Original Title: "Huobi Growth Academy | Web3 Parallel Computing In-Depth Research Report: The Ultimate Path to Native Scalability"

Original Source: Huobi Growth Academy

1. Introduction: Scalability is an Eternal Topic, and 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 also struggles to surpass the performance bottleneck of tens 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 simply solved by "adding more servers"; instead, it is a systemic limitation deeply embedded in the underlying consensus and structural design of blockchain—namely, the impossible triangle of blockchain where decentralization, security, and scalability cannot be achieved simultaneously.

Over the past decade, we have witnessed numerous attempts at scalability. From the Bitcoin scaling wars to the Ethereum sharding vision, from state channels and Plasma to Rollup and modular blockchains, from Layer 2's off-chain execution to the structural reorganization of Data Availability, the entire industry has embarked on a scalability journey full of engineering imagination. As the currently most widely accepted scalability paradigm, Rollup has achieved a significant increase in TPS while relieving the main chain's execution burden and maintaining Ethereum's security. However, it has not reached the true limit of the blockchain's underlying "single-chain performance," especially at the execution level—namely, the throughput of the blocks themselves—still constrained by the ancient processing paradigm of on-chain serial execution.

It is precisely for this reason that on-chain parallel computing has gradually come into the industry's view. Unlike off-chain scaling and cross-chain distribution, on-chain parallelism attempts to maintain single-chain atomicity and integrated structure while completely reengineering the execution engine. Guided by the principles of modern operating systems and CPU design, it transforms the blockchain from a "single-threaded execution of transactions" serial mode to a high-concurrency computing system of "multi-threading + pipelining + dependency scheduling." This path may not only achieve hundreds of times the throughput improvement but also serve as a key prerequisite for the explosion of smart contract applications.

In fact, in the Web2 computing paradigm, single-threaded computation has long been replaced by modern hardware architecture, ushering in a plethora of optimization models such as parallel programming, asynchronous scheduling, thread pools, microservices, and more. However, blockchain, as a more primitive, conservative computing system with high demands for determinism and verifiability, has never fully leveraged 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 pipelined execution, optimistic concurrency, asynchronous message-driven processes, and other deep mechanisms, exhibiting features that are increasingly close to modern operating systems.

It can be said that parallel computing is not just a "performance optimization technique," but also a turning point in the paradigm of blockchain execution models. It challenges the fundamental mode of smart contract execution, redefining the basic logic of transaction batching, state access, call relationships, and storage layout. If Rollup can be seen as "moving transactions off-chain for execution," then on-chain parallelism is "building a supercomputing kernel on-chain," with the goal not only to increase throughput but to provide truly sustainable infrastructure support for future Web3 native applications such as high-frequency trading, game engines, AI model execution, and on-chain social interactions.

As the Rollup track gradually converges towards homogenization, on-chain parallelism is quietly becoming a decisive variable in the new era of Layer1 competition. Performance is no longer just about being "faster," but about whether it can support the possibility of an entire heterogeneous application world. This is not just a technical race but a paradigmatic battle. The next generation sovereign execution platform in the Web3 world is likely to emerge from this on-chain parallelism tug-of-war.

II. Panorama of Scaling Paradigms: Five Routes, Each with Its Focus

Scaling, as one of the most important, sustained, and challenging topics in the evolution of public chain technology, has spawned the emergence and evolution of almost all mainstream technical paths in the past decade. Starting from the block size debate in Bitcoin, this technological race about "how to make the chain run faster" has ultimately differentiated into five basic routes, each tackling bottlenecks from different perspectives, with their own technological philosophies, implementation difficulties, risk models, and applicable scenarios.

