In-depth Research Report on Web3 Parallel Computing: The Ultimate Path to Native Scaling

I. Preface: Scaling Is an Eternal Proposition, and Parallelism Is the Ultimate Battlefield

Since the birth of Bitcoin, blockchain systems have always faced a core issue that cannot be avoided: scaling. Bitcoin handles less than 10 transactions per second, and Ethereum also struggles to break through the performance bottleneck of tens of TPS (transactions per second), which seems 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 a systemic limitation deeply embedded in the underlying consensus and structural design of blockchain — the blockchain impossible trinity of "decentralization, security, and scalability".

Over the past decade, we have witnessed countless scaling attempts. From the Bitcoin scaling war to Ethereum's sharding vision, from state channels and Plasma to Rollup and modular blockchains, from off-chain execution of Layer 2 to the structural reconstruction of Data Availability, the entire industry has forged a scaling path full of engineering imagination. Rollup, as the most widely accepted scaling paradigm currently, has achieved the objective of greatly increasing TPS while reducing the execution burden on the main chain and preserving Ethereum's security. But it has yet to reach the true limit of "single-chain performance" at the blockchain bottom layer, especially at the execution layer, where the block’s throughput capacity remains constrained by the legacy paradigm of on-chain serial computation.

This is precisely why on-chain parallel computing has gradually come into the industry's spotlight. Unlike off-chain scaling and cross-chain distribution, on-chain parallelism completely reconstructs the execution engine while maintaining single-chain atomicity and integrated structure. Drawing from modern operating systems and CPU design, it upgrades blockchain from a "single-threaded model of sequential transaction execution" to a high-concurrency computing system featuring "multi-threading, pipelining, and dependency scheduling". This path cannot merely potentially achieve hundreds of times 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 computation has long been rendered obsolete by modern hardware architectures. It has been replaced by a plethora of optimization models such as parallel programming, asynchronous scheduling, thread pools, and microservices. Blockchain, as a more primitive, conservative computing system with extremely high requirements for determinism and verifiability, has always been unable to fully leverage 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, leading the way in this exploration; while emerging projects like Monad and MegaETH have further elevated on-chain parallelism to breakthroughs in deep mechanisms such as pipeline execution, optimistic concurrency, and asynchronous message-driven processes, exhibiting characteristics increasingly resembling modern operating systems.

It can be said that parallel computing is not just a "performance optimization method", but also a turning point in the paradigm of the blockchain execution model. It challenges the fundamental mode of smart contract execution, redefining the basic logic of transaction packaging, state access, invocation relationships, and storage layout. If Rollup is about "moving transactions off-chain for execution", then on-chain parallelism is about "building a supercomputing kernel on-chain", with the objective of providing truly sustainable infrastructure support for future Web3 native applications — high-frequency trading, game engines, AI model execution, on-chain social networking, etc. — rather than simply increasing throughput.

As the Rollup landscape becomes increasingly homogeneous, on-chain parallelism is quietly emerging as the decisive factor in the next phase of Layer1 competition. Performance is no longer just about being "faster", but about the possibility of supporting an entire world of heterogeneous applications. This is not only a technology race, but also a battle for paradigm dominance. The next-generation sovereign execution platform for the Web3 world is likely to emerge from this wrestling match of on-chain parallelism.

II. The Big Picture of Scaling Paradigms: Five Types of Paths, Each with Its Own Focus

Scaling, as one of the most important, persistent, and difficult topics in the technological evolution of public chains, has spurred the emergence and evolution of almost all mainstream technological paths over the past decade. Starting from the Bitcoin block size debate, this technological competition on "how to make the chain run faster" eventually diverged into five basic paths, each tackling bottlenecks from different angles, with their own technological philosophies, implementation difficulties, risk models, and applicable scenarios.

The first type of path is the most direct on-chain scaling, represented by practices such as increasing block size, shortening block time, or improving processing capacity through optimizing data structures and consensus mechanisms. This approach was once at the center of the Bitcoin scaling debate, leading to "big block" forks such as BCH and BSV, and shaping the design philosophies of early high-performance public chains like EOS and NEO. The advantage of this type of path is that it preserves the simplicity of single-chain consistency, easy to understand and deploy. However, it also very easily hits systemic upper limits such as centralization risks, increased node running costs, and increased synchronization difficulty. Therefore, it is no longer the mainstream core solution in today's designs, but more often serves as a supplementary match for other mechanisms.

