The Ultimate Path to Native Scaling: A Deep Dive into Web3 Parallel Computing!
Since Bitcoin’s inception, blockchain systems have faced a core challenge: scaling. Bitcoin’s transaction processing capacity is limited to fewer than 10 transactions per second, and Ethereum struggles to break through the performance bottleneck of dozens of transactions per second. This is in stark contrast to traditional Web2 systems, which can handle tens of thousands of transactions per second.
The past decade has seen numerous scaling attempts, from Bitcoin’s scaling wars to Ethereum’s sharding vision, from state channels and Plasma to Rollup and modular blockchain designs. Rollup, currently the most widely accepted scaling paradigm, has achieved significant improvements in transaction processing capacity while preserving Ethereum’s security. However, it has not reached the true limit of blockchain performance, particularly in terms of execution — the throughput capacity of blocks themselves which remains constrained by the outdated serial processing paradigm.
The Five Paths to Scaling
There are five basic approaches to scaling:
- On-chain scaling: increasing block size, reducing block time, or optimizing data structures and consensus mechanisms. This approach has been explored in Bitcoin’s scaling debates and early high-performance public chains like EOS and NEO.
- Off-chain scaling: state channels and sidechains, which transfer most transaction activity to off-chain and only record final results on the main chain.
- Layer 2 Rollup: executing transactions off-chain and verifying them on-chain, achieving a balance between decentralization and high performance.
- Modular blockchain architecture: decoupling core functions like execution, consensus, and data availability, and combining them into an extensible network.
- On-chain parallel computing: restructuring the execution engine to achieve concurrent processing of atomic transactions within a single chain.
The Parallel Computing Spectrum
Parallel computing can be categorized into five technical paths:
- Account-level parallelism: Solana’s approach, which analyzes transactions involving account collections and executes them concurrently if there are no conflicts.
- Object-level parallelism: introducing resource and module semantic abstraction to schedule concurrency at a finer granularity.
- Transaction-level parallelism: building a transaction dependency graph and scheduling concurrent execution.
- Virtual machine-level parallelism: embedding concurrent execution capabilities directly into the VM’s instruction scheduling logic.
- Instruction-level parallelism: scheduling and analyzing each bytecode instruction like a CPU executes x86 instructions.
Monad vs MegaETH: Two Mainstream Tracks
Monad and MegaETH represent two different approaches to parallel computing:
- Monad: a “from-scratch” parallel computing chain that redefines the blockchain execution engine’s underlying operation mode.
- MegaETH: an “EVM internal parallel revolution” that embeds parallel computing capabilities into the existing EVM execution engine.
The Future of Parallel Computing
Parallel computing is poised to release its potential as it transitions from paper designs to on-chain implementation. New development paradigms and business models are being redefined around “on-chain high performance.” However, challenges like state concurrency consistency guarantees and transaction conflict handling strategies need to be addressed.
Conclusion
Parallel computing is not the easiest path to implement, but it may be the most suitable for blockchain’s essence. It preserves the core trust model of blockchain and provides sustainable performance for future complex on-chain applications. The difficulty lies in its structure, but its charm also lies in its structure.
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