The first route is the most direct on-chain scaling, represented by practices such as increasing block size, reducing block time, or improving processing capacity through optimized data structures and consensus mechanisms. This approach became a focal point in the Bitcoin scaling debate, leading to forks such as BCH and BSV in the "big block" camp, and also influencing the design philosophies of early high-performance blockchains like EOS and NEO. The advantage of this route is that it preserves the simplicity of on-chain consensus, making it easy to understand and deploy. However, it is also prone to centralization risks, increased node operation costs, increased synchronization difficulties, and other systemic limits. Therefore, it is no longer a mainstream core solution in today's designs but rather serves as a complementary element to other mechanisms.

The second route is off-chain scaling, represented by State Channels and Sidechains. The basic idea of this route is to move most transaction activities off-chain, only writing the final results to the main chain, where the main chain acts as the final settlement layer. In terms of technological philosophy, it is close to the asynchronous architecture concept of Web2—trying to leave heavy transaction processing off-chain, with the main chain providing minimal trusted verification. Although this approach theoretically allows for infinite throughput scaling, issues such as the trust model of off-chain transactions, fund security, and interaction complexity limit its applications. Notable examples include the Lightning Network, which has a clear financial use case but has yet to see explosive ecosystem growth. Meanwhile, designs based on sidechains, such as Polygon POS, have exposed the challenge of inheriting main chain security while achieving high throughput.

The third path is the most popular and widely deployed Layer2 Rollup path. This approach does not directly change the main chain itself, but achieves scalability through a mechanism of off-chain execution and on-chain validation. Optimistic Rollup and ZK Rollup each have their own advantages: the former is fast and highly compatible, but faces challenges such as dispute period delays and the fraud proof mechanism; 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 keeping data and validation 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 demonstrated the feasibility of this path, but has also revealed mid-term bottlenecks such as over-reliance on data availability (DA), persistently high fees, and fragmented development experience.

The fourth path is the emergence of modular blockchain architectures in recent years, represented by projects such as Celestia, Avail, and EigenLayer. The modular paradigm advocates for a thorough decoupling of the core functions of blockchain — execution, consensus, data availability, and settlement — to be carried out by multiple specialized chains that are then combined into a scalable network through cross-chain protocols. This direction is deeply influenced by the modularity architecture of operating systems and the composable concept of cloud computing. Its advantages include the ability to flexibly replace system components and significantly improve efficiency at specific points (such as DA). However, its challenges are also very apparent: the high cost of synchronization, validation, and trust between systems after module decoupling, extreme decentralization of the developer ecosystem, and the much higher requirements for long-term protocol standards and cross-chain security compared to traditional chain designs. This pattern fundamentally no longer constructs a "chain" but instead builds a "chain network," presenting unprecedented challenges in understanding and operating the overall architecture.

The last path, which is also the focus of the subsequent analysis in this article, is the on-chain parallel computing optimization path. Unlike the first four paths, which mainly conduct "horizontal splitting" at the structural level, parallel computing emphasizes "vertical upgrading," achieving concurrent transaction processing within a single chain by changing the execution engine architecture. This requires rewriting the VM scheduling logic and introducing a set of modern computer system scheduling mechanisms such as transaction dependency analysis, state conflict prediction, parallelism control, and asynchronous calls. Solana was the first 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. New generation projects such as Monad, Sei, Fuel, MegaETH, etc., go further by attempting to introduce cutting-edge concepts such as pipeline execution, optimistic concurrency, storage partitioning, and parallel decoupling to build a high-performance execution core similar to a modern CPU. The core advantage of this direction is that it can achieve a throughput limit breakthrough without relying on a multi-chain architecture, while providing sufficient computational elasticity for complex smart contract execution. This is an important technological prerequisite for future applications such as AI Agents, large-scale chain games, high-frequency derivatives, and other scenarios.

