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

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

Since the birth of blockchain systems, they have faced the core issue of scalability. The performance bottlenecks of Bitcoin and Ethereum struggle to surpass the processing capabilities of the traditional Web2 world. This cannot be resolved simply by adding more servers, but stems from systematic constraints in the underlying design of blockchain - the "decentralization, security, and scalability" trilemma, where not all three can be achieved simultaneously.

In the past decade, we have witnessed countless scaling attempts, from the Bitcoin scaling wars to Ethereum sharding, from state channels to Rollups and modular blockchains. As the most widely accepted scaling paradigm today, Rollups have achieved a significant increase in TPS while alleviating the burden on the main chain. However, it has not touched the true limits of "single-chain performance" at the underlying blockchain level, especially at the execution layer, which is still constrained by the ancient paradigm of on-chain serial computation.

In-chain parallel computing is gradually coming into the industry spotlight. It attempts to thoroughly reconstruct the execution engine while maintaining the atomicity and integrated structure of a single chain, upgrading the blockchain from the "serial execution of transactions one by one" single-threaded model to a "multi-threaded + pipelining + dependency scheduling" high-concurrency computing system. This could not only achieve hundreds of times the improvement in throughput but also become a key prerequisite for the explosion of smart contract applications.

It can be said that parallel computing is not only a "performance optimization method" but also a turning point in the execution model paradigm of blockchain. It challenges the fundamental model of smart contract execution and redefines the basic logic of transaction packaging, state access, calling relationships, and storage layout. If Rollup is about "executing transactions off-chain," then on-chain parallelism is about "building supercomputing kernels on-chain," with the goal of providing truly sustainable infrastructure support for future Web3 native applications.

After the Rollup track has become increasingly homogeneous, on-chain parallelism is quietly becoming a decisive variable in the competition for the new cycle of Layer 1. Performance is no longer just about being "faster", but about the possibility of supporting an entire heterogeneous application world. This is not only a technological competition, but also a battle for paradigms. The next generation of sovereign execution platforms in the Web3 world is likely to emerge from this struggle of on-chain parallelism.

Expansion Paradigm Overview: Five Types of Routes, Each with Its Focus

Scalability, as one of the most important, persistent, and difficult challenges in the evolution of public chain technology, has given rise to almost all mainstream technological paths in the past decade. Starting from the block size debate of Bitcoin, this technical competition about "how to make the chain run faster" has ultimately diverged into five basic routes, each addressing the bottleneck from different angles, with its own technical philosophy, implementation difficulty, risk model, and applicable scenarios.

The first type of route is the most direct on-chain scaling, represented by practices such as increasing block size, shortening block time, or enhancing processing capacity through optimizing data structures and consensus mechanisms. This approach became a focal point in the Bitcoin scaling debate, giving rise to "big block" faction forks like BCH and BSV, and also influenced the design ideas of early high-performance public chains like EOS and NEO. The advantage of this type of 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 rather serves as an auxiliary pairing for other mechanisms.

The second type of route is off-chain scaling, 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 concept of Web2 – trying to keep heavy transaction processing on the periphery, while the main chain does minimal trust verification. Although this approach can theoretically scale throughput infinitely, 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 been able to explode in ecological scale; while multiple designs based on sidechains, such as the POS of a certain trading platform, expose drawbacks in inheriting the security of the main chain even with high throughput.

The third type of route is the currently most popular and widely deployed Layer2 Rollup route. This approach 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 has 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 while keeping data and verification on the main chain, achieving a relative balance between decentralization and high performance. The rapid growth of certain Layer2 projects proves the feasibility of this path, but also exposes medium-term bottlenecks such as excessive reliance on data availability (DA), still high fees, and fragmented development experience.

The fourth type of route is the modular blockchain architecture that has emerged in recent years, represented by projects such as Celestia, Avail, and EigenLayer. The modular paradigm advocates for the complete decoupling of the core functions of a blockchain - execution, consensus, data availability, and settlement - to be accomplished by multiple specialized chains performing different functions, which are then combined into a scalable network through cross-chain protocols. This direction is profoundly influenced by the modular architecture of operating systems and the composability concept of cloud computing, with its advantages lying in the ability to flexibly replace system components and significantly improve efficiency in specific links like DA(. However, its challenges are also very evident: after the modular decoupling, the synchronization, verification, and trust costs 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 far greater than those of traditional chain designs. This model essentially no longer constructs a "chain", but rather builds a "chain network", posing unprecedented thresholds for understanding and operating the overall architecture.

