Original Title: "Huobi Growth Academy | Web3 Parallel Computing In-Depth Research Report: The Ultimate Path of Native Scalability"
Original Source: Huobi Growth Academy
I. Introduction: Scalability is an Eternal Proposition, Parallelism is the Ultimate Battlefield
Since the birth of Bitcoin, blockchain systems have always faced an unavoidable core issue: scalability. Bitcoin processes less than 10 transactions per second, and Ethereum struggles to break through the performance bottleneck of dozens of 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," but rather a systemic limitation deeply embedded in the underlying consensus and structural design of blockchain—namely, the blockchain trilemma of "decentralization, security, and scalability" where all three cannot be achieved simultaneously.
Over the past decade, we have witnessed the rise and fall of countless scalability attempts. From the Bitcoin scalability wars to Ethereum's sharding vision, from state channels, Plasma to Rollup and modular blockchains, from off-chain execution of Layer 2 to structural reconstruction of Data Availability, the entire industry has embarked on a path of scalability filled with engineering imagination. Rollup, as the most widely accepted scalability paradigm today, has significantly increased TPS while alleviating the execution burden on the main chain and preserving Ethereum's security. However, it has not touched the true limits of "single-chain performance" at the blockchain's core, especially in terms of execution—i.e., the throughput capacity of the block itself—still constrained by the ancient processing paradigm of in-chain serial computation.
For this reason, in-chain parallel computing has gradually entered the industry's vision. Unlike off-chain scalability and cross-chain distribution, in-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, upgrading the blockchain from a "serial execution of transactions one by one" single-threaded model to a "multi-threaded + pipelined + dependency scheduling" high-concurrency computing system. This path not only has the potential to achieve hundreds of times the 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 array 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 ideas. 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 pipelined 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 tool" but also a turning point in the paradigm of blockchain execution models. It challenges the fundamental model of smart contract execution, redefining the basic logic of transaction packaging, state access, call relationships, and storage layout. If Rollup is about "executing transactions off-chain," then in-chain parallelism is about "building a supercomputing kernel on-chain," with the goal not merely to enhance 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, in-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 whether it can support an entire heterogeneous application world. This is not only a technical race but also a battle for paradigms. The next generation of sovereign execution platforms in the Web3 world is likely to emerge from this struggle for in-chain parallelism.
II. Overview of Scalability Paradigms: Five Routes, Each with Its Focus
Scalability, as one of the most important, persistent, and challenging topics in the evolution of public chain technology, has given rise to almost all mainstream technical paths over the past decade. Starting from the Bitcoin block size debate, this technical competition about "how to make the chain run faster" has ultimately diverged into five basic routes, each approaching the bottleneck from different angles, with its own technical philosophy, implementation difficulty, risk model, and applicable scenarios.
The first route is the most direct on-chain scalability, represented by methods such as increasing block size, shortening block time, or enhancing processing capacity through optimized data structures and consensus mechanisms. This approach became the focus during the Bitcoin scalability debate, giving rise to forks like BCH and BSV that advocate for "large blocks," and also influenced the design ideas of early high-performance public chains like EOS and NEO. The advantage of this route is that it retains the simplicity of single-chain consistency, making it easy to understand and deploy, but it is also prone to systemic limits such as centralization risks, increased node operation costs, and greater synchronization difficulties. Therefore, it is no longer a mainstream core solution in today's designs but has become more of an auxiliary pairing with other mechanisms.
The second route is off-chain scalability, represented by state channels and sidechains. The basic idea of this path is to move most transaction activities off-chain, only writing the final results to the main chain, with the main chain serving as the final settlement layer. In terms of technical philosophy, it is close to the asynchronous architecture ideas of Web2—trying to keep heavy transaction processing on the periphery, with the main chain doing minimal trusted verification. Although this idea theoretically allows for unlimited throughput expansion, issues such as the trust model of off-chain transactions, fund security, and interaction complexity limit its application. A typical example is the Lightning Network, which has a clear financial scenario positioning but has never achieved explosive ecological scale; while multiple designs based on sidechains, such as Polygon POS, expose the difficulty of inheriting the security of the main chain despite high throughput.
