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

Author: ChainFeeds 

EVM++ reduces parallel execution conflicts through AI models to accurately predict transaction dependencies, and maintains stable dApp performance through elastic block space.

In March of this year, Artela, a scalability L1 blockchain network, launched EVM++, an upgrade to the next-generation EVM execution layer technology. The first "+" in EVM++ stands for "Extensibility", which is scalability achieved through Aspect technology. This technology supports developers to create on-chain custom programs in the WebAssembly (WASM) environment. These programs can work with EVM to provide high-performance customized application-specific extensions for dApp. The second "+" stands for "Scalability", which greatly improves network processing power and efficiency through parallel execution technology and elastic block space design.

WebAssembly (WASM) is an efficient binary code format that can achieve near-native execution speed performance in web browsers, and is particularly suitable for processing computationally intensive tasks such as AI and big data processing.

Yesterday, 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 some transactions, they must be executed one after another in the order in the block, that is, serial processing. This approach 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 simultaneously, significantly improving processing efficiency and shortening running time, especially for complex or large-scale computing problems that can be decomposed into multiple independent tasks. Parallel EVM is an extension or improvement of the traditional Ethereum virtual machine, which can execute multiple smart contracts or contract function calls simultaneously, significantly improving the throughput and efficiency of the entire network. In addition, it can also 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 characteristics of WASM. This enhanced 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 also can dynamically add and run high-performance modules on the chain, such as AI agents, which can run independently as on-chain coprocessors or directly participate in on-chain games to create truly programmable NPCs.

Artela uses parallel execution design to ensure that the computing power of network nodes can be flexibly expanded according to demand. In addition, the validator node supports horizontal expansion, and the network can automatically adjust the scale of computing nodes according to the current load or demand. This expansion process is coordinated by the elastic protocol to ensure that there are sufficient computing resources in the consensus network. Through elastic computing, the computing power of network nodes can be expanded, and finally elastic block space is realized, allowing large dApps to apply for independent block space according to specific needs. This not only meets the need to expand the public block space, but also ensures the performance and stability of large applications.

Detailed Explanation of Artela's Parallel Execution Architecture

1. Predictive Optimistic Execution

Predictive optimistic execution is one of Artela's core technologies and one of the characteristics that distinguishes it from other parallel EVMs such as Sei and Monad. Optimistic execution refers to a parallel execution strategy that assumes that 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. Predictive means analyzing historical transaction data through a specific AI model to predict the dependencies between transactions to be executed, that is, which transactions may access the same data, and grouping transactions accordingly to arrange their execution order, thereby reducing execution conflicts and duplicate executions. In contrast, in terms of prediction, Sei relies on a file of transaction dependencies defined in advance by the developer, while Monad uses compiler-level static analysis to generate a file of transaction dependencies. Neither of them has EVM equivalence, and both lack the adaptive capabilities of Artela's AI-based dynamic prediction model.

2. Async Preloading

Async Preloading is dedicated to solving the input and output (I/O) bottleneck caused by state access, with the goal of increasing data access speed and reducing waiting time during transaction execution. Before executing a transaction, Artela preloads the required state data from slow storage (such as hard disk) to fast storage (such as memory) based on the prediction model. By loading the necessary data in advance, the I/O waiting time during execution is reduced. When data is preloaded and cached, multiple processors or execution threads can access the data at the same time, further improving the parallelism of execution.

3. Parallel Storage

With the introduction of parallel execution technology, although transaction processing can be parallelized, if the reading, writing and updating speed of data cannot be improved synchronously, it will become a key factor limiting the overall system performance. Therefore, the bottleneck of the system has gradually shifted to the storage level. Solutions such as MonadDB and SeiDB have begun to focus on optimization at the storage level. Artela has borrowed and integrated a variety of mature traditional data processing technologies to develop parallel storage, further improving the efficiency of parallel processing.

The parallel storage system is designed mainly 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. In the process of data storage, common problems include data expansion when writing and increased pressure on database processing. In order to effectively deal with these problems, Artela adopted a separation strategy of state commitment (SC) and state storage (SS). This strategy divides the storage task 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 the method of merging small blocks of data into large blocks, reducing the complexity of data storage.

4. Elastic Blockspace (EBS)

Artela's Elastic Blockspace (EBS) is designed based on the concept of elastic computing, which can automatically adjust the number of transactions contained in the block according to the degree of 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 utilization 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, providing independent expansion block space for dApps with high demand, aiming to solve the problem of significant differences in blockchain performance requirements for different applications. The core advantage of EBS lies in "predictable performance", that is, it can provide predictable TPS for dApps. Therefore, regardless of whether the public block space is crowded or not, dApps with independent block space will obtain stable TPS. In addition, if the contract written by the dApp supports parallelism, higher TPS can be further obtained. 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 cause dApp performance to decline when the network is crowded, such as during the inscription boom or the peak of DeFi activities. Artela effectively solves such problems through customized and optimized resource management.

In summary, Artela achieves highly scalable and predictable network performance through parallel execution stack and elastic block space. This parallel execution architecture accurately predicts transaction dependencies through AI models, reducing conflicts and duplicate executions. Moreover, large applications can use their own processing power and resources as needed, ensuring stable performance 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.