Speculative Execution in Blockchains: Fast Until Proven Wrong

Posted on by uplatzblog

1. Executive Summary

The architectural trajectory of distributed ledger technology is currently undergoing a fundamental paradigm shift. For the first decade of blockchain history, the industry was dominated by the sequential execution model, best exemplified by the Ethereum Virtual Machine (EVM). This model, while prioritizing safety and deterministic state transitions through a strict serial ordering of transactions, effectively capped the throughput of decentralized networks to the single-threaded performance of a validator’s CPU. As the demand for blockspace has grown exponentially—driven by high-frequency trading, complex decentralized finance (DeFi) primitives, and mass-scale non-fungible token (NFT) mints—the sequential bottleneck has become an existential threat to the scalability of Layer 1 blockchains.

In response, a new generation of high-performance blockchains has emerged, abandoning the safety of serial execution for the aggressive efficiency of parallelism. At the forefront of this revolution is Speculative Execution, often implemented via Optimistic Concurrency Control (OCC). This report provides an exhaustive, expert-level analysis of this architectural evolution, encapsulated by the operational philosophy: “Fast Until Proven Wrong.”

The core premise of speculative execution is simple yet radical: rather than waiting to verify that transactions are conflict-free before execution (a “pessimistic” approach), the system assumes that conflicts are rare. It executes transactions in parallel across multiple CPU cores immediately, speculating that they will not interfere with one another. Only after execution does the system retrospectively validate the results. If the speculation was correct, the network achieves massive throughput gains, scaling linearly with hardware capabilities. If the speculation was incorrect—if a dependency violation is detected—the system must identify the divergence, roll back the conflicting state changes, and re-execute the transactions with the correct data.

This report dissects the technical mechanisms of the leading speculative engines, specifically Block-STM (used by Aptos and Sei) and Monad’s Deferred Execution pipeline. We contrast these optimistic models with the Static/Deterministic parallelism of Solana (Sealevel) and the Object-Centric model of Sui, which leverages causal ordering to bypass consensus for independent assets.

Furthermore, we critically examine the downstream implications of these architectures. We explore the complex economic landscape of Maximal Extractable Value (MEV) in a non-deterministic execution environment, where the very concept of transaction ordering becomes fluid. We analyze the severe hardware requirements imposed on validators, moving the industry from consumer-grade hardware to datacenter-class bare metal servers equipped with high-performance NVMe storage and massive RAM pools. Finally, we expose the security underbelly of speculative execution, detailing how the pursuit of raw speed reintroduces vulnerability vectors long thought mitigated, including side-channel attacks (Spectre/Meltdown variants) and sophisticated Denial of Service (DoS) strategies that weaponize the rollback mechanism itself.

This document serves as a comprehensive reference for understanding the trade-offs, mechanisms, and future of parallel execution in blockchain infrastructure.

2. The Sequential Bottleneck: Anatomy of a Scalability Crisis

2.1 The Legacy of the Single-Threaded State Machine

To understand the necessity of speculative execution, one must first dissect the limitations of the incumbent model. A blockchain is, formally, a Replicated State Machine (RSM). The fundamental requirement of an RSM is that every node in the distributed network, given the same initial state and the same sequence of inputs (transactions), must transition to the exact same final state.

In the original design of the Ethereum Virtual Machine (EVM), and arguably in Bitcoin before it, this determinism was achieved through rigid sequentiality. Transactions are ordered into a block by a proposer, and then every validator node processes them one by one, in that specific order.1 This “single-lane” processing ensures that there is zero ambiguity regarding the state of the ledger at any given instruction. If Transaction A sends 5 ETH to Bob, and Transaction B uses those 5 ETH to buy an NFT, Transaction A must complete before Transaction B begins.