The third route is the currently most popular and widely deployed Layer 2 Rollup route. This method does not directly change the main chain itself but achieves scalability through off-chain execution and on-chain verification mechanisms. Optimistic Rollup and ZK Rollup each have their advantages: the former achieves speed and high compatibility but faces issues 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 the type of Rollup, its essence is to outsource execution rights while retaining data and verification on the main chain, achieving a relative balance between decentralization and high performance. The rapid growth of projects like Arbitrum, Optimism, zkSync, and StarkNet proves the feasibility of this path, but it also exposes mid-term bottlenecks such as excessive reliance on data availability (DA), still high costs, and fragmented developer experience.
The fourth route is the modular blockchain architecture that has emerged in recent years, represented by projects like Celestia, Avail, and EigenLayer. The modular paradigm advocates for the complete decoupling of the core functions of blockchain—execution, consensus, data availability, and settlement—by having multiple specialized chains perform different functions, which are then combined into a scalable network through cross-chain protocols. This direction is deeply influenced by the modular architecture of operating systems and the composable ideas of cloud computing, with the advantage of being able to flexibly replace system components and significantly improve efficiency in specific areas (such as DA). However, its challenges are also very apparent: after modular decoupling, the costs of synchronization, verification, and mutual trust between systems are extremely high, the developer ecosystem is highly fragmented, and the requirements for medium- to long-term protocol standards and cross-chain security are much higher than traditional chain designs. This model essentially no longer builds a "chain" but rather constructs a "chain network," posing unprecedented thresholds for understanding and operating the overall architecture.
The final route, which is the focus of the subsequent analysis in this article, is the in-chain parallel computing optimization path. Unlike the first four types that mainly perform "horizontal splitting" from a structural level, parallel computing emphasizes "vertical upgrading," that is, achieving concurrent processing of atomic transactions by changing the execution engine architecture 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 calls. Solana was one of the first projects to implement the concept of a parallel VM at the chain level, achieving multi-core parallel execution through transaction conflict judgment based on an account model. New generation projects like Monad, Sei, Fuel, and MegaETH further attempt to introduce cutting-edge ideas such as pipelined execution, optimistic concurrency, storage partitioning, and parallel decoupling, constructing a high-performance execution kernel akin to modern CPUs. The core advantage of this direction is that it can achieve throughput limit breakthroughs without relying on a multi-chain architecture, while providing sufficient computational elasticity for complex smart contract execution, making it an important technical prerequisite for future applications such as AI agents, large-scale chain games, and high-frequency derivatives.
Looking at the five scalability paths mentioned above, the underlying divisions actually reflect the systematic trade-offs that blockchain faces between performance, composability, security, and development complexity. Rollup excels in consensus outsourcing and security inheritance, modularization highlights structural flexibility and component reuse, off-chain scalability attempts to break through the main chain bottleneck but incurs high trust costs, while in-chain parallelism focuses on fundamental upgrades at the execution layer, attempting to approach the performance limits of modern distributed systems without compromising on-chain consistency. Each path cannot solve all problems, but these directions collectively form a panoramic view of the upgrade of the Web3 computing paradigm, providing developers, architects, and investors with extremely rich strategic options.
Just as operating systems historically transitioned from single-core to multi-core, and databases evolved from sequential indexing to concurrent transactions, the path to scalability in Web3 will ultimately move towards a highly parallelized execution era. In this era, performance will no longer be merely a race of chain speed, but a comprehensive reflection of underlying design philosophy, depth of architectural understanding, hardware-software synergy, and system control capabilities. And in-chain parallelism may very well be the ultimate battlefield of this long-term war.
III. Classification Map of Parallel Computing: Five Paths from Accounts to Instructions
In the context of the continuous evolution of blockchain scalability technology, parallel computing has gradually become the core path for performance breakthroughs. Unlike the horizontal decoupling of structural layers, network layers, or data availability layers, parallel computing is a deep exploration at the execution layer, concerning the most fundamental logic of blockchain operational efficiency, determining a blockchain system's response speed and processing capacity when facing high concurrency and complex transactions of various types. From the perspective of execution models, reviewing the development trajectory of this technological lineage, we can outline 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 range from coarse-grained to fine-grained, representing both a continuous refinement of parallel logic and an increasing complexity of systems and scheduling difficulties.