Looking across the five scalability paths mentioned above, the underlying division is actually the systematic trade-off of blockchain among performance, composability, security, and development complexity. Rollup excels in off-chain consensus and security inheritance, highlighting modularity for structural flexibility and component reuse. Off-chain scaling attempts to break through the main chain bottleneck but at a high cost of trust, while on-chain parallelism focuses on a fundamental upgrade of the execution layer, aiming to approach the performance limit of modern distributed systems without breaking on-chain consistency. Each path cannot solve all problems, but it is these directions that together form the panoramic view of the Web3 computing paradigm upgrade, providing developers, architects, and investors with extremely rich strategic options.

Just as historically operating systems transitioned from single-core to multi-core and databases evolved from sequential indexing to concurrent transactions, Web3's scalability journey will eventually move towards the era of highly parallelized execution. In this era, performance is no longer just a race for chain speed but a comprehensive reflection of underlying design philosophy, deep architectural understanding, software-hardware synergy, and system control. On-chain parallelism may be the ultimate battlefield in this long-term war.

III. Parallel Computing Taxonomy: 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 of performance breakthrough. Unlike the horizontal decoupling of the structural layer, network layer, or data availability layer, parallel computing is a deep dive at the execution layer, which concerns the most fundamental logic of blockchain operation efficiency, determining a blockchain system's responsiveness and processing capacity when faced with high concurrency and complex transactions of multiple types. Starting from the execution model and tracing the development trajectory of this technological lineage, we can outline a clear taxonomy 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 a continuous refinement process of parallel logic but also a path where system complexity and scheduling difficulties continue to rise.

The earliest appearance of account-level parallelism, represented by Solana, is based on the account-state decoupling design, analyzing statically the set of accounts involved in transactions to determine if there are conflicting relationships. 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 handling structured and clear input-output transactions, especially for predictable paths such as DeFi programs. However, its inherent assumption is that account access is predictable and state dependency is statically inferable, which makes it prone to conservative execution and decreased parallelism when facing complex smart contracts (e.g., chain games, AI agents, etc., with dynamic behavior). In addition, the cross-dependencies between accounts weaken parallel benefits in certain high-frequency trading scenarios. Solana's runtime has achieved high optimization in this area, but its core scheduling strategy is still constrained by the account granularity.

Building on the account-based model, we further refine our exploration into the object-level parallelism. Object-level parallelism introduces semantic abstractions of resources and modules, allowing concurrent scheduling at a finer granularity based on "state objects." Aptos and Sui are key explorers in this direction, with the latter, especially, leveraging Move language's linear type system to define ownership and mutability of resources at compile time, enabling precise control of resource access conflicts at runtime. This approach, compared to account-level parallelism, is more generalizable and scalable, covering more complex state read/write logic and inherently serving highly heterogeneous scenarios such as gaming, social networks, AI, and more. However, object-level parallelism also brings higher language complexity and development overhead. Move is not a direct replacement for Solidity, and the high ecosystem migration cost limits the widespread adoption of its parallel paradigm.

Advancing further to transaction-level parallelism, a new generation of high-performance chains represented by Monad, Sei, and Fuel explores this direction. This path no longer considers state or accounts as the minimal parallel unit but revolves around building a dependency graph of the entire transaction itself. Transactions are seen as atomic operation units, and a transaction graph (Transaction DAG) is constructed through static or dynamic analysis, with a scheduler orchestrating concurrent pipelined execution. This design allows the system to maximize parallelism without the need to fully understand the underlying state structure. Monad, in particular, is notable for combining optimistic concurrency control (OCC), parallel pipeline scheduling, out-of-order execution, and other modern database engine techniques, making blockchain execution more akin to a "GPU scheduler" paradigm. In practice, this mechanism requires highly complex dependency managers and conflict detectors, and the scheduler itself may become a bottleneck. However, its potential throughput far exceeds that of the account or object model, making it one of the most theoretically bottlenecked forces in the current parallel computing race.