However, this architecture suffers from a critical flaw: it fails to align with the trajectory of modern hardware. Over the last two decades, CPU performance gains have shifted from increasing clock speeds (frequency) to increasing core counts (parallelism). A modern server-grade CPU might feature 64 physical cores (128 threads).2 In a single-threaded blockchain, one core performs the heavy lifting of transaction execution, while the other 63 cores sit idle or perform peripheral tasks like signature verification or P2P networking. This results in a massive “computational waste,” where the throughput of the network is capped not by the aggregate power of the machine, but by the speed of a single thread.

2.2 Amdahl’s Law and the “Head-of-Line” Blocking

The performance limit of this system is governed by Amdahl’s Law, which states that the theoretical speedup of a task is limited by the portion of the task that cannot be parallelized. In traditional blockchains, the execution phase is 100% serial.

This serial dependency creates a phenomenon known as Head-of-Line (HOL) Blocking. If the first transaction in a block is computationally expensive (e.g., a complex zero-knowledge proof verification or a heavy DeFi interaction), every subsequent transaction—even simple transfers that have absolutely no relationship to the first transaction—must wait. The entire network stalls behind the “head” of the line.

Consider a block containing:

  1. Transaction $T_1$: A complex arbitrage trade touching 5 different liquidity pools (Execution time: 50ms).
  2. Transaction $T_2$: Alice sends 1 USDC to Bob (Execution time: 0.1ms).
  3. Transaction $T_3$: Charlie sends 1 USDC to Dave (Execution time: 0.1ms).

In a sequential system, $T_2$ and $T_3$ are delayed by 50ms, despite being totally independent of $T_1$. The cumulative latency destroys the user experience and strictly limits the Transactions Per Second (TPS) metric.

2.3 The Divergence of Parallel Approaches

The industry’s response to this bottleneck has been the development of parallel execution engines. However, parallelism in blockchains is significantly harder than in general computing because of the State Contention problem. If two parallel threads try to modify the same account balance simultaneously, the result is non-deterministic (a race condition), which breaks the consensus of the blockchain.

This challenge has bifurcated the landscape into three distinct schools of thought regarding dependency management:

  1. The Pessimistic (Static) Approach: This model, championed by Solana and its Sealevel runtime, requires developers or users to declare upfront exactly which state accounts a transaction will read or write. This “read-write aware” model allows the validator to construct a dependency graph before execution, scheduling non-overlapping transactions on separate cores. While efficient, it places a heavy burden on the developer to predict state access perfectly, and fails in cases where the state access is dynamic (e.g., a transaction that decides which pool to swap with based on on-chain price data at runtime).1
  2. The Object-Centric Approach: Championed by Sui, this model restructures the ledger storage from a monolithic state trie into distinct “objects.” It utilizes Causal Ordering rather than total ordering for independent objects, allowing transactions that touch disjoint objects to be processed asynchronously without a global consensus bottleneck.4
  3. The Optimistic (Speculative) Approach: Championed by Aptos (Block-STM), Monad, and Sei, this model requires no upfront declaration from the user. It utilizes Optimistic Concurrency Control (OCC). The system assumes no conflicts exist, runs everything in parallel, and then uses sophisticated algorithms to detect conflicts retrospectively. This “Fast Until Proven Wrong” approach is the focus of our deep dive, as it represents the most direct attempt to scale the EVM/MoveVM without changing the developer experience.6

3. Theoretical Foundations of Speculative Execution

The mechanics of speculative execution in blockchains are not invented from scratch but are adapted from decades of research in database theory and Software Transactional Memory (STM).

