Why Parallel Execution Isn't a Feature—It's a Prerequisite

Sequential EVM chains like Ethereum and Arbitrum cannot process independent transactions (e.g., a Uniswap swap and an Aave deposit) simultaneously. This creates artificial congestion and high fees, capping Total Value Locked (TVL) and user growth.

• Market Consequence: Limits composability and forces protocols like GMX and Uniswap onto expensive, congested L2s.
• Performance Gap: Sequential chains max out at ~50-100 TPS, while parallelized chains like Solana and Sui target 10,000+ TPS.

User intents (e.g., 'buy X token at best price') require solving across multiple domains—DEXs, bridges, liquidity pools. Sequential execution makes this slow and expensive. Parallel execution is the only way to efficiently resolve intents in real-time.

• Key Driver: The rise of intent-based protocols like UniswapX, CowSwap, and Across.
• Requirement: Solvers need sub-second access to parallelized liquidity across chains, a feat impossible on sequential blockchains.

Web2 users expect instant, seamless interactions. Sequential blockchains introduce perceptible lag and unpredictable costs, a non-starter for gaming, social, or commerce apps with millions of users.

• Adoption Barrier: Games like Star Atlas or social apps need ~500ms finality and sub-cent fees.
• Market Trend: The shift towards consumer crypto and parallel EVMs like Monad, Sei, and Aptos is a direct response to this demand. Infrastructure like SVM and MoveVM are built for parallelism from first principles.

Ethereum's single-threaded EVM and similar architectures serialize all transactions, creating artificial congestion and volatile fees. This model fails at scale.

• Gas wars inflate costs for simple swaps during peak demand.
• Throughput caps at ~15-30 TPS limit complex DeFi and gaming applications.
• Poor hardware utilization leaves modern multi-core servers mostly idle.

These L1s use a Move-based data model to explicitly declare transaction dependencies, enabling safe parallel execution of non-conflicting operations.

• Deterministic concurrency: Runtime schedules transactions based on accessed memory locations.
• Horizontal scaling: Throughput increases linearly with available cores and independent transactions.
• Pipelined execution: Separation of validation, execution, and storage boosts efficiency.

These chains assume transactions are independent and optimistically execute them in parallel, rolling back only the conflicting subset via a runtime scheduler.

• SeaLevel runtime: Schedules across all cores at the hardware level.
• Local Fee Markets: Isolated congestion prevents network-wide gas spikes.
• Proven at scale: Supports high-frequency DEXs like Raydium and Jupiter with ~3k sustained TPS.

These projects retrofit parallelism onto the EVM ecosystem, offering compatibility with existing tooling while breaking the sequential bottleneck.

• Monad: Uses parallel execution, async I/O, and MonadDB to target 10k+ TPS.
• Neon EVM: A Solana-based L2 that executes EVM transactions in parallel as Solana programs.
• Key benefit: Leverages Ethereum's $50B+ DeFi TVL and developer base without the performance tax.

Parallel execution introduces new challenges that sequential chains don't face, requiring sophisticated engineering solutions.

• Synchronization overhead: Managing locks and dependencies can negate performance gains if not designed perfectly.
• Accelerated state growth: Higher throughput can lead to massive state expansion, challenging node operators.
• Debugging hell: Non-deterministic execution order makes reproducing bugs and building indexers significantly harder.

For any new L1 or high-performance L2, parallel execution is no longer an optimization—it's a foundational requirement for mainstream adoption.

• User Expectation: Apps require sub-second finality and sub-cent costs.
• Market Reality: Solana, Aptos, and Sui have set a new baseline; competing without parallelism is non-viable.
• Future-Proofing: Enables AI agents, fully on-chain games, and high-frequency finance that are impossible on sequential VMs.

The EVM's single-threaded design forces all transactions into a global queue, creating artificial congestion and capping throughput. This is the root cause of high fees and poor UX during demand spikes.

• Global State Contention: Every tx must wait for the previous one to finish, even if they are unrelated.
• Wasted Capacity: Idle compute resources while the chain processes unrelated transfers sequentially.

Modern runtimes like Sui's Move and Aptos' Block-STM treat transactions as independent until proven otherwise. They execute speculatively in parallel and only serialize operations that touch the same state.

• Optimistic Concurrency: Assume no conflicts, validate, and re-execute only if needed.
• Linear Scalability: Throughput increases near-linearly with more cores, as most user actions (e.g., NFT mints, swaps) are independent.

Parallel execution isn't about bragging rights on benchmarks; it's the only way to deliver predictable, low-cost transactions for millions of users. Projects like Monad and Sei v2 are building entire L1s around this premise.

• Sub-second Finality: Parallel processing slashes block times and confirmation latency.
• Sub-cent Fees: Efficient resource utilization drives transaction costs toward the marginal cost of compute, not auction-based scarcity.

The frontier is bringing parallelism to the existing EVM ecosystem without fracturing liquidity. Neon EVM on Solana and Polygon's upcoming parallel EVM demonstrate that the stack is evolving.

• EVM-Equivalent VMs: Execute Solidity smart contracts in parallel by analyzing bytecode for dependencies.
• Backwards Compatibility: Developers don't need to learn new languages; the runtime handles the optimization.