The earliest form of account-level parallelism is represented by the paradigm of Solana. This model is based on the decoupling design of account-state, determining whether there are conflict relationships by statically analyzing the set of accounts involved in transactions. If the sets of accounts accessed by two transactions do not overlap, they can be executed concurrently on multiple cores. This mechanism is particularly suitable for handling transactions with clear structures and well-defined input and output, especially programs with predictable paths like DeFi. However, its inherent assumption is that account access is predictable and state dependencies can be statically inferred, which leads to issues of conservative execution and reduced parallelism when facing complex smart contracts (such as chain games, AI agents, and other dynamic behaviors). Additionally, cross-dependencies between accounts can severely diminish the benefits of parallelism in certain high-frequency trading scenarios. Solana's runtime has achieved high optimization in this regard, but its core scheduling strategy is still limited by account granularity.
Building further on the account model, we enter the technical level of object-level parallelism. Object-level parallelism introduces semantic abstractions of resources and modules, conducting concurrent scheduling at a finer granularity using "state objects" as units. Aptos and Sui are important explorers in this direction, especially the latter, which uses the Move language's linear type system to define resource ownership and mutability at compile time, allowing for precise control of resource access conflicts at runtime. This approach is more versatile and scalable compared to account-level parallelism, covering more complex state read-write logic and naturally serving high heterogeneity scenarios such as gaming, social interactions, and AI. However, object-level parallelism also introduces higher language barriers and development complexity; Move is not a direct replacement for Solidity, and the high cost of ecosystem switching limits the speed of its parallel paradigm's adoption.
Going 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 considers state or accounts as the smallest parallel units but constructs dependency graphs around the entire transaction itself. It views transactions as atomic operation units, building transaction graphs (Transaction DAG) through static or dynamic analysis, and relies on a scheduler for concurrent pipelined execution. This design allows the system to maximize parallelism without needing to fully understand the underlying state structure. Monad is particularly noteworthy, as it combines optimistic concurrency control (OCC), parallel pipelined scheduling, out-of-order execution, and other modern database engine technologies, bringing chain execution closer to the paradigm of "GPU scheduling." In practice, this mechanism requires extremely complex dependency managers and conflict detectors, and the scheduler itself may become a bottleneck, but its potential throughput capability far exceeds that of account or object models, making it one of the most theoretically promising forces in the current parallel computing track.
Virtual machine-level parallelism directly embeds concurrent execution capabilities into the underlying instruction scheduling logic of the VM, striving to completely break through the inherent limitations of EVM's sequential execution. MegaETH, as a "super virtual machine experiment" within the Ethereum ecosystem, is attempting to redesign the EVM to support multi-threaded concurrent execution of smart contract code. Its underlying mechanisms, such as segmented execution, state isolation, and asynchronous calls, allow each contract to run independently in different execution contexts while ensuring final consistency through a parallel synchronization layer. The most challenging aspect of this approach is that it must be fully compatible with the existing EVM behavior semantics while transforming the entire execution environment and gas mechanism to enable a smooth migration of the Solidity ecosystem to the parallel framework. Its challenges are not only due to the deep technical stack but also involve the Ethereum L1 political structure's acceptance of significant protocol changes. However, if successful, MegaETH is expected to become the "multi-core processor revolution" in the EVM domain.
The final path is the most fine-grained and has the highest technical threshold: instruction-level parallelism. Its concept originates from out-of-order execution and instruction pipelining in modern CPU design. This paradigm posits that since every smart contract is ultimately compiled into bytecode instructions, it is entirely feasible to schedule and analyze each operation for parallel rearrangement, just like a CPU executing the x86 instruction set. The Fuel team has already introduced a preliminary instruction-level reorderable execution model in its FuelVM, and in the long run, once the blockchain execution engine achieves predictive execution and dynamic rearrangement of instruction dependencies, its parallelism will reach theoretical limits. This approach could even elevate the collaborative design of blockchain and hardware to a new height, making the chain a true "decentralized computer" rather than just a "distributed ledger." Of course, this path is still in the theoretical and experimental stages, and the relevant schedulers and security verification mechanisms have yet to mature, but it points to the ultimate boundary of parallel computing in the future.
