Artela White Paper Interpretation: Unique Parallel Execution Stack + Flexible Block Space

In March this year, the scalable L1 blockchain network Artela launched EVM++, an upgrade to the next generation EVM execution layer technology. The first "+" in EVM++ represents "Extensibility", which is the scalability achieved through Aspect technology. This technology supports developers to create on-chain custom programs in the WebAssembly (WASM) environment. These programs can be combined with EVM Collaborate to provide high-performance, customized application-specific extensions to dApps. The second "+" represents "Scalability", which means that network processing capabilities and efficiency are greatly improved through parallel execution technology and flexible block space design. WebAssembly (WASM) is an efficient binary code format that enables near-native execution speed performance in web browsers and is particularly suitable for processing computationally intensive tasks such as AI and big data processing. On June 21, Artela released a white paper detailing how it enhances blockchain scalability by developing a parallel execution stack and introducing elastic block space based on elastic computing.

The Importance of Parallel Processing

In the traditional Ethereum Virtual Machine (EVM), all smart contract operations and state transitions must be consistent across the entire network. This requires all nodes to execute the same transactions in the same order. Therefore, even if there is actually no dependency between certain transactions, they must be executed one after another in the order in the block, that is, serial processing. This method not only causes unnecessary waiting, but is also inefficient.

Parallel processing allows multiple processors or multiple computing cores to perform multiple computing tasks or process data at the same time, significantly improving processing efficiency and shortening running time, especially for complex or large-scale calculations that can be broken down into multiple independent tasks question.

Parallel EVM is an extension or improvement to the traditional Ethereum Virtual Machine. It can execute multiple smart contracts or contract function calls simultaneously, significantly improving the throughput and efficiency of the entire network. In addition, it can optimize the efficiency of single-threaded execution. The most direct advantage of parallel EVM is to enable existing decentralized applications to achieve Internet-level performance.

Artela Network and EVM++

Artela is an L1 that improves the scalability and performance of EVM by introducing EVM++. EVM++ is an upgrade to the EVM execution layer technology, integrating the flexibility of EVM and the high-performance features of WASM. This enhanced version of the virtual machine supports parallel processing and efficient storage, allowing more complex and performance-demanding applications to run on Artela.

EVM++ not only supports traditional smart contracts, but can also dynamically add and run high-performance modules on the chain, such as AI agents. These agents can run independently as on-chain co-processors, or directly participate in on-chain games, creating truly trustworthy Programming NPCs.

Artela ensures that the computing power of network nodes can be flexibly expanded according to demand through parallel execution design. In addition, the validator nodes support horizontal expansion, and the network can automatically adjust the size of the computing nodes according to the current load or demand. This expansion process is coordinated by the elastic protocol to ensure sufficient computing resources in the consensus network.

Ensure the scalability of network node computing power through elastic computing, and ultimately achieve elastic block space, allowing large dApps to apply for independent block space according to specific needs. This not only meets the need to expand public block space, but also ensures that large applications performance and stability.

Detailed explanation of Artela’s parallel execution architecture

1. Predictive Optimistic Execution

Predictive Optimistic Execution is one of Artela’s core technologies and is different from other parallel EVMs such as Sei and Monad One of the characteristics. Optimistic execution refers to a parallel execution strategy that assumes there are no conflicts between transactions in the initial state. In this mechanism, each transaction maintains a private version of the state, recording modifications but not finalizing them immediately. After the transaction is executed, a verification phase is performed to check whether there are conflicts with global state changes caused by other parallel transactions in the same period. Once a conflict is detected, the transaction is re-executed.

Predictability refers to analyzing historical transaction data through a specific AI model to predict the dependencies between upcoming transactions, that is, which transactions are likely to access the same data, and group transactions to arrange their execution accordingly order, thereby reducing execution conflicts and duplicate executions. In contrast, in terms of prediction, Sei relies on files with transaction dependencies defined in advance by developers, while Monad uses compiler-level static analysis to generate files with transaction dependencies. Neither has EVM equivalence. , and both lack the adaptive capabilities of Artela's dynamic prediction model based on AI.

2. Asynchronous preloading technology (Async Preloading)

Asynchronous preloading technology is committed to solving the input and output (I/O) bottlenecks caused by state access, with the purpose of improving data access speed and reducing waiting time during transaction execution. . Artela pre-loads the required state data from slow storage (such as hard disk) to fast storage (such as memory) based on predictive models before the transaction is executed. Reduce I/O wait time during execution by loading necessary data ahead of time. When data is loaded and cached in advance, multiple processors or execution threads can access the data simultaneously, further increasing execution parallelism.

3. Parallel Storage

With the introduction of parallel execution technology, although transaction processing can be parallelized, if the data reading, writing and updating speed cannot be improved simultaneously, it will limit the overall system performance. The key factor is that the bottleneck of the system gradually shifts to the storage level. Solutions like MonadDB and SeiDB have begun to focus on storage-level optimization. Artela draws on and integrates a variety of mature traditional data processing technologies to develop parallel storage, further improving the efficiency of parallel processing.

The parallel storage system is mainly designed to address two major issues: one is to achieve parallel processing of storage, and the other is to improve the ability to efficiently record data status to the database. During the data storage process, common problems include data expansion during data writing and increased database processing pressure. In order to effectively deal with these problems, Artela adopts the separation strategy of State Commitment (SC) and State Storage (SS). This strategy divides storage tasks into two parts: one part is responsible for fast processing operations and does not retain complex data structures, thereby saving space and reducing data duplication; the other part is responsible for recording all detailed data information. In addition, in order not to affect performance when processing large amounts of data, Artela adopts a method of merging small pieces of data into large pieces, reducing the complexity of data saving.

4. Elastic Block Space (EBS)

Artela’s Elastic Block Space (EBS) is designed based on the concept of elastic computing and can automatically adjust the number of transactions accommodated in a block based on network congestion. Elastic computing is a cloud computing service model that allows the system to automatically adjust the configuration of computing resources to adapt to changing load requirements. The main purpose is to optimize resource usage efficiency and ensure that additional computing power is quickly provided when demand increases. EBS dynamically adjusts block resources according to the specific needs of dApps and provides independent expansion block space for dApps with high demand, aiming to solve the problem of significantly different blockchain performance requirements for different applications.

The core advantage of EBS is "predictable performance", that is, it can provide predictable TPS for dApps. Therefore, dApps with independent block spaces will receive stable TPS regardless of whether the public block space is crowded or not. In addition, if the contract written by the dApp supports parallelism, it can further achieve higher TPS. It can be said that EBS provides a more stable environment compared to traditional blockchain platforms such as Ethereum and Solana. These traditional platforms often lead to dApp performance degradation when the network is congested, such as during the Inscription boom or during peak DeFi activity. Artela effectively solves such problems through customized and optimized resource management.

To summarize, Artela achieves high scalability and predictablenetwork performance through parallel execution stacks and elastic block space. This parallel execution architecture uses the AI ​​model to precisely predict transaction dependencies, reducing conflicts and repeated executions. In addition, large-scale applications can use dedicated processing power and resources according to their needs, ensuring that stable performance can be maintained even under high network load. This enables the Artela network to support more complex application scenarios, such as real-time big data processing and complex financial transactions.

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