The second type of path is off-chain scaling, represented by State Channels and Sidechains. The basic idea of this type of path is to move most transaction events off-chain, only writing the final results to the main chain, with the main chain acting as the final settlement layer. In terms of technological philosophy, it is close to the asynchronous architectural thinking of Web2, namely, trying to leave heavy transaction processing in the periphery, with the main chain providing minimal trusted verification. Although this idea can theoretically infinitely expand throughput, the trust model, capital security, interaction complexity and other issues of off-chain transactions limit its application. A typical example is the Lightning Network, which, despite its clear financial scenario positioning, has yet to see explosive ecosystem growth; while many sidechain-based designs, such as Polygon POS, have exposed the challenge of inheriting the security of the main chain while achieving high throughput.

The third type of path is the currently most popular and widely deployed Layer2 Rollup route. This method does not directly change the main chain itself, but achieves scaling through the mechanism of off-chain execution and on-chain verification. Optimistic Rollup and ZK Rollup each have their own advantages: the former is quick to implement and has high compatibility, but faces challenges such as dispute period delays and fraud proof mechanism; the latter has strong security and good data compression capability, but is complex to develop and lacks EVM compatibility. Regardless of which 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 such as Arbitrum, Optimism, zkSync, and StarkNet has proven the feasibility of this approach, while also exposing medium-term bottlenecks including excessive reliance on data availability (DA), persistently high costs, and fragmented development experience.

The fourth type of path is the modular blockchain architecture that has emerged in recent years, represented by Celestia, Avail, EigenLayer, etc. The modular paradigm advocates completely decoupling the core functions of blockchain — execution, consensus, data availability, settlement — with multiple specialized chains performing different roles, and then combining them into a scalable network through cross-chain protocols. This direction is deeply influenced by the modular architecture of operating systems and the concept of composability in cloud computing. Its advantage lies in being able to flexibly replace system components and greatly improve efficiency in specific links (such as DA). But its challenges are also very obvious: after module decoupling, the synchronization, verification, and mutual trust costs between systems are extremely high, the developer ecosystem is extremely scattered, and the requirements for medium and long-term protocol standards and cross-chain security are far higher than traditional chain designs. This model essentially no longer builds a "chain", but a "chain network", presenting unprecedented challenges in understanding and operating the overall architecture.

The fifth type of path, which is also the focus of subsequent analysis in this article, is the in-chain parallel computing optimization route. Unlike the first four types that mainly perform "horizontal refactoring" at the 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, introducing a whole set of modern computer system scheduling mechanisms such as transaction dependency analysis, state conflict prediction, parallelism control, and asynchronous invocation. Solana is the earliest project to implement the concept of parallel VM in a chain-level system, achieving multi-core parallel execution through transaction conflict judgment based on an account model. The new generation of projects such as Monad, Sei, Fuel, MegaETH, etc., go further to try to introduce cutting-edge ideas such as pipelined execution, optimistic concurrency, storage partitioning, and parallel decoupling to build a high-performance execution kernel like modern CPUs. The core advantage of this direction is that it can achieve breakthroughs in throughput limits without relying on multi-chain architecture, while providing sufficient computing elasticity for complex smart contract execution, which is an important technological prerequisite for future AI Agents, large-scale blockchain games, high-frequency derivatives and other application scenarios.

A review of the five types of scaling paths reveals a fundamental divide rooted in the systemic trade-offs between performance, composability, security, and development complexity in blockchain design. Rollup is strong in consensus outsourcing and security inheritance, modularization highlights structural flexibility and component reuse, off-chain scaling tries to break through the main chain bottleneck but at a high cost of trust, while in-chain parallelism focuses on fundamental upgrading of the execution layer, attempting to approach the performance limit of modern distributed systems without breaking on-chain consistency. No single path can solve all problems, but it is precisely these directions that together constitute a panoramic view of the Web3 computing paradigm upgrade, providing extremely rich strategic options for developers, architects, and investors.

Just as operating systems have historically transitioned from single-core to multi-core, and databases have evolved from sequential indexing to concurrent transactions, the scaling path of Web3 will also eventually move towards a highly parallelized execution era. In this era, performance is no longer just a race of chain speed, but a comprehensive reflection of underlying design philosophy, architectural understanding depth, software and hardware coordination, and system control. And on-chain parallelism may be the ultimate battlefield of this long-term war.