In summary, the five paths of accounts, objects, transactions, VMs, and instructions constitute the spectrum of development for in-chain parallel computing, ranging from static data structures to dynamic scheduling mechanisms, from state access prediction to instruction-level rearrangement. Each leap in parallel technology signifies a significant increase in system complexity and development thresholds. At the same time, they also mark a paradigm shift in blockchain computing models, moving from traditional total-order consensus ledgers to high-performance, predictable, and schedulable distributed execution environments. This is not only a pursuit of the efficiency of Web2 cloud computing but also a deep conceptualization of the ultimate form of a "blockchain computer." The choice of parallel paths by different public chains will also determine the upper limits of their future application ecosystems and their core competitiveness in scenarios such as AI agents, chain games, and on-chain high-frequency trading.
IV. In-Depth Analysis of Two Major Tracks: Monad vs MegaETH
Among the multiple paths of evolution in parallel computing, the two main technical routes that currently attract the most market focus, have the highest acclaim, and present the most complete narratives are undoubtedly the "parallel computing chain built from scratch" represented by Monad and the "internal parallel revolution of EVM" represented by MegaETH. These two not only represent the most concentrated research and development directions for current cryptographic primitive engineers but also symbolize the two poles of certainty in the current performance competition of Web3 computers. The distinction between them lies not only in the starting point and style of their technical architectures but also in the ecological objects they serve, migration costs, execution philosophies, and future strategic paths, which are entirely different. They represent a competition between a "reconstructionist" and a "compatibilist" parallel paradigm, profoundly influencing the market's imagination of the ultimate form of high-performance chains.
Monad is a thorough "computational fundamentalist," whose design philosophy is not aimed at compatibility with the existing EVM but rather draws inspiration from modern databases and high-performance multi-core systems to redefine the underlying operational methods of blockchain execution engines. Its core technical system relies on mature mechanisms from the database field, such as optimistic concurrency control (Optimistic Concurrency Control), transaction DAG scheduling, out-of-order execution, and pipelined execution, aiming to elevate the transaction processing performance of the chain to the level of millions of TPS. In the Monad architecture, the execution and ordering of transactions are completely decoupled; the system first constructs a transaction dependency graph and then hands it over to the scheduler for pipelined parallel execution. All transactions are treated as atomic units, with clear read-write sets and state snapshots, and the scheduler performs optimistic execution based on the dependency graph, rolling back and re-executing in the event of conflicts. This mechanism is technically complex, requiring the construction of an execution stack similar to modern database transaction managers, while also needing to introduce multi-level caching, prefetching, and parallel validation mechanisms to compress the final state submission delay. However, theoretically, it can push throughput limits to heights previously unimagined in the current blockchain space.
More critically, Monad has not abandoned interoperability with the EVM. It supports developers in writing contracts using Solidity syntax through an intermediate layer similar to a "Solidity-Compatible Intermediate Language," while optimizing and parallelizing the intermediate language in the execution engine. This design strategy of "surface compatibility, underlying reconstruction" retains friendliness towards Ethereum ecosystem developers while maximizing the liberation of underlying execution potential, embodying a typical technical strategy of "swallowing the 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 may also become the ideal execution layer for Layer 2 Rollup networks, and even potentially serve as a "pluggable high-performance kernel" for execution modules of other chains in the long term. From this perspective, Monad is not just a technical route but a new logic of system sovereignty design—it advocates for the "modular-high-performance-reusable" nature of the execution layer, thereby creating a new standard for inter-chain collaborative computing.
In contrast to Monad's "new world builder" stance, MegaETH is a completely opposite type of project. It chooses to start from the existing world of Ethereum, achieving a significant improvement in execution efficiency with minimal change costs. MegaETH does not overturn the EVM specification but strives to embed the capabilities of parallel computing into the existing EVM execution engine, creating a future version of a "multi-core EVM." Its basic principle lies in thoroughly reconstructing the current EVM instruction execution model to enable thread-level isolation, contract-level asynchronous execution, and state access conflict detection, allowing multiple smart contracts to run simultaneously within the same block and ultimately merge state changes. This model requires developers to make no changes to existing Solidity contracts and does not necessitate the use of new languages or toolchains; they can achieve significant performance gains simply by deploying the same contracts on the MegaETH chain. This "conservative revolution" path is highly attractive, especially for the Ethereum L2 ecosystem, as it provides an ideal pathway for performance upgrades without the need to migrate 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 by introducing an asynchronous call stack and execution context isolation mechanism, enabling the simultaneous execution of "concurrent EVM contexts." Each contract can call its own logic in an independent thread, while all threads perform conflict detection and convergence on the state through a parallel commit layer when submitting the final state. This mechanism is very similar to the modern JavaScript multi-threading model in browsers (Web Workers + Shared Memory + Lock-Free Data), retaining the determinism of main thread behavior while introducing a high-performance scheduling mechanism for background asynchronous operations. In practice, this design is also very friendly to block builders and searchers, allowing them to optimize Mempool sorting and MEV capture paths based on parallel strategies, forming an economic advantage closed loop at the execution layer.