Virtual machine-level parallelism embeds concurrent execution capability directly into the VM's underlying instruction scheduling logic, aiming to fundamentally break free from the inherent limitations of EVM's sequential execution. MegaETH, as an internal "super Virtual Machine experiment" within the Ethereum ecosystem, is attempting to enable multi-threaded concurrent execution of smart contract code by redesigning the EVM. Through mechanisms such as segmented execution, state isolation, and asynchronous calls, each contract runs independently in different execution contexts and leverages a parallel synchronization layer to ensure final consistency. The greatest challenge lies in maintaining full compatibility with the existing EVM behavioral semantics while transforming the entire execution environment and Gas mechanism to facilitate a smooth migration of the Solidity ecosystem onto a parallel framework. This challenge is not only deeply rooted in the technology stack but also involves the acceptance of significant protocol changes by the Ethereum L1 governance structure. However, if successful, MegaETH could become the "multi-core processor revolution" in the EVM domain.

The final category of pathways is the most granular and has the highest technical threshold, known as instruction-level parallelism. This concept is derived from modern CPU design principles such as Out-of-Order Execution and Instruction Pipeline. This paradigm suggests that since every smart contract is ultimately compiled into bytecode instructions, it is entirely possible to analyze, schedule, and parallelize each operation similar to how a CPU executes x86 instructions. The Fuel team has already introduced an instruction-level reordering execution model in its FuelVM. Looking ahead, once a blockchain execution engine achieves predictive execution of instruction dependencies and dynamic reordering, its level of parallelism will reach the theoretical limit. This approach may even elevate blockchain to a new height of hardware-coordinated design, transforming the chain into a true "decentralized computer" rather than just a "distributed ledger." Of course, this pathway is currently in the theoretical and experimental stage, with related schedulers and security verification mechanisms still immature. However, it points towards the ultimate boundary of future parallel computing.

In conclusion, the five main paths of accounts, objects, transactions, VMs, and instructions constitute the spectrum of on-chain parallel computing development. From static data structures to dynamic scheduling mechanisms, from state access prediction to instruction-level reordering, each leap in parallel technology signifies a significant increase in system complexity and development threshold. Nevertheless, they also signify a paradigm shift in the blockchain computing model, transitioning from the traditional total order consensus ledger to a high-performance, predictable, and schedulable distributed execution environment. This is not just catching up to the efficiency of Web2 cloud computing but also an in-depth envisioning of the ultimate form of the "blockchain computer." The selection of parallel paths by different public blockchains will determine their future application ecosystem's carrying capacity and their core competitiveness in scenarios such as AI agents, blockchain games, and high-frequency on-chain transactions.

4. In-Depth Analysis of the Two Mainstream Tracks: Monad vs. MegaETH

Among the diverse paths of parallel computing evolution, the two most focused, highly regarded, and well-narrated mainstream technological routes in the current market are undoubtedly represented by Monad's "building a parallel computing chain from scratch" and MegaETH's "EVM internal parallel revolution." These two are not only the most intensely researched directions by crypto primitive engineers but also the most deterministic symbols of the ongoing Web3 computer performance competition. The divergence between the two lies not only in the starting point and style of their technical architectures but also in the entirely different ecosystem targets, migration costs, execution philosophies, and future strategic paths they serve. They respectively represent a competition between a "reconstructionist" and a "compatibilist" parallel paradigm, significantly influencing the market's imagination of the ultimate form of a high-performance chain.

Monad is a staunch "computation fundamentalist," and its design philosophy is not aimed at compatibility with the existing EVM. Instead, it draws inspiration from modern databases and high-performance multi-core systems to redefine the underlying operation of a blockchain execution engine. Its core technological system relies on mature mechanisms from the database field, such as Optimistic Concurrency Control, transaction DAG scheduling, Out-of-Order Execution, and Pipelined Execution. The goal is to elevate the transaction processing performance of the chain to the order of millions of TPS. In the Monad architecture, transaction execution and ordering are completely decoupled. The system first constructs a transaction dependency graph, which is then handed over to a scheduler for pipelined parallel execution. All transactions are treated as transactional atomic units with explicit read-write sets and state snapshots. The scheduler performs optimistic execution based on the dependency graph and rolls back and re-executes in case of conflicts. This mechanism is highly complex in technical implementation, requiring the construction of a transaction manager-like execution stack. Additionally, it needs to introduce mechanisms such as multi-level caching, prefetching, and parallel verification to compress the final state commitment delay. However, in theory, this approach can push the throughput limit to unprecedented heights in the current blockchain space.