III. Taxonomy of Parallel Computing: Five Paths from Account to Instruction

In the context of the continuous evolution of blockchain scaling technology, parallel computing has gradually become the core path for performance breakthroughs. Unlike the horizontal decoupling of the structural layer, network layer or data availability layer, parallel computing is a deep dive at the execution layer. It concerns the most underlying logic of blockchain operational efficiency and determines the reaction speed and processing capacity of a blockchain system when facing highly concurrent and diverse complex transactions. Starting from the execution model and reviewing the development context of this technology lineage, we can sort out a clear taxonomy of parallel computing, which can be roughly divided into five technological paths: Account-level parallelism, object-level parallelism, transaction-level parallelism, virtual machine-level parallelism, and instruction-level parallelism. These five types of paths range from coarse-grained to fine-grained, which is not only a process of continuous refinement of parallel logic, but a path of increasing system complexity and scheduling difficulty.

The earliest account-level parallelism is a paradigm represented by Solana. This model is based on the decoupled design of account-state, statically analyzing the set of accounts involved in transactions to determine if there are conflicting relationships. If the sets of accounts accessed by two transactions do not overlap, they can be executed concurrently on multiple cores. This mechanism is very suitable for handling transactions with clear structure and input-output, especially procedures with predictable paths such as DeFi. But its natural assumption is that account access is predictable and state dependencies can be statically inferred, which makes it prone to conservative execution and decreased parallelism when faced with complex smart contracts (such as blockchain games, AI agents and other dynamic behaviors). In addition, cross-dependencies between accounts also severely weaken the benefits of parallelism in some high-frequency trading scenarios. Solana's runtime has already achieved a high degree of optimization in this regard, but its core scheduling strategy is still limited by the account granularity.

Further refining on the basis of the account model, we enter the technological level of object-level parallelism. Object-level parallelism introduces semantic abstractions of resources and modules, using finer-grained "state objects" as units for concurrent scheduling. 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 the runtime to precisely control resource access conflicts. This method is more general and extensible than account-level parallelism, covering more complex state read and write logic, and naturally serving 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 ecosystem switching cost is high, limiting the popularization speed of its parallel paradigm.

A further step is transaction-level parallelism, which is the direction explored by the new generation of high-performance chains represented by Monad, Sei, and Fuel. This path no longer takes states or accounts as the smallest parallel unit, but builds a dependency graph around the entire transaction itself. It treats transactions as atomic operation units, statically or dynamically analyzing to construct a transaction graph (Transaction DAG), and relies on a scheduler for concurrent pipelined execution. This design allows the system to maximize the mining of parallelism without needing to fully understand the underlying state structure. Monad is particularly noteworthy, combining modern database engine technologies such as optimistic concurrency control (OCC), parallel pipelined scheduling, and out-of-order execution, making chain execution closer to the paradigm of a "GPU scheduler". In practice, this mechanism requires an extremely complex dependency manager and conflict detector, and the scheduler itself may become a bottleneck, but its potential throughput capacity is much higher than the account or object model, making it the most theoretically promising force in the current parallel computing track.

Virtual machine-level parallelism, on the other hand, directly embeds concurrent execution capabilities into the VM's underlying instruction scheduling logic, 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 re-design the EVM to support multi-threaded concurrent execution of smart contract code. Its underlying mechanisms, such as segmented execution, state partitioning, and asynchronous invocation, allow each future to run independently in different execution contexts, and rely on a parallel synchronization layer to ensure final consistency. The most difficult part of this method is that it must be fully compatible with the existing EVM behavior semantics, while overhauling the entire execution environment and Gas mechanism, enabling the Solidity ecosystem to smoothly migrate to the parallel framework. Its challenge is not merely a deep technology stack, but also involves the acceptance issue of major protocol modifications by Ethereum's L1 political structure. But if successful, MegaETH is expected to become the "multi-core processor revolution" in the EVM field.