More importantly, MegaETH chooses to be deeply tied to the Ethereum ecosystem, and its future main landing point is likely to be on an EVM L2 Rollup network, such as Optimism, Base, or Arbitrum Orbit. Once widely adopted, it can achieve nearly a hundredfold performance improvement on the existing Ethereum technology stack without changing contract semantics, state models, gas logic, calling methods, etc. This makes it a highly attractive technical upgrade direction for EVM conservatives. The paradigm of MegaETH is: as long as you are still doing things on Ethereum, I will elevate your computing performance on the spot. From a realism and engineering perspective, it is easier to implement than Monad and aligns more with the iterative paths of mainstream DeFi and NFT projects, making it a more likely candidate to gain 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 the "reconstructionist" and "compatibilist" factions in blockchain development: the former pursues paradigm breakthroughs, reconstructing all logic from the virtual machine to underlying state management to achieve extreme performance and architectural plasticity; the latter seeks incremental optimization, pushing traditional systems to their limits while respecting existing ecological constraints, thereby minimizing migration costs. There is no absolute superiority between the two; rather, they serve different developer groups and ecological visions. Monad is more suitable for building entirely new systems from scratch, pursuing extreme throughput for chain games, 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 resembles a high-speed train on a brand new track, redefining everything from the rails and power grid to the train body, solely to achieve unprecedented speed and experience; the other is like installing turbines on existing highways, improving lane scheduling and engine structure to make vehicles run faster without leaving the familiar road network. Ultimately, these two may converge: in the next phase 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 a resonant duality in the high-performance distributed execution engine of the future Web3 world.
V. Future Opportunities and Challenges of Parallel Computing
As parallel computing gradually transitions from paper design to on-chain implementation, the potential it releases is becoming increasingly tangible and measurable. On one hand, we see new development paradigms and business models beginning to redefine "on-chain high performance": more complex chain game logic, more realistic AI agent lifecycles, more real-time data exchange protocols, more immersive interactive experiences, and even on-chain collaborative Super App operating systems are all shifting from "can it be done" to "how well can it be done." On the other hand, what truly drives the leap in parallel computing is not just the linear improvement in system performance but also the structural transformation of developers' cognitive boundaries and ecological migration costs. Just as Ethereum's introduction of Turing-complete contract mechanisms led to the multidimensional explosion of DeFi, NFTs, and DAOs, the "asynchronous reconstruction between state and instructions" brought by parallel computing is also nurturing a brand new on-chain world model, which is not only a revolution in execution efficiency but also a breeding ground for disruptive innovation in product structure.
First, from the perspective of opportunities, the most direct benefit is the "removal of the application ceiling." Current DeFi, gaming, and social applications are mostly limited by state bottlenecks, gas costs, and latency issues, making it impossible to truly scale on-chain high-frequency interactions. Taking chain games as an example, there are almost no GameFi projects that genuinely possess action feedback, high-frequency behavior synchronization, and real-time combat logic, because traditional EVM's linear execution cannot support the broadcast confirmation of dozens of state changes per second. With the support of parallel computing, mechanisms such as transaction DAGs and contract-level asynchronous contexts can construct high-concurrency behavior chains, ensuring deterministic execution results through snapshot consistency, thus achieving a structural breakthrough for "on-chain game engines." Similarly, the deployment and operation of AI agents will also gain essential improvements due to parallel computing. In the past, we often ran AI agents off-chain, only uploading their behavioral results to on-chain contracts, but in the future, on-chain parallel transaction scheduling will support asynchronous collaboration and state sharing among multiple AI entities, thereby truly realizing the real-time autonomous logic of agents on-chain. Parallel computing will become the infrastructure for such "behavior-driven contracts," pushing Web3 from "transactions as assets" to a new world of "interactions as agents."