3.1 Optimistic Concurrency Control (OCC)

Optimistic Concurrency Control is a transaction management method applied in relational databases. It operates on the premise that in many high-volume systems, the probability of two transactions trying to modify the exact same data record at the exact same microsecond is statistically low. Therefore, the cost of locking resources (Pessimistic Control) is higher than the cost of occasionally rolling back a transaction.1

The lifecycle of a speculative transaction generally follows three phases 1:

  1. The Read/Execute Phase (Speculative): The transaction is dispatched to a worker thread. It executes its logic against a specific version of the state. Crucially, it does not write its results to the global state immediately. Instead, it records its inputs in a Read-Set (a log of memory locations and versions read) and its outputs in a Write-Set (a private buffer of intended changes). This is the “speculation”—the assumption that the state read is valid and will remain valid.
  2. The Validation Phase: This is the critical checkpoint. Once execution is complete, the system must verify that the speculation was correct. It checks the Read-Set against the global state. Has any transaction ordered before this one modified the data since it was read? If the Read-Set is still valid (i.e., the versions match), the transaction is “proven right.”
  3. The Commit/Abort Phase:
  • Success: If validation passes, the Write-Set is applied to the global state, making the changes visible to subsequent transactions.
  • Failure: If validation fails (a conflict is detected), the transaction is “proven wrong.” The system discards the Write-Set (Rollback) and schedules the transaction for re-execution (Re-incarnation).

3.2 The Challenge of Preset Serialization

In a standard distributed database, the system simply needs to find any serializable order—any sequence that makes sense. Blockchains are stricter. They typically require Preset Serialization. The block proposer (leader) determines a specific order of transactions (e.g., based on gas fees or timestamp), and the parallel execution engine must produce a final state that is mathematically identical to executing the transactions sequentially in that exact order.9

This constraint makes the “validation” phase significantly harder. It is not enough to say “Transaction A and Transaction B didn’t conflict.” The system must ensure that if Transaction A is effectively ordered before Transaction B, then Transaction B must see the changes made by Transaction A. If Transaction B executed on Core 2 while Transaction A was still running on Core 1, Transaction B likely read the “old” state. The validation logic must detect this “Read-After-Write” dependency violation and force Transaction B to re-execute using Transaction A’s output.6

3.3 The “Fast Path” Concept

The efficiency of this model relies on the ratio of conflicting to non-conflicting transactions. In a block of 10,000 transactions, if 9,000 are independent peer-to-peer transfers, they can all execute in parallel on 64 cores, achieving massive speedups. This is the “Fast Path.” The remaining 1,000 transactions (e.g., a crowded NFT mint) might conflict and require sequential re-execution. The goal of speculative engines is to maximize the width of the Fast Path while minimizing the penalty of the Slow Path (rollbacks).7

4. Architectural Deep Dive: Aptos and Block-STM

Aptos utilizes Block-STM, a parallel execution engine originally developed for the Diem (Libra) blockchain. Block-STM is widely considered the state-of-the-art in production-grade optimistic execution, specifically designed to turn the “curse” of sequential ordering into a performance asset.

4.1 The Core Mechanism: Collaborative Scheduling

Block-STM does not view transactions as isolated events but as a unified block-level operation. It employs a Collaborative Scheduler to coordinate the work of multiple threads. Unlike a simple “fork-join” model where a main thread dispatches tasks, threads in Block-STM autonomously pick tasks from a shared priority queue.10

This queue is ordered by the transaction index in the block. Threads prioritize processing lower-index transactions first because these transactions are the “truth” upon which later transactions depend. If Transaction 5 fails, it might invalidate Transactions 6 through 100. Therefore, finishing Transaction 5 is the highest priority for system stability.

4.2 Dynamic Dependency Estimation

A pivotal innovation in Block-STM is Dynamic Dependency Estimation. In naive optimistic systems, if Transaction B conflicts with Transaction A, it aborts and restarts immediately. If Transaction A is still running, Transaction B might read the old data again, fail again, and enter a thrashing loop.

Block-STM solves this by using “ESTIMATE” markers. When a transaction aborts because it read a stale value, it marks the memory location in the multi-version data structure with a tag. This tag effectively says: “Wait! A lower-index transaction is likely to write here.”

