Why Parallel Execution Engines Are an Energy Mirage

Parallelism is bottlenecked by the serial portion of any transaction. A chain with 95% parallelizable code still hits a maximum 20x speedup regardless of added cores. Real-world blockchains have far lower parallelizability due to shared state dependencies.

• Theoretical Limit: Speedup = 1 / (S + P/N) where S is serial fraction.
• Real-World Impact: Contention for hot accounts (e.g., USDC, stETH) forces sequential processing, nullifying core advantages.

Adding cores requires shared memory access. Contention for global state (e.g., a popular NFT mint, DEX pool) creates cache-coherence traffic, turning parallel execution into a memory bandwidth fight.

• Hardware Reality: Bandwidth per core decreases as core count increases.
• Performance Cliff: Under load, systems like Aptos, Sui, and Solana see latency spikes and failed transactions as contention saturates memory subsystems.

To find parallelism, engines like Solana's Sealevel or Monad speculatively execute transactions, wasting energy and compute on rollsbacks when dependencies are discovered.

• Energy Inefficiency: ~40% of executed work can be discarded, increasing total system energy use per final transaction.
• Complexity Cost: Requires sophisticated schedulers and conflict detection, increasing node operator costs and centralization pressure.

Parallel execution lowers marginal cost per transaction, incentivizing state growth. This accelerates the state bloat problem, increasing hardware requirements for validators and pushing network centralization.

• Storage Spiral: Higher TPS directly correlates with faster state growth (terabytes/year).
• Centralization Force: Only well-funded operators can afford the NVMe arrays and high-bandwidth memory required, defeating decentralization.

The capital cost (hardware) and operational cost (energy) of parallel execution are borne by validators, but the fee market often fails to compensate them. This creates a long-term sustainability crisis.

• CAPEX Spike: Validator requirements jump from $10k to $100k+ setups.
• Fee Market Failure: Users demand low fees, creating a subsidy gap that leads to chain security erosion.

True scaling requires specialization, not brute-force parallelism. Execution layers (Rollups), Data Availability layers (Celestia, EigenDA), and Settlement layers distribute the load according to physical constraints.

• Efficiency Gain: Each layer optimizes for its specific task (compute vs. data vs. security).
• Sustainable Scaling: Avoids the single-machine scaling limits of monolithic parallel engines like Aptos or Monad.

Parallel engines like Aptos' Block-STM and Sui's Narwhal/Bullshark can process transactions concurrently, but the block gas limit remains a hard ceiling. Throughput is gated by the single-threaded execution of the most complex transaction in the batch.\n- Real-World Cap: Theoretical 160k TPS collapses to ~5-10k TPS under mixed workloads.\n- Analogy: Adding more checkout lanes doesn't help if one customer has a cart with 10,000 items.

True parallelism requires independent transactions. In practice, DeFi and NFT apps create hot state (e.g., popular liquidity pools, NFT mints) that forces sequential execution. The engine spends more cycles on dependency detection and re-execution than on actual parallel work.\n- Overhead Cost: 30-40% of execution time can be wasted on scheduling and conflict resolution.\n- Result: Marginal gains after ~32 cores, making ultra-parallel hardware wasteful.

Protocols claim developers don't need to think about parallelism. In reality, to achieve advertised performance, devs must manually structure data using Move's object model (Sui) or carefully design resource accounts (Aptos) to minimize contention. This is a significant cognitive and engineering tax.\n- Reality: High-performance dApp design is now a distributed systems problem.\n- Outcome: Most dApps will see negligible speed-up versus EVM chains without major rewrites.

Monad acknowledges the parallel execution mirage. Its innovation is a pipelined, parallel EVM that combines speculative execution with a state-of-the-art consensus mechanism (MonadBFT) and a custom deferred execution architecture. It optimizes the entire stack, not just execution.\n- Key Insight: ~10,000 TPS target is achieved by redesigning the EVM state tree (MonadDB) for parallel reads/writes.\n- Contrast: This is a full-stack engineering approach vs. Aptos/Sui's language/runtime focus.

Solana's single global state and pipelined transaction processing set the practical benchmark. Its Sealevel VM schedules transactions across cores at the instruction level, avoiding the dependency detection overhead of optimistic parallel VMs. The hardware requirement is the trade-off.\n- Metric: Sustains 2-5k real TPS with ~400ms finality.\n- Lesson: Aggressive vertical integration (hardware, client, protocol) often beats a clever VM alone.

The true scaling path is specialization. Fuel as a parallel execution layer and Eclipse as a customizable SVM rollup separate execution from consensus/data availability. This lets parallel VMs compete on a level playing field, optimized for specific use cases.\n- Architecture: Sovereign execution + shared security (e.g., from Celestia, EigenLayer).\n- Outcome: Parallel engines become a rollup runtime option, not a monolithic L1 bet.

Parallelism assumes independent transactions, but DeFi's composability creates massive shared-state contention (e.g., a popular Uniswap pool). The engine must serialize these, collapsing theoretical gains.

• Real-World Throughput often matches or barely exceeds optimized sequential execution.
• Overhead Cost: Dynamic dependency checking and scheduling consume ~30-40% of the performance uplift.

Faster execution encourages more speculative transactions and complex state interactions, directly increasing the network's storage and synchronization burden.

• Jevons Paradox: Efficiency gains are consumed by increased demand, leading to net higher total system energy use.
• Node Requirements: Archive nodes and validators face exponentially growing hardware demands, centralizing infrastructure.

These parallel L1s highlight the trade-offs. Sui's object model minimizes contention for simple assets but struggles with complex composability. Aptos' Block-STM optimistically executes everything then re-executes on conflict.

• Energy Per Tx is lower only in ideal, contention-free benchmarks.
• Real-DeFi Load sees both chains' performance converge towards ~10k TPS, far below theoretical peaks, for similar energy cost per useful transaction.

Offloading execution to a parallel layer (e.g., a parallelized rollup) doesn't eliminate energy cost; it shifts and often multiplies it across the stack.

• Data Availability energy from Celestia or EigenDA must be accounted for.
• Verification Overhead: The L1 (e.g., Ethereum) still expends energy to verify proofs of this parallel work, adding a fixed ~100k+ gas cost per batch.