More importantly, Monad has not given up interoperability with EVM. Through an intermediate layer similar to "Solidity-Compatible Intermediate Language," it supports developers to write contracts in Solidity syntax while conducting intermediate language optimization and parallelized scheduling in the execution engine. This "surface-level compatibility, underlying restructuring" design strategy preserves its developer-friendly nature to the Ethereum ecosystem and maximizes the liberation of the underlying execution potential. It is a typical "swallow EVM, then deconstruct it" technological strategy. This also implies that once Monad is implemented, it will not only become a sovereign chain of performance optimization but also potentially an ideal execution layer for Layer 2 Rollup networks. It might even evolve into a "pluggable high-performance kernel" for execution modules of other chains in the future. From this perspective, Monad is not just a technical roadmap but a new logic of system sovereignty design. It advocates the "modular-high-performance-reusable" nature of the execution layer, thereby establishing a new standard for cross-chain collaborative computing.

Unlike Monad's "builder of a new world" stance, MegaETH belongs to a completely opposite category of projects. It chooses to start from the existing world of Ethereum and achieve a significant improvement in execution efficiency with minimal changes. MegaETH does not overturn the EVM specification but instead seeks to embed the ability for parallel computation into the existing EVM's execution engine, creating a future version of a "multi-core EVM." The basic principle involves a thorough restructuring of the current EVM instruction execution model to provide capabilities such as thread-level isolation, contract-level asynchronous execution, and state access conflict detection. This allows multiple smart contracts to run simultaneously within the same block and eventually merge state changes. This mode of operation requires developers to not modify existing Solidity contracts or utilize new languages or toolchains. By solely deploying the same contracts on the MegaETH chain, significant performance benefits can be achieved. This "conservative revolution" path is highly attractive, especially for the Ethereum L2 ecosystem as it offers an ideal path for performance improvement without syntax migration and seamless upgrades.

The core breakthrough of MegaETH lies in its VM multi-threading scheduling mechanism. The traditional EVM adopts a stack-based single-threaded execution model, where each instruction is linearly executed, and state updates must occur synchronously. MegaETH breaks this pattern by introducing an asynchronous call stack and execution context isolation mechanism, thereby achieving the concurrent execution of "EVM contexts." Each contract can invoke its own logic in an independent thread, and all threads, upon final state submission, perform conflict detection and convergence on the state through a Parallel Commit Layer in parallel. This mechanism is very similar to the modern browser's JavaScript multi-threading model (Web Workers + Shared Memory + Lock-Free Data), preserving the determinism of main thread behavior while introducing a high-performance asynchronous background scheduling mechanism. In practice, this design is also very friendly to block builders and searchers, allowing optimization of Mempool sorting and MEV capture paths based on parallel strategies, forming an economic advantage loop on the execution layer.

More importantly, MegaETH chooses to deeply integrate with the Ethereum ecosystem, and its future primary landing point is likely to be on a certain EVM L2 Rollup network, such as Optimism, Base, or Arbitrum Orbit chain. Once widely adopted, it can achieve nearly a hundredfold performance improvement on top of the existing Ethereum technology stack without changing contract semantics, state model, Gas logic, calling methods, etc., making it an attractively conservative technical upgrade direction for EVM purists. MegaETH's paradigm is: as long as you are still working on Ethereum, I will make your computational performance skyrocket in place. From a perspective of realism and engineering, it is more likely to land and more aligned with the iterative paths of mainstream DeFi and NFT projects, making it a candidate solution that is more likely to receive ecosystem support in the short term.