Secondly, the developer toolchain and virtual machine abstraction layer will also undergo structural reshaping due to parallelization. The traditional Solidity development paradigm is based on a serial thinking model, where developers are accustomed to designing logic as single-threaded state changes. However, under a parallel computing architecture, developers will be forced to think about read-write set conflicts, state isolation strategies, and transaction atomicity, even introducing architectural patterns based on message queues or state pipelines. This cognitive structural leap will also give rise to the rapid emergence of a new generation of toolchains. For example, parallel smart contract frameworks that support transaction dependency declarations, optimization compilers based on IR, and concurrent debuggers that support transaction snapshot simulation will all become breeding grounds for infrastructure breakthroughs in the new cycle. At the same time, the continuous evolution of modular blockchains also provides excellent pathways for the implementation of parallel computing: Monad can be inserted as an execution module into L2 Rollups, MegaETH can be deployed as an EVM alternative on mainstream chains, Celestia provides data availability layer support, and EigenLayer offers 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 without its challenges, which may be even more structural and difficult to tackle than the opportunities. On one hand, the core technical challenges lie in "ensuring consistency in state concurrency" and "handling transaction conflicts." Unlike off-chain databases, on-chain systems cannot tolerate arbitrary levels of transaction rollbacks or state retractions; any execution conflict requires prior modeling or precise control during execution. This means that parallel schedulers must possess strong capabilities for building dependency graphs and predicting conflicts, while also designing efficient optimistic execution fault tolerance mechanisms. Otherwise, the system is likely to experience "concurrent failure retry storms" under high load, resulting in decreased throughput and even chain instability. Moreover, the current security model for multi-threaded execution environments has not been fully established, with new issues yet to be resolved, such as the precision of inter-thread state isolation mechanisms, new forms of reentrancy attacks in asynchronous contexts, and gas explosions in cross-thread contract calls.
More insidious challenges arise from ecological and psychological levels. Whether developers are willing to migrate to new paradigms, whether they can master the design methods of parallel models, and whether they are willing to sacrifice some readability and contract auditability for performance gains—these soft issues are the key determinants of whether parallel computing can form ecological momentum. Over the past few years, we have seen several high-performance chains that lack developer support gradually fall silent, such as NEAR, Avalanche, and even some Cosmos SDK chains that far exceed EVM performance. Their experiences remind us: without developers, there is no ecosystem; without an ecosystem, no matter how good the performance, it is just a castle in the air. Therefore, parallel computing projects must not only create the strongest engines but also establish the most gentle ecological transition paths, making "performance plug-and-play" rather than "performance as a cognitive barrier."
Ultimately, the future of parallel computing is both a victory of systems engineering and a trial of ecological design. It will force us to re-examine "what is the essence of a 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—abilities previously regarded as "technical details of the chain"—will ultimately become the primary indicators defining the value of the chain. The parallel computing paradigm that truly completes this leap will also become the most core and compounding infrastructure primitive in this new cycle, with impacts that extend far beyond a single technical module, potentially constituting a turning point in the overall computing paradigm of Web3.
VI. Conclusion: Is Parallel Computing the Best Path for Native Scaling in Web3?
Among all the paths exploring the performance boundaries of Web3, parallel computing may not be the easiest to implement, but it could be the one closest to the essence of blockchain. It does not rely on migrating off-chain, nor does it sacrifice decentralization for throughput; rather, it attempts to reconstruct the execution model itself within the atomicity and determinism of the chain, reaching down to the root of performance bottlenecks from the transaction layer, contract layer, and virtual machine layer. This "native to the chain" scaling method not only preserves the core trust model of blockchain but also reserves sustainable performance soil for more complex on-chain applications in the future. Its difficulty lies in its structure, and its charm also lies in its structure. If modularization reconstructs the "architecture of the chain," then parallel computing reconstructs the "soul of the chain." This may not be a shortcut for immediate success, but it is likely the only sustainable correct path in the long-term evolution of Web3. We are witnessing an architectural leap similar to that from single-core CPUs to multi-core/threaded operating systems, and the appearance of the Web3 native operating system may be hidden within these on-chain parallel experiments.
This article is from a submission and does not represent the views of BlockBeats.
免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。