The last type of path is instruction-level parallelism, which is the finest-grained and has the highest technological threshold. Its idea originates from Out-Of-Order Execution and Instruction Pipeline in modern CPU design. This paradigm believes that since every smart contract is ultimately compiled into bytecode instructions, it is entirely possible to perform scheduling analysis and parallel reordering on each operation, just like a CPU executing the x86 instruction set. The Fuel team has preliminarily introduced an instruction-level reorderable execution model in its FuelVM, and in the long run, once the blockchain execution engine achieves predictive execution and dynamic reordering of instruction dependencies, its parallelism will reach the theoretical limit. This method may even push the collaborative design of blockchain and hardware to a whole new level, making the chain a true "decentralized computer" rather than just a "distributed ledger". This path is still in the theoretical and experimental stage, and the relevant scheduler and security verification mechanisms are not yet mature, but it points to the ultimate boundary of the future of parallel computing.

To summarize, account, object, transaction, VM, and instruction-level paths constitute the development spectrum of in-chain parallel computing, from static data structures to dynamic scheduling mechanisms, from state access prediction to instruction-level reordering. Every leap in parallel technology signifies a significant rise in system complexity and development threshold. But at the same time, they also signify a paradigm shift in the blockchain computing model, from the traditional fully serialized consensus ledger to a high-performance, predictable, schedulable distributed execution environment. This is not just a pursuit of Web2 cloud computing efficiency, but a deep vision of the ultimate form of the "blockchain computer". The choice of parallel paths by different public chains will also determine the upper limit of their future application ecosystem and their core competitiveness in scenarios such as AI agents, blockchain games, and on-chain high-frequency trading.

IV. Deep Dive into Two Main Tracks: Monad vs MegaETH

Among the multiple paths of parallel computing evolution, the two main technological routes that currently receive the most market focus, have the loudest voice, and the most complete narratives are undoubtedly the "building a parallel computing chain from scratch" represented by Monad and the "parallel revolution within EVM" represented by MegaETH. These two are not only the R&D directions with the most intensive investment by cryptographic primitive engineers, but also the two polar symbols with the most deterministic significance in the current Web3 computer performance competition. The divide between the two lies not only in the starting point and style of the technological architecture, but in the vastly different ecosystem objects they serve, the migration costs, execution philosophies, and future strategic paths behind them. They respectively represent a "reconstructionism" and a "compatibilism" paradigm of parallel competition, profoundly influencing the market's imagination of the ultimate 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 operating methods of the blockchain execution engine. Its core technological 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 boost 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 viewed as atomic transaction units, 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 technical implementation, requiring the construction of an execution stack similar to a modern database transaction manager, as well as the introduction of mechanisms such as multi-level caching, prefetching, and parallel verification to compress the final state commit latency. But in theory, it can push the throughput limit to heights previously unimaginable in the current chain circle.

More crucially, Monad has not given up interoperability with EVM. Through an intermediate layer similar to a "Solidity-Compatible Intermediate Language", it supports developers to write futures in Solidity syntax, while performing intermediate language optimization and parallelized scheduling in the execution engine. This "surface-compatible, bottom-up reconstruction" design strategy allows it to retain friendliness to Ethereum ecosystem developers while maximizing the potential of underlying execution, a typical "swallow EVM, then reconstruct it" technological strategy. This also means that once Monad is implemented, it will not only become a sovereign chain with extreme performance, but may also become an ideal execution layer for Layer 2 Rollup networks, and even become a "pluggable high-performance kernel" for the execution modules of other chains in the long run. From this perspective, Monad is not only a technological route, but also a new logic of system sovereignty design - it advocates the "modularization-high performance-reusability" of the execution layer, thus creating a new standard for inter-chain collaborative computing.

Unlike Monad's posture as a "new world builder", MegaETH is a completely opposite type of project, choosing to start from Ethereum's existing world and achieve a significant increase in execution efficiency with minimal modification costs. MegaETH does not overturn the EVM standard, but strives to implant the capability of parallel computing into the execution engine of the existing EVM, creating a future version of a "multi-core EVM". Its basic principle is to thoroughly reconstruct the current EVM instruction execution model, giving it the capabilities of thread-level isolation, future-level asynchronous execution, and state access conflict detection, thus allowing multiple smart contracts to run simultaneously within the same block and ultimately merge state modifications. This model requires developers to neither change existing Solidity futures nor use new languages or toolchains. By deploying the same futures on the MegaETH chain, significant performance benefits can be obtained. This "conservative revolution" path is extremely attractive, especially for the Ethereum L2 ecosystem, as it provides an ideal pathway for painlessly upgrading performance without migrating syntax.