In a sense, the Monad and MegaETH paths are not only two implementation methods of parallel technology paths but also a classic confrontation between the "restructuring faction" and the "compatibility faction" in the blockchain development path: the former pursues a paradigm breakthrough, rebuilding all the logic from the virtual machine to the underlying state management to achieve ultimate performance and architectural flexibility; the latter pursues incremental optimization, pushing traditional systems to the limit while respecting existing ecosystem constraints, thereby minimizing migration costs. Neither is absolutely superior or inferior but rather serves different developer communities and ecosystem visions. Monad is more suitable for building entirely new systems from scratch, pursuing high-throughput blockchain games, AI agents, and modular execution chains; while MegaETH is more suitable for L2 projects that seek performance upgrades with minimal development changes, DeFi projects, and infrastructure protocols.

One is like a high-speed rail on a brand-new track, redefining everything from the rails and power grid to the train body in order to achieve unprecedented speed and experience; the other is like installing a turbocharger on an existing highway, improving lane scheduling and engine structure to make vehicles run faster without leaving the familiar road network. These two may ultimately converge: in the next stage of modular blockchain architecture, Monad could become the "execution-as-a-service" module of Rollup, while MegaETH could become a performance acceleration plugin for mainstream L2. The two may eventually merge to form the dual-wing resonance of a high-performance distributed execution engine in the future Web3 world.

Five, Future Opportunities and Challenges of Parallel Computing

As parallel computing gradually moves from paper design to on-chain implementation, the potential it unleashes is becoming more concrete and measurable. On the one hand, we see new development paradigms and business models emerging around "on-chain high performance," redefining more complex chain game logic, more realistic AI agent lifecycles, real-time data exchange protocols, immersive interactive experiences, and even on-chain collaborative Super App operating systems, all transitioning from "can we do it" to "how well can we do it." On the other hand, what truly drives the transition of parallel computing is not just the linear improvement of system performance, but a structural change in developers' cognitive boundaries and ecosystem migration costs. Just as the introduction of Turing-complete smart contracts to Ethereum years ago gave rise to the multidimensional explosion of DeFi, NFTs, and DAOs, the "asynchronous restructuring between state and instruction" brought about by parallel computing is also nurturing a 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.

Firstly, in terms 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, unable to truly scale to support high-frequency on-chain interactions. Taking chain games as an example, true GameFi with action feedback, high-frequency behavior synchronization, and real-time combat logic is almost non-existent because traditional EVM's linear execution cannot support broadcast confirmation of state changes tens of times per second. With the support of parallel computing, a high-concurrency behavior chain can be built through mechanisms such as transaction DAGs and contract-level asynchronous contexts and can ensure deterministic execution results through snapshot consistency, thereby achieving a structural breakthrough in the "on-chain game engine." Similarly, the deployment and operation of AI agents will also undergo essential improvements due to parallel computing. In the past, we often ran AI agents off-chain and only uploaded their behavioral results to on-chain contracts, but in the future, on-chain, through parallel transaction scheduling, can support asynchronous collaboration and state sharing among multiple AI entities, thereby truly achieving real-time autonomous logic of Agent on-chain. Parallel computing will become the infrastructure for such "behavior-driven contracts," propelling Web3 from "transaction as asset" to a whole new world of "interaction 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, where developers are used to designing logic as single-threaded state changes. However, in a parallel computing architecture, developers will be forced to consider read/write set conflicts, state isolation strategies, and transaction atomicity. They may even introduce architectural patterns based on message queues or state pipelines. This cognitive leap has also spurred the rapid rise of a new generation of toolchains. For example, a parallel smart contract framework that supports transaction dependency declarations, an IR-based optimization compiler, and a concurrent debugger that supports transaction snapshot simulation will all become hotbeds for infrastructure breakthroughs in the new era. At the same time, the continuous evolution of modular blockchains has provided an excellent landing path for parallel computing: Monad can serve as an execution module plugged into L2 Rollup, MegaETH can replace the EVM and be deployed on mainstream chains, Celestia provides data availability layer support, and EigenLayer provides a decentralized validator network, forming a high-performance integrated architecture from underlying data to execution logic.