Subsequent transactions that try to read this location see the ESTIMATE tag and proactively pause, waiting for the dependency to resolve rather than executing speculatively and failing. This dramatically reduces wasted compute cycles in high-contention workloads.7

4.3 Managing Incarnations and Cascading Rollbacks

Block-STM introduces the concept of Incarnations. When a transaction is first executed, it is Incarnation 0. If it aborts, it becomes Incarnation 1, and so on.

The system must manage Cascading Rollbacks. Consider a dependency chain:

  • $TX_1$ writes to Address X.
  • $TX_2$ reads Address X and writes to Address Y.
  • $TX_3$ reads Address Y.

If $TX_1$ is re-executed (perhaps it read a value from $TX_0$) and changes its write to Address X, then $TX_2$ is now invalid (it read the wrong X). The system aborts $TX_2$. But because $TX_2$ is re-executed, its write to Address Y might change, which means $TX_3$ is also invalid and must be aborted.

The scheduler tracks these dependencies. When $TX_1$ re-executes, the scheduler identifies all transactions that read the values produced by the previous incarnation of $TX_1$ and schedules them for validation/re-execution. This ensures safety but highlights the potential cost of deep dependency chains.6

4.4 Performance Benchmarks and Contention Analysis

Aptos Labs has published extensive benchmarks characterizing Block-STM’s performance profile 7:

  • Low Contention: In a workload consisting of transfers between unique accounts (the ideal scenario), Block-STM achieves 160,000 to 170,000 TPS on a 32-core machine. This represents a roughly 16x-17x speedup over sequential execution, demonstrating near-linear scaling.
  • High Contention: In a workload where many transactions access the same account (e.g., a “hot” smart contract), parallelism is naturally limited. However, thanks to the ESTIMATE markers and efficient scheduling, performance does not collapse to zero. Benchmarks show 50,000 to 80,000 TPS under moderate contention.
  • Sequential Worst-Case: In a purely sequential workload (where $TX_{n}$ depends on $TX_{n-1}$ for all $n$), Block-STM cannot parallelize. In this scenario, it incurs a roughly 30% overhead compared to a simple serial executor due to the costs of managing the STM metadata (read-sets, write-sets, atomic counters). This is the “tax” paid for the potential of parallelism.

5. Architectural Deep Dive: Monad and Deferred Execution

Monad introduces a variation on the speculative theme, focusing on Deferred Execution and re-engineering the EVM execution environment from the ground up.

5.1 The “Agree First, Execute Later” Philosophy

Monad identifies a critical inefficiency in standard blockchains like Ethereum: the coupling of consensus and execution. In Ethereum, a validator must execute all transactions in a block before it can vote on the block’s validity (because the state root is part of the block header). This means the time available for execution is strictly limited to the block interval minus the network propagation latency.13

Monad decouples these processes. It employs a Pipelined Architecture:

  1. Consensus Phase (MonadBFT): Validators agree on the ordering of transactions. They check basic validity (signatures, nonces) but do not execute the full smart contract logic.
  2. Execution Phase (Deferred): Once the order is finalized, the execution engine processes the transactions. Because the order is fixed and deterministic, the resulting state is guaranteed to be consistent across all nodes.

This separation allows the execution budget to fill the entire block time, rather than just a fraction of it. It effectively doubles the “window” available for computation.13

5.2 Optimistic Parallel Execution with Sequential Commit

Like Aptos, Monad uses optimistic execution. Nodes begin executing transactions in the finalized order immediately, utilizing all available cores.