The core breakthrough of MegaETH lies in its VM multithreading scheduling mechanism. The traditional EVM adopts a stack-based single-threaded execution model, with each instruction executed linearly and state updates occurring synchronously. MegaETH breaks this model by introducing an asynchronous call stack and execution context isolation mechanism, thus realizing the simultaneous execution of "concurrent EVM contexts". Each future can call its own logic in an independent thread, and all threads, when finally committing states, uniformly perform conflict detection and convergence on the states through a parallel synchronization layer (Parallel Commit Layer). This mechanism is very similar to the JavaScript multithreading model of modern browsers (Web Workers + Shared Memory + Lock-Free Data), preserving the determinism of the main thread behavior while introducing a high-performance asynchronous scheduling mechanism in the background. In practice, this design is also extremely friendly to block builders and searchers, allowing optimization of Mempool sorting and MEV capture paths based on parallel strategies, forming an economic advantage loop at the execution layer.

More importantly, MegaETH chooses to deeply bind with the Ethereum ecosystem, and its main future landing point is likely to be an EVM L2 Rollup network, such as Optimism, Base, or Arbitrum Orbit chain. Once widely adopted, it can achieve a nearly hundredfold performance improvement on top of the existing Ethereum technology stack without changing futures semantics, state models, Gas logic, invocation methods, etc., making it an extremely attractive technological 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 on the spot. From a realist and engineering perspective, it is easier to implement than Monad and more in line with the iteration path of mainstream DeFi and NFT projects, becoming a candidate solution more likely to gain ecosystem support in the short term.

In a sense, these two routes of Monad and MegaETH are not merely two implementation methods of parallel technology paths, but the classic confrontation between "reconstructionists" and "compatibilists" in the blockchain development path: the former pursues a paradigm breakthrough, rebuilding all logic from the virtual machine to the underlying state management to achieve extreme performance and architectural plasticity; the latter pursues incremental optimization, pushing traditional systems to the limit on the basis of respecting existing ecosystem constraints, thus minimizing migration costs. Neither is absolutely superior, but rather serves different developer groups and ecosystem visions. Monad is more suitable for building brand new systems from scratch, blockchain games, AI agents, and modular execution chains that pursue extreme throughput; while MegaETH is more suitable for L2 project parties, DeFi projects, and infrastructure protocols that want to upgrade performance through minimal development modifications.

One is like a high-speed rail on a brand new track, redefining everything from rails and power grids to car bodies, just to achieve unprecedented speed and experience; the other is like installing turbos on existing highways, improving lane scheduling and engine structure to make vehicles run faster without leaving the familiar road network. These two may eventually converge: in the next stage of modular blockchain architecture, Monad can become the "execution as a service" module of Rollup, and MegaETH can become the performance acceleration plug-in of mainstream L2s. The two may eventually converge, forming a two-wing resonance of the high-performance distributed execution engine in 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 unleashes is becoming increasingly tangible and measurable. On one hand, we are witnessing new development paradigms and business models beginning to redefine around "on-chain high performance": more complex blockchain game logic, more realistic AI agent life cycles, 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 it can be done". On the other hand, what truly drives the leap of parallel computing is not just the linear improvement of system performance, but also the structural transformation of developers' cognitive boundaries and ecosystem migration costs. Just as Ethereum's introduction of Turing-complete smart contracts catalyzed the multidimensional explosion of DeFi, NFTs, and DAOs, the "asynchronous reconstruction between state and instructions" enabled by parallel computing is incubating a brand new model of the on-chain world. This represents both a revolution in execution efficiency and a hotbed of disruptive product structure innovation.

First, from the perspective of opportunities, the most direct benefit is the "removal of application ceilings". Current DeFi, gaming, and social applications are mostly limited by state bottlenecks, Gas costs, and latency issues, unable to truly scale to support on-chain high-frequency interactions. Take blockchain games as an example. GameFi with true action feedback, high-frequency behavior synchronization, and real-time combat logic is virtually non-existent because the linear execution of traditional EVM cannot support the broadcast confirmation of dozens of state modifications per second. However, with the support of parallel computing, through mechanisms such as transaction DAG and future-level asynchronous contexts, high-concurrency behavior chains can be constructed, and deterministic execution results can be guaranteed through snapshot consistency, thus achieving a structural breakthrough in "on-chain game engines". Similarly, the deployment and operation of AI agents will also gain fundamental improvements due to parallel computing. In the past, we often ran AI agents off-chain and only uploaded their behavior results to on-chain futures. But in the future, on-chain parallel transaction scheduling can support asynchronous collaboration and state sharing between multiple AI entities, truly realizing the real-time autonomous logic of Agent on-chain. Parallel computing will become the infrastructure for such "behavior-driven futures", driving Web3 from "transaction as asset" to a whole new world of "interaction as intelligent agent".