However, the advancement of parallel computing is by no means smooth-sailing. The challenges it faces are even more structural and difficult to tackle than the opportunities. On one hand, the most fundamental technical challenge lies in "state concurrency consistency assurance" and "transaction conflict resolution strategies." Unlike off-chain databases, on-chain systems cannot tolerate any degree of transaction rollback or state rollback. Any execution conflict requires prior modeling or precise in-flight control. This means that a parallel scheduler must have a strong ability to construct dependency graphs and predict conflicts. It must also design efficient optimistic execution fault tolerance mechanisms; otherwise, the system is prone to "concurrency failure retry storms." This can not only result in reduced throughput under high loads but can also destabilize the chain. Furthermore, the security model of the current multi-threaded execution environment is not yet fully established. Issues such as the precision of inter-thread state isolation mechanisms, new types of reentrancy attacks in asynchronous contexts, and Gas explosions from cross-thread contract calls are all new problems that remain to be solved.

A more covert challenge comes from the ecosystem and psychological aspects. Whether developers are willing to migrate to a new paradigm, whether they can grasp the design methods of parallel models, and whether they are willing to sacrifice some readability and contract auditability for performance gains are the soft issues that truly determine whether parallel computing can form ecosystem potential. Over the past few years, we have seen many high-performance chains lacking developer support gradually fade away, such as NEAR, Avalanche, and even some Cosmos SDK chains that far outperform the EVM in terms of performance. Their experiences remind us that without developers, there is no ecosystem; without an ecosystem, good performance is just a castle in the air. Therefore, parallel computing projects must not only build the strongest engine but also create the most gentle ecosystem transition path. They must make "performance plug-and-play" rather than "performance as a cognitive threshold."

Ultimately, the future of parallel computing is both a triumph of system engineering and a trial of ecological design. It will force us to reexamine "what is the essence of a chain": is it a decentralized settlement engine or a globally distributed real-time state synchronizer? If it is the latter, then the abilities previously considered as "technical details of the chain" such as state throughput, transaction concurrency, and contract responsiveness will eventually become the primary indicators defining the value of the chain. And the parallel computing paradigm that truly accomplishes this transition will also become the most core, most compounding infrastructure primitive in this new era, with its impact far beyond a technical module, possibly constituting a turning point in the overall computing paradigm of Web3.

Chapter 6: Conclusion - Is Parallel Computing the Best Path for Web3 Native Scalability?

Among all the paths exploring the performance boundaries of Web3, parallel computing is not the easiest to implement, but it may be the closest to the essence of blockchain. It does not achieve scalability by moving off-chain, nor does it rely on sacrificing decentralization for throughput. Instead, it attempts to reconstruct the execution model itself within the atomicity and determinism of the chain, from the transaction layer, contract layer, to the virtual machine layer, reaching the root of performance bottlenecks. This "on-chain native" scalability approach not only preserves the most fundamental trust model of blockchain but also reserves sustainable performance soil for more complex on-chain applications in the future. Its challenge lies in structure, and its allure also lies in structure. If modular refactoring is "the architecture of the chain," then what parallel computing is reconstructing is "the soul of the chain." Perhaps this is not a shortcut for short-term success but likely the only sustainable correct path for long-term evolution of Web3. We are witnessing a similar architectural transition from single-core CPUs to multi-core/threaded OS, and the appearance of a Web3 native operating system may be hidden within these experiments of on-chain parallelism.

This article is a contributed submission and does not represent the views of BlockBeats.

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