  • Speculation: Cores execute transactions assuming no conflicts.
  • Conflict Detection: Monad utilizes a “Sequential Commit” phase. Although execution is parallel, the results (state updates) are merged into the global state in the strict linear order defined by consensus. During this merge, the system checks: “Did this transaction use input data that has been modified by a preceding transaction in the merge queue?”
  • Re-execution Optimization: If a conflict is found, the transaction is re-executed. Monad optimizes this by caching the data. Since the transaction just attempted to run, the relevant state data is likely already in the CPU’s L1/L2 cache or RAM. This “warm cache” re-execution is significantly faster than the initial cold execution, mitigating the penalty of the rollback.16

5.3 MonadDB: Solving the I/O Bottleneck

A critical insight from the Monad team is that parallel execution is useless if the database cannot keep up. If 32 threads try to read state from the disk simultaneously, a standard blockchain database (like RocksDB or LevelDB) becomes a bottleneck due to locking contention and synchronous I/O.

Monad replaces these with MonadDB, a custom database built for Asynchronous I/O (utilizing the io_uring kernel interface in Linux). MonadDB allows the execution engine to issue thousands of concurrent state reads without blocking the CPU threads. The database handles the disk operations asynchronously and returns the data to the threads when ready. This prevents the CPU cores from stalling while waiting for SSD responses, which is a common failure mode in high-throughput systems.17

5.4 The “Plagiarism” Controversy

It is worth noting the friction within the industry regarding these architectures. Aptos researchers have publicly accused Monad of replicating the core tenets of Block-STM and the pipelined consensus structure (Diem/Jolteon) without sufficient attribution. Monad’s leadership has rebutted this, claiming their implementation of Optimistic Concurrency Control is a standard computer science application (dating back to 1979) and that their specific C++ implementation and MonadDB are novel engineering feats distinct from the Move-based Aptos codebase.19 This dispute highlights the convergence of high-performance blockchain designs toward a common set of optimal solutions: OCC, pipelining, and custom databases.

6. Alternative Paradigms: Sui and Solana

To fully appreciate the speculative model, it must be contrasted with its primary alternatives.

6.1 Sui: The Object-Centric “Fast Path”

Sui takes a fundamentally different approach to state. Instead of a global key-value store (like the EVM or Aptos), Sui’s storage is strictly Object-Centric. Every asset (token, NFT, smart contract) is a distinct object with a unique ID and owner.

Sui leverages this to bypass consensus entirely for a large class of transactions. It distinguishes between:

  • Owned Objects: Assets owned by a single address (e.g., a user sending a P2P payment). These transactions are causally independent of the rest of the network. Sui uses Byzantine Consistent Broadcast (a simpler, non-consensus protocol) to process these. The validator locks the object, executes the transaction, and issues a certificate. This is the “Fast Path,” offering near-instant finality without the overhead of Block-STM’s speculation.4
  • Shared Objects: Assets accessed by multiple users (e.g., a liquidity pool). These do require ordering and must go through the consensus protocol (Mysticeti).

Comparison: Sui effectively “static analyzes” the transaction type. If it’s simple, it skips the line. If it’s complex, it waits. Aptos/Monad, by contrast, treat all transactions uniformly and rely on the engine to sort them out at runtime.

6.2 Solana: The Static Scheduler (Sealevel)

Solana’s Sealevel runtime is the primary example of Pessimistic/Static parallelism. To execute a transaction on Solana, the user must specify upfront every single account the transaction will read or write.

The scheduler reads these headers before execution begins. It can then mathematically verify: “Transaction A touches Account 1. Transaction B touches Account 2. They do not overlap.” It assigns them to different threads.

Pros: Zero rollbacks. No wasted compute. Deterministic performance.

Cons: High developer/user burden. It makes “dynamic” transactions difficult—for example, a DEX aggregator that wants to check 10 pools and swap on the best one cannot easily declare which pool it will write to before it reads the prices. This limitation often forces developers to create complex workarounds or serialize operations that could theoretically be parallel.1

7. Performance Realities: Benchmarks and Bottlenecks

7.1 Throughput vs. Latency Trade-offs

The speculative model prioritizes Throughput (TPS) over Latency. By batching transactions into blocks and optimizing for aggregate execution time, systems like Aptos and Monad achieve massive throughput numbers (100k+ TPS). However, the latency for an individual transaction includes the time spent in the “speculation buffer” and potentially the time spent re-executing if a conflict occurred.25

In contrast, Sui’s Fast Path prioritizes Latency. An owned-object transaction can be finalized in sub-second timeframes because it doesn’t wait for block consensus. However, the aggregate throughput of the shared-object path in Sui may be lower than Block-STM under certain conditions due to the overhead of the consensus DAG.23

7.2 The Reality of Conflict Rates

The effectiveness of “Fast Until Proven Wrong” depends entirely on how often the system is proven wrong.