Secondly, the developer toolchain and virtual machine abstraction layer are also undergoing 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 modifications. But under the parallel computing architecture, developers will be forced to consider read-write set conflicts, state isolation strategies, and transaction atomicity, and even introduce architectural patterns based on message queues or state pipelines. This leap in cognitive structure has also spawned the rapid rise of a new generation of toolchains. For example, parallel smart contract frameworks that support transaction dependency declaration, optimizing compilers based on IR, and concurrent debuggers that support transaction snapshot simulation will all become hotbeds for infrastructure explosion in the new cycle. At the same time, the continuous evolution of modular blockchains also provides an excellent landing path for parallel computing: Monad can be inserted as an execution module into L2 Rollup, MegaETH can be deployed by mainstream chains as an EVM replacement, Celestia provides data availability layer support, and EigenLayer provides a decentralized verifier network, forming a high-performance integrated architecture from bottom-layer data to execution logic.

However, the advancement of parallel computing is not all smooth sailing, and the challenges it faces are even more structural and harder to crack than the opportunities. On one hand, the most core technical difficulty lies in "ensuring the consistency of state concurrency" and "handling strategies for transaction conflicts". On-chain is different from off-chain databases, unable to tolerate arbitrary degrees of transaction rollback or state rollback. Any execution conflict needs to be modeled in advance or precisely controlled during the process. This means that the parallel scheduler must have extremely strong dependency graph construction and conflict prediction capabilities, while also designing efficient optimistic execution fault tolerance mechanisms. Otherwise, the system can easily experience "concurrent failure retry storms" under high load, not only decreasing throughput but even causing chain instability. Moreover, the safety model of the current multithreaded execution environment has not been fully established. For example, the precision of inter-thread state isolation mechanisms, new exploitation methods of re-entrancy attacks in asynchronous contexts, Gas explosion of cross-thread futures calls, etc., are all new problems yet to be solved.

More hidden challenges come from the ecosystem and psychological aspects. Whether developers are willing to migrate to new paradigms, whether they can master the design methods of parallel models, whether they are willing to give up some readability and futures auditability for performance gains, these soft issues are the key to determining whether parallel computing can form ecosystem momentum. In the past few years, we have seen multiple chains with superior performance but lacking developer support gradually fade away, such as NEAR, Avalanche, and even some Cosmos SDK chains with performance far exceeding EVM. Their experiences all remind us: without developers, there is no ecosystem; without an ecosystem, even the best performance is just a castle in the air. Therefore, parallel computing projects not only need to build the strongest engine, but also the gentlest ecosystem transition path. They need to make "performance out of the box" rather than "performance as a cognitive threshold".

Ultimately, the future of parallel computing is both a victory of systems engineering and a test of ecosystem 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 collaborator? If it is the latter, then state throughput, transaction concurrency, and futures response capabilities, which were previously regarded as "technical details of the chain", will eventually become the primary indicators defining the value of a chain. The parallel computing paradigm that truly completes this leap will also become the most core and most compounding infrastructure primitive in this new cycle. Its impact will far exceed a technology module and may constitute a turning point in the overall computing paradigm of Web3.

VI. Conclusion: Is Parallel Computing the Best Path for Web3 Native Scaling?

Among all the paths exploring the performance boundary of Web3, parallel computing is not the easiest one to implement, but it may be the one closest to the essence of blockchain. It does not try to scale by migrating off-chain, nor by sacrificing decentralization for throughput, but attempts to reconstruct the execution model itself within the atomicity and determinism of the chain, reaching the root of the performance bottleneck from the transaction layer, futures layer, and virtual machine layer. This scaling method that is "native to the chain" not just preserves the most 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 short-term clearance, but it is likely the only sustainable optimal path in the long-term evolution of Web3. We are witnessing an architectural leap similar to the transition from single-core CPUs to multi-core/threaded OS, and the appearance of the Web3 native operating system may be hidden in these on-chain parallel experiments.