Parallel execution is meaningless without parallel state access. A chain can advertise 100k TPS from parallelized EVMs, but if all transactions contend for the same hot storage slot, performance collapses to single-threaded speed.
solana-and-the-rise-of-high-performance-chains
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Why Parallel Execution is Meaningless Without Parallel State Access
A deep dive into the fundamental bottleneck of high-performance blockchains. Having 1000 cores is irrelevant if all transactions contend for a single global state lock. True scalability requires concurrency designed at the state layer, not just the execution layer.
introduction
THE BOTTLENECK
The Core Contradiction of High-Performance Chains
Parallel transaction execution is a marketing metric that fails without a corresponding architecture for parallel state access.
The state access pattern determines real throughput. Most high-value DeFi operations—like swaps on Uniswap V3 or liquidations on Aave—create contention for a handful of global state keys, serializing the entire execution pipeline.
Solana's performance stems from its architecture, which treats state as a global database. Its runtime explicitly models read-write sets, allowing the scheduler to group non-conflicting transactions, a concept Ethereum's EIP-6480 is exploring.
Evidence: Aptos and Sui build on this principle with Move and object-centric models, but face adoption friction. The contradiction is that optimizing for parallel execution often requires sacrificing the composability that defines Ethereum's ecosystem.
key-trends
THE BOTTLENECK
The State Access Arms Race
Parallel execution engines are useless if they're all waiting in line to read and write the same global state.
01
The Global Singleton Bottleneck
Traditional EVM chains treat state as a single, globally locked database. Even with 100 parallel threads, they all queue for the same storage slot, creating contention and negating performance gains.
- Contention Kills Throughput: Parallelism fails when multiple transactions touch the same popular token (e.g., USDC, WETH).
- Wasted Resources: Idle CPU cores waiting on I/O, a classic Amdahl's Law failure.
0-2x
Real Speedup
>90%
Idle CPU
02
The Solution: Sharded State & Access Lists
Break the global lock by partitioning state into independent shards. Transactions declare their access sets upfront, allowing the scheduler to run non-conflicting ones in parallel.
- Aptos Move & Sui's Objects: Native data models where assets are owned objects, enabling conflict-free parallel execution.
- Solana's SeaLevel: Scheduler uses declared accounts to find parallelizable transactions, achieving ~50k TPS.
- Monad's Async Execution: Deferred state writes with a monolithic state design, a different architectural gamble.
10k-50k
Peak TPS
~100ms
Finality
03
The New Frontier: Parallelized EVMs (EigenLayer, Monad, Neon)
Projects are retrofitting parallelism onto the EVM by modifying the client to analyze and schedule transactions based on state access patterns.
- EigenLayer's EigenDA: Provides a high-throughput data availability layer, but execution parallelism is still an L2 problem.
- Monad: Forked Go-Ethereum client with parallel execution, pipelining, and a custom state database for ~10k TPS.
- Neon EVM: Ethereum Smart Contracts on Solana, leveraging its native parallel scheduler for existing Solidity dApps.
~10x
vs Base EVM
$1B+
Collective TVL
04
The Verdict: Execution vs. State Architecture
True scalability requires rethinking data structures, not just execution threads. The winner will be the chain that minimizes cross-shard communication for common operations.
- Move-based chains (Aptos, Sui): Bet on a new programming model for native parallelism.
- Parallel EVMs: Bet on backward compatibility, accepting the overhead of analyzing EVM bytecode for conflicts.
- The Trade-off: Developer onboarding vs. theoretical max throughput.
2-5 yrs
Arch Lock-in
100x Gap
Potential TPS
deep-dive
THE BOTTLENECK
From Global Lock to Localized State
Parallel execution is a hardware optimization that fails without a corresponding software architecture for parallel state access.
Parallel execution is meaningless without parallel state access. A blockchain's performance is defined by its slowest component, which is always state I/O, not CPU cycles. Solana's Sealevel and Aptos' Block-STM are execution engines that stall if transactions contend for the same hot accounts like a popular NFT mint or a Uniswap pool.
The global state lock persists. Most L1s and L2s use a single, monolithic state tree (a Merkle Patricia Trie). This creates a serialization point for all state reads and writes, negating any gains from parallelized transaction processing. The bottleneck shifts from computation to a single-threaded database commit.
Sharding is the only solution. Systems like Monad and Sei v2 implement parallelized state access via sharded state trees or optimistic concurrency control. This allows independent state updates to proceed simultaneously, turning the state database into a true multi-core resource. The design mirrors distributed systems like Google's Spanner.
Evidence: The MonadDB benchmark. Monad demonstrates this by separating execution from state commitment, claiming 10,000 real TPS. The metric that matters is not theoretical execution speed but committed state updates per second, which requires breaking the global lock.