  • Ethereum Traffic Analysis: Research indicates that historic Ethereum blocks have a relatively high independence rate (~50% of transactions are non-conflicting). This suggests that Block-STM would perform very well on “classic” Ethereum traffic.27
  • The Bot Factor: However, on low-fee chains (like Solana), traffic is dominated by arbitrage bots. These bots all target the same few “hot” states (e.g., the Orca or Raydium liquidity pools). Analysis of Solana blocks shows high conflict chains, where ~59% of transactions in a block might be dependent on each other.27
    Implication: If a speculative chain like Monad becomes popular for DeFi, the conflict rate will spike. The engine will spend a significant percentage of its cycles rolling back and re-executing bot transactions. While Block-STM handles this gracefully via “Estimate” markers, it essentially degrades to a sequential executor for those specific “hot” contracts, nullifying the parallel advantage for that slice of the network.7

8. The Economic Layer: MEV in a Parallel World

8.1 Non-Determinism and Searcher Strategy

Maximal Extractable Value (MEV) is the profit miners/validators make by reordering transactions. In a sequential chain, ordering is absolute. If a searcher places a “buy” before a victim and a “sell” after, the profit is guaranteed.

In a parallel/speculative chain, execution is non-deterministic at the thread level. Even if the block order is fixed, the timing of execution on the cores varies.

  • JIT Liquidity: A searcher trying to front-run a trade might find that their transaction was scheduled on a core that executed slower than the victim’s core, causing the attack to fail or backfire.
  • Probabilistic MEV: Searchers on parallel chains may need to adopt probabilistic strategies, flooding the network with multiple variations of an attack to ensure one “lands” in the correct execution window. This exacerbates the spam problem.28

8.2 The Spam “Death Spiral”

The low fees and high throughput of parallel chains encourage spam. In Solana, this has been a recurring issue. In a speculative engine, spam is even more dangerous.

If an attacker floods the network with transactions that intentionally create complex dependency chains (e.g., $TX_A$ writes to $X$, $TX_B$ reads $X$ and writes $Y$, $TX_C$ reads $Y$…), they can force the validator to perform speculative work that is constantly rolled back.

Flashbots Research indicates that regardless of bandwidth or parallel capacity, chains hit a “MEV Spam Wall” where the cost of processing these failed arbitrage attempts consumes the majority of block gas, crowding out real users. Monad and Aptos attempt to mitigate this with “Gas per instruction” metering that charges for the attempted work, but the economic equilibrium remains fragile.29

9. Security Critical Analysis: Side-Channels and DoS

The “Fast Until Proven Wrong” philosophy introduces new attack vectors.

9.1 Side-Channel Attacks (The Spectre of Blockchain)

Speculative execution in hardware (CPUs) led to the Spectre and Meltdown vulnerabilities. These attacks exploit the fact that even if a speculative instruction is rolled back (because a branch was mispredicted), it leaves traces in the CPU cache.

A similar risk exists in speculative blockchain validators.

