Technical Analysis of Artela: Why "Parallel EVM" Matters in the Battle for Ethereum EVM Ecosystem Survival?
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Technical Analysis of Artela: Why "Parallel EVM" Matters in the Battle for Ethereum EVM Ecosystem Survival?
"Parallel EVM" is the last stand for EVM chains against high-performance layer-1 chains, a battle关乎the survival of the Ethereum EVM ecosystem.
Navigating Web3 tides with focused insightsAuthor: Haotian
Recently, Paradigm made a significant bet by leading a $225 million funding round for Monad, sparking strong market interest in "parallel EVM." So, what problem does "parallel EVM" actually solve? What are the bottlenecks and key challenges in developing parallel EVM? In my view, "parallel EVM" represents the final stand for EVM-based chains to counter high-performance Layer1 blockchains—a survival battle for the Ethereum EVM ecosystem. Why? Let me explain:
Because the Ethereum EVM virtual machine can only process transactions "sequentially," this inherently limits the performance of EVM-compatible Layer1 chains as well as EVM-compatible Layer2 chains, since they all fundamentally rely on the same framework for state processing and transaction finality.
However, high-performance Layer1 chains like Solana, Sui, and Aptos have native advantages in parallel execution. Under this context, EVM-genesis chains must address their inherent lack of parallelism if they are to compete head-on with these high-performance public chains. How can this be achieved? From a technical standpoint, let me use @Artela_Network, an emerging parallel EVM chain, as an example:
1) Enhanced EVM Layer1 chains such as Monad, Artela, and SEI significantly increase TPS while enabling parallel transaction execution within a near-EVM environment, all while maintaining high compatibility with EVM. These independent parallel EVM Layer1 chains have their own consensus mechanisms and technical features but still aim to be compatible with and expand the EVM ecosystem—essentially reconstructing EVM chains through a "change of lineage," yet still serving the EVM ecosystem;
2) Scalable Layer2 EVM-compatible chains like Eclipse and MegaETH leverage the independent consensus and "pre-processing" capabilities of Layer2s. Before batching large volumes of transactions onto the mainnet, they can filter and process transaction states, and even choose any other chain's execution layer to finalize those states. This is equivalent to abstracting EVM into a pluggable execution module, allowing selection of the optimal "execution layer" based on needs, thereby achieving parallelism. However, while such solutions serve EVM, they go beyond the traditional EVM framework;
3) Equivalent Alt-Layer1 chains such as Polygon and BSC have achieved some degree of parallel EVM processing, but only through algorithmic-level optimizations. They haven't deeply optimized the consensus or storage layers, so their parallel capability should be seen more as a specific feature rather than a fundamental solution to EVM’s parallelization problem;
4) Divergent Non-EVM parallel chains like Aptos, Sui, and Fuel do not truly implement EVM chains. Instead, building on their native high-concurrency execution capabilities, they achieve EVM compatibility via middleware or encoding translation methods. Take Starknet—an Ethereum Layer2—as an example. Thanks to Cairo language support and account abstraction, Starknet has parallel execution abilities, but its EVM compatibility requires a special bridge. Most Non-EVM chains face similar issues when bridging parallel capabilities to EVM environments.
These four approaches each have different focuses: parallel Layer2s emphasize modular flexibility in combining execution layers; EVM-Compatible chains highlight customized functionality; other Non-EVM chains mainly seek to capture Ethereum's liquidity through EVM compatibility. Only one path remains truly focused on consolidating the EVM ecosystem at its foundation—enhanced EVM Layer1 chains.
Then, what's crucial for building powerful parallel EVM Layer1 public chains? How can we rebuild EVM chains while still serving the EVM ecosystem? Two key aspects:
1) The ability to access and perform I/O operations for reading from and writing to state. Simply reordering and scheduling transactions cannot fundamentally improve parallel processing. Since data read/write consumes time, deeper improvements require techniques like caching, data sharding, or even distributed storage—optimizing both speed and conflict resolution at the foundational level of state storage and retrieval;
2) Efficient network communication, data synchronization, algorithm optimization, VM enhancement, and separation of computation and I/O tasks—all requiring holistic optimization across consensus-layer components. This involves deep architectural changes affecting every aspect of underlying components and collaboration workflows, ultimately enabling fast response times, controllable computational costs, and highly accurate parallel transaction processing;
Specifically, what technological innovations and framework optimizations do parallel EVM Layer1 projects need to make to achieve "parallel EVM"?
To fully realize resource coordination and optimization at the architectural foundation, Artela introduced Elastic Computing and Elastic Block Space. How should we understand these? Elastic Computing allows the network to dynamically allocate and adjust computing resources based on demand and load; Elastic Block Space enables dynamic adjustment of block size according to transaction volume and data size in the network. The entire elastic design works much like escalators in a mall that automatically sense foot traffic—very intuitive and sensible.
As mentioned earlier, State I/O disk read performance is critical for parallel EVM. Chains like Polygon and BSC achieve 2–4x efficiency gains through algorithmic-level "parallelism," but this only scratches the surface with algorithmic tweaks—no deep optimization of consensus or storage layers. What would true deep optimization look like?
To tackle this, Artela draws inspiration from database technologies, enhancing both state reading and writing. For state writes, it adopts Write-Ahead Logging (WAL): before committing a state change to disk, the change is first recorded in a log and committed to memory—this effectively completes the "write" operation asynchronously, avoiding immediate disk I/O during state updates, thus reducing disk I/O pressure. For state reads, it also applies asynchronous strategies through predictive preloading: analyzing historical contract execution patterns to anticipate which states will be needed next and loading them into memory ahead of time, improving I/O request efficiency.
In short, this is an algorithm that trades memory space for execution time, fundamentally boosting the EVM virtual machine’s parallel processing capacity and addressing state conflict issues at the root.
Beyond this, Artela introduces Aspect-based modular programming to better manage complexity and boost developer productivity. It supports WASM encoding parsing to enhance programming flexibility and provides low-level API access, ensuring secure isolation at the execution layer. This empowers developers to efficiently build, debug, and deploy smart contracts on Artela, unlocking customization and extensibility. Notably, developers are incentivized to optimize code toward parallelizability at the smart contract level—after all, minimizing state conflicts means every contract call logic and algorithm matters.
In summary,
It's clear that the concept of "parallel EVM" essentially revolves around optimizing the execution process of transaction states. @monad_xyz claims to handle up to 10,000 transactions per second—their core technology leverages specialized databases, developer-friendliness, deferred execution consensus, and superscalar pipelining to enable massive-scale parallel transaction processing. The underlying logic isn't vastly different from Artela’s elastic computing and asynchronous I/O operations.
But what I really want to emphasize is that these high-performance parallel EVM chains are, in essence, outcomes of integrating Web2 products and engineering practices—drawing directly from proven Web2 techniques used to handle high-load traffic spikes in mature application markets.
Looking toward a distant future of mass adoption, "parallel EVM" is indeed the foundational infrastructure enabling the EVM ecosystem to reach broader Web2 markets—and its bullish reception from capital markets is therefore entirely justified.
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Haotian | CryptoInsight