THE STATE BOTTLENECK
Architectural Comparison: Execution vs. State Concurrency
This table compares the core architectural approaches to transaction processing, highlighting why parallel execution is ineffective without corresponding state access concurrency.
| Architectural Feature / Metric | Sequential Execution (e.g., Ethereum L1) | Parallel Execution, Shared State (e.g., Solana, Sui) | Parallel Execution, Partitioned State (e.g., Aptos, Monad, Fuel) |
|---|---|---|---|
Transaction Execution Model | Single-threaded | Multi-threaded (e.g., Sealevel) | Multi-threaded (e.g., Block-STM) |
State Access Concurrency | None (Linearized) | Optimistic (Shared Mutable State) | Deterministic (Sharded/Partitioned State) |
State Conflict Resolution | Not applicable | Runtime abort & re-execute (pessimistic/optimistic) | Runtime abort & re-execute (optimistic) or pre-declared keys |
Theoretical Peak TPS (est.) | ~15-45 | ~50k-65k (Solana), ~297k (Sui) | ~160k+ (Aptos), ~10k+ (Fuel V1), ~1M+ (Monad target) |
Developer Complexity for Speed | None | High (Must manage dynamic state conflicts) | Medium (Can pre-declare access sets for optimal speed) |
State Growth Bottleneck | Global state size impacts all nodes | Global state size impacts all nodes; requires aggressive state expiry | Scales with number of state shards/partitions |
Consensus Coupling | Tightly coupled (Execution blocks consensus) | Tightly coupled | Loosely coupled (e.g., Monad's pipelining, Fuel's parallel DA) |
Real-World Throughput Determinism | Deterministic, but low | Non-deterministic (varies with conflict rate) | More deterministic with proper access declaration |
counter-argument
THE BOTTLENECK
The Optimistic Concurrency Fallacy
Parallel execution is a marketing term that ignores the fundamental serialization of state access, which remains the true performance ceiling.
Parallel execution is meaningless without parallel state access. A blockchain's throughput is gated by the speed of its state database, not its CPU core count. Solana's Sealevel and Sui's Move demonstrate this by focusing on state access patterns first, execution second.
The real bottleneck is state contention. Most high-throughput applications involve shared hot accounts like USDC or a major NFT mint. This creates serialization points where all parallel threads must queue, collapsing theoretical gains. This is why Aptos' Block-STM uses optimistic execution with re-execution.
Evidence: Benchmarks showing 100k TPS use synthetic, non-contended workloads. Real-world DeFi activity on Solana or Monad will hit the RocksDB/state trie limit long before CPU saturation. The industry's focus on parallel EVMs misses the state access problem entirely.
takeaways
THE BOTTLENECK IS STATE
TL;DR for Architects
Parallel execution is a marketing term; real throughput is gated by how you read and write to the state trie.
01
The Problem: The Sequential State Bottleneck
Even with 1000 parallel threads, if all transactions touch the same hot account (e.g., a major DEX pool or NFT mint), they must serialize for state access. This is the Amdahl's Law of blockchains.\n- Hotspot Contention: A single USDC contract can bottleneck an entire block.\n- False Parallelism: Benchmarks often use perfectly partitioned, synthetic workloads.
1x
Effective Speed
~90%
Wasted Compute
02
The Solution: Sharded State & Access Lists
True scalability requires partitioning the state itself. This is the core innovation behind Solana, Sui, and Aptos.\n- Owned Objects (Sui): Transactions modifying independent objects execute in parallel.\n- Explicit Access Lists (Ethereum): Pre-declare read/write sets to enable optimistic concurrency.
10k+
TPS Potential
>95%
Utilization
03
The Trade-off: Complexity vs. Composability
Partitioning state breaks atomic composability across shards. This is the fundamental trade-off architects must design for.\n- Asynchronous Composability: Cross-shard calls add latency (see NEAR, Ethereum L2s).\n- Developer Burden: Apps must be explicitly designed for sharded/object models.
2-10s
Cross-Shard Latency
High
Dev Complexity
04
The Reality: Most 'Parallel' EVMs Are Faking It
L2s like Monad and Sei add parallel execution to the EVM, but are still bound by Ethereum's global state model. Their gains come from: \n- Pipelining: Overlap execution, validation, and mempool ops.\n- Speculative Execution: Guess state access, roll back on conflict (high overhead).
~2-5x
Realistic Gain
High
Node Specs
05
The Benchmark: Look at State Access Patterns
Ignore peak TPS claims. Demand benchmarks on realistic, contentious workloads (e.g., a Uniswap v3 pool during a market crash). Key metrics are: \n- State Conflict Rate: Percentage of txns requiring serialization.\n- Effective Throughput: Sustained TPS under real load, not synthetic.
Critical
Metric
Often Omitted
In Marketing
06
The Future: Dynamic State Sharding & ZK
The endgame is automatic, fine-grained state partitioning verified by zero-knowledge proofs. Ethereum's Danksharding and zkSync's Boojum point in this direction.\n- ZK-Proofs of Execution: Verify parallel batches without re-execution.\n- Data Availability Sampling: Ensures state is available for reconstruction.
Long-term
Horizon
Exponential
Scalability
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