some Cosmos SDK chains whose performance far exceeds EVM. Their experience reminds us: without developers, there is no ecology; without ecology, no matter how good the performance is, it is just a castle in the air. Therefore, parallel computing projects must not only make the strongest engine, but also make the most gentle ecological transition path, so that performance is ready to use out of the box rather than performance is the cognitive threshold.
Ultimately, the future of parallel computing is both a victory for system engineering and a test of ecological 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 coordinator? If it is the latter, then state throughput, transaction concurrency, and contract responsiveness, which were previously regarded as technical details of the chain, will eventually become the primary indicators for defining the value of the chain. The parallel computing paradigm that truly completes this transition will also become the most core and most compounding infrastructure primitive in this new cycle. Its impact will far exceed that of 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 expansion of Web3?
Among all the paths to explore the performance boundaries of Web3, parallel computing is not the easiest one to implement, but it may be the one that is closest to the essence of blockchain. It does not migrate off-chain, nor does it sacrifice decentralization in exchange for throughput, but rather attempts to reconstruct the execution model itself in the atomicity and determinism of the chain, reaching the root of the performance bottleneck from the transaction layer, contract layer, and virtual machine layer. This native to the chain expansion 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 also lies in the structure. If modular reconstruction is the architecture of the chain, then parallel computing reconstruction is the soul of the chain. This may not be a shortcut to a short-term clearance, but it may be the only sustainable correct solution path in the long-term evolution of Web3. We are witnessing an architectural transition similar to the transition from a single-core CPU to a multi-core/threaded OS, and the appearance of the Web3 native operating system may be hidden in these parallel experiments within the chain.
