Artela白皮书解读：独特的并行执行堆栈+弹性区块空间

今年 3 月，可扩展性 L1 区块链网络Artela推出了 EVM++，这是针对下一代 EVM 执行层技术的升级。 EVM++ 中的第一个「+」代表了「Extensibility」，即通过 Aspect 技术实现的可扩展性，这一技术支持开发者在 WebAssembly（WASM）环境中创建链上自定义程序，这些程序可以与 EVM 协作，为 dApp 提供高性能的定制化应用特定扩展。第二个「+」则代表了「Scalability」，即通过并行执行技术和弹性区块空间的设计大幅提升网络处理能力和效率。

WebAssembly（WASM）是一种高效的二进制代码格式，能够在 Web 浏览器中实现接近本地执行速度的性能，特别适合处理计算密集型任务，如 AI 和大数据处理。

昨日，Artela发布了白皮书，详细介绍了其如何通过开发并行执行堆栈和引入基于弹性计算的弹性区块空间来增强区块链可扩展性。

并行处理的重要性

在传统的以太坊虚拟机（EVM）中，所有的智能合约操作和状态转换都必须在全网范围内保持一致。这就要求所有的节点都按照相同的顺序执行相同的交易。因此，即使某些交易之间实际上没有依赖关系，它们也必须按照区块中的顺序一个接一个地执行，也就是串行处理。这种方法不仅造成了不必要的等待，而且效率低下。

并行处理允许多个处理器或多个计算核心同时执行多个计算任务或处理数据，显著提高处理效率和缩短运行时间，尤其是对于那些可以分解成多个独立任务的复杂或大规模计算问题。并行 EVM 是对传统以太坊虚拟机的一个扩展或改进，能够同时执行多个智能合约或合约函数调用，显著提高整个网络的吞吐量和效率。此外，它还可以优化单线程执行时的效率。并行 EVM 最直接的优势是让现有的去中心化应用，实现互联网级别的性能。

Artela 网络与 EVM++

Artela 是一个通过引入 EVM++ 来提高 EVM 的扩展性和性能的 L1。EVM++ 是对 EVM 执行层技术的升级，整合了 EVM 的灵活性和 WASM 的高性能特性。这种增强版的虚拟机支持并行处理和高效存储，使得更复杂和对性能要求更高的应用能够在 Artela 上运行。EVM++ 不仅支持传统智能合约，还能在链上动态添加和运行高性能模块，例如 AI 代理，这些代理可以作为链上协处理器独立运行，或直接参与到链上游戏中，创造真正可编程的 NPC。

Artela 通过并行执行设计来确保网络节点的计算能力可以根据需求灵活扩展。此外，验证器节点支持水平扩展，网络能够根据当前的负载或需求自动调整计算节点的规模，这一扩容过程由弹性协议协调，以确保共识网络中的计算资源充足。通过弹性计算保证网络节点算力可扩展，最终实现弹性区块空间，允许大型 dApp 根据特定需求申请独立的区块空间，这不仅满足了扩展公共区块空间的需要，还确保了大型应用的性能和稳定性。

Artela 的并行执行架构详解

1. 预测性乐观执行（Predictive Optimistic Execution）

预测性乐观执行是 Artela 的核心技术之一，也是不同于 Sei 、Monad 等其他并行 EVM 的特点之一。乐观执行指的是一种并行执行策略，假设初始状态下事务之间没有冲突。在这种机制中，每个事务都保持一个私有的状态版本，记录修改但不立即最终确定。事务执行完毕后，进行一次验证阶段，检查是否存在与同时期其他并行事务所引起的全局状态变化的冲突。一旦检测到冲突，就会重新执行事务。预测性是指通过特定的 AI 模型分析历史交易数据，来预测即将执行的交易之间的依赖关系，即哪些交易可能会访问相同的数据，并据此将交易分组安排它们的执行顺序，从而减少执行冲突和重复执行。相比之下，在预测方面，Sei 依赖于开发者提前定义好的交易依赖关系的文件，而 Monad 是采用编译器级别的静态分析生成交易依赖关系的文件，两者都不具备 EVM 等效性，并且都缺乏 Artela 基于 AI 的动态预测模型的自适应能力。

2. 异步预加载技术（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) into fast storage (such as memory) based on predictive models before the transaction is executed. Reduce I/O waiting time during execution by loading necessary data in advance. 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 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 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, the ability to 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.

In summary, Artela achieves highly scalable and predictable network performance through parallel execution stacks and elastic block space. This parallel execution architecture uses AI models to accurately predict transaction dependencies, reducing conflicts and duplicate executions. In addition, large applications can have dedicated processing capabilities and resources as needed, ensuring stable performance even under high network loads. 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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