The last category of routes, which is the focus of subsequent analysis in this article, is the on-chain parallel computing optimization path. Unlike the first four categories that mainly perform "horizontal splitting" from a structural perspective, parallel computing emphasizes "vertical upgrading", which means achieving concurrent processing of atomic transactions within a single chain by changing the execution engine architecture. 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. A certain high-performance public chain was one of the first projects to implement the concept of 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 have further attempted to introduce cutting-edge ideas such as pipelined execution, optimistic concurrency, storage partitioning, and parallel decoupling, constructing a high-performance execution kernel akin to a modern CPU. The core advantage of this direction is that it can achieve breakthrough throughput limits without relying on multi-chain architecture, while providing sufficient computational flexibility for complex smart contract execution, serving as an important technological prerequisite for future applications such as AI Agents, large-scale chain games, and high-frequency derivatives.

Looking at the five types of scalability paths mentioned above, the underlying division is actually the systematic trade-off between blockchain performance, composability, security, and development complexity. Rollups excel in consensus outsourcing and security inheritance, modularization highlights structural flexibility and component reuse, off-chain scaling attempts to break through the main chain bottleneck but comes at a high trust cost, while on-chain parallelism focuses on fundamental upgrades to the execution layer, aiming to approach the performance limits of modern distributed systems without disrupting on-chain consistency. Each path cannot solve all problems, but these directions together form a panoramic view of the Web3 computing paradigm upgrade, providing developers, architects, and investors with extremely rich strategic options.

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

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

In the context of the continuous evolution of blockchain scalability technologies, 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 excavation at the execution layer. It relates to the fundamental logic of blockchain operational efficiency, determining a blockchain system's response speed and processing capability when facing high concurrency and various types of complex transactions. Starting from the execution model and 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 not only a continuous refinement process of parallel logic but also a path of increasing system complexity and scheduling difficulty.

The earliest form of account-level parallelism is represented by a certain high-performance public chain paradigm. This model is based on a decoupled design of account-state, which determines the existence of 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 well-suited for handling transactions with clearly defined structures and clear input/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 such as conservative execution and reduced parallelism when faced with complex smart contracts ), such as dynamic behaviors in blockchain games or AI agents (. Additionally, the cross-dependencies between accounts also severely diminish the benefits of parallelism in certain high-frequency trading scenarios. The runtime of this high-performance public chain has achieved high levels of optimization in this regard, but its core scheduling strategy is still limited by the granularity of accounts.

Further refining based on the account model, we enter the technical level of object-level parallelism. Object-level parallelism introduces semantic abstraction of resources and modules, allowing for concurrent scheduling at a finer granularity of "state objects." Some new generation Layer 1 projects are significant explorers in this direction, especially the latter, which defines resource ownership and mutability at compile time through the linear type system of the Move language, thereby allowing 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 and write logic, and naturally serving high heterogeneity scenarios such as gaming, social networking, and AI. However, object-level parallelism also introduces higher language thresholds and development complexity. Move is not a direct replacement for Solidity, and the high cost of ecosystem switching limits the dissemination speed of its parallel paradigm.

Further transaction-level parallelism is a direction explored by a new generation of high-performance chains represented by Monad, Sei, and Fuel. This approach does not treat state or accounts as the smallest parallel unit, but instead constructs a dependency graph around the entire transaction itself. It views transactions as atomic operation units, constructing a transaction graph )Transaction DAG( through static or dynamic analysis, and relies on a scheduler for concurrent pipeline execution. This design allows the system to maximize 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 pipeline scheduling, and out-of-order execution, making chain execution closer to the paradigm of "GPU schedulers." 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 far exceeds that of account or object models, making it a powerful force with the highest theoretical ceiling in the current parallel computing arena.

And the virtual machine-level parallelism directly embeds the concurrent execution capability into the VM's underlying instruction scheduling logic, striving to completely break through the inherent limitations of EVM sequential execution. MegaETH serves as the "super virtual machine experiment" within the Ethereum ecosystem,