  • The Attack Vector: A malicious user submits a transaction that speculatively reads a sensitive piece of data (e.g., a private key fragment or a blinded bid in a privacy auction). The transaction uses this data to trigger a memory access pattern (e.g., “If key bit is 1, read Array A; if 0, read Array B”).
  • The Leak: The transaction is designed to conflict and abort. The blockchain state rolls back. However, the validator’s CPU cache now holds Array A or Array B. A subsequent transaction (probing transaction) can measure access times to determine which array is cached, leaking the secret.30
  • Mitigation vs. Performance: To prevent this, security advisories (Microsoft, VMware) recommend disabling Hyperthreading (SMT) on CPUs running untrusted code.32 However, disabling SMT reduces the validator’s parallel processing capacity by ~30-40%. Validators face a hard choice: maximum throughput (risk side-channels) or maximum security (reduced performance).

9.2 Denial of Service via “Block Stuffing” and Grinding

An attacker can exploit the difference between “Validation Cost” and “Execution Cost.”

  • Grinding Attack: An attacker submits a transaction that performs heavy computation but is designed to fail validation at the very last step due to a conflict. The validator spends resources executing it speculatively. If the attacker scales this, they can fill the validator’s thread pool with “zombie” transactions that never commit but consume electricity and delay legitimate traffic.
  • Block Stuffing: By filling a block with transactions that have high dependency density (worst-case scenario), an attacker can force the Block-STM engine into its slowest, most sequential mode. If the gas pricing does not accurately reflect the “cost of sequentiality,” this is a cheap way to degrade network performance.34

10. Infrastructure and Hardware Requirements

The shift to parallel execution moves blockchain validation from “hobbyist” territory to “enterprise” territory.

10.1 Bare Metal Necessity

Monad explicitly advises against using Cloud VMs (AWS/GCP). The virtualization layer introduces latency (“noisy neighbors,” hypervisor overhead) that disrupts the tight timing of pipelined consensus. Bare Metal servers are a requirement to ensure the CPU can feed the execution engine without jitter.36

10.2 Hardware Specification Comparison

 

Feature Aptos Validator Monad Validator Sui Validator
CPU 32 Cores / 64 Threads (AMD EPYC/Intel Xeon) 16+ Cores @ 4.5GHz+ (High Frequency Critical) 24 Physical Cores / 48 vCPUs
RAM 64 GB 32 GB+ 128 GB (Recommended)
Storage 3TB NVMe (60K+ IOPS) 2x 2TB NVMe (PCIe Gen4x4) 4TB NVMe
Network 1 Gbps 1 Gbps (100Mbps min) 1 Gbps / 10 Gbps rec
Key Constraint IOPS: Must support massive concurrent R/W for STM. Single Core Speed: Essential for the sequential merge phase. RAM: Essential for the large object index in memory.

Implication: The entry barrier for validation is rising. The requirement for NVMe drives with high IOPS (60k+) is particularly strict, as the speculative engine essentially hammers the disk with read requests from 32+ threads simultaneously. This favors centralized datacenter operators over home validators.

11. Conclusion

Speculative Execution represents the industrialization of the blockchain. It is an admission that the “single world computer” cannot scale on a single core. By adopting the philosophy of “Fast Until Proven Wrong,” protocols like Aptos, Monad, and Sei unlock the potential of modern hardware, offering throughputs that rival centralized databases.

However, this speed is not free. It is purchased with Complexity. The simplicity of the sequential loop is replaced by the probabilistic chaos of dynamic dependency estimation, cascading rollbacks, and asynchronous I/O. It is purchased with Risk, re-opening the door to side-channel attacks and complex DoS vectors that exploit the speculative mechanism itself. And it is purchased with Centralization, as the hardware required to participate in consensus becomes increasingly specialized and expensive.

As the industry matures, it is likely that “Hybrid” models will dominate—using Deterministic/Object-Centric paths (like Sui) for known independent assets, and Speculative paths (like Block-STM) for complex, dynamic interactions. The future of blockchain is parallel, but it will be a “managed” parallelism, where the optimism of the engine is tempered by the hard realities of adversarial environments.

Works cited

  1. A Systematic Analysis of Parallel Execution in Deterministic Systems: From Optimistic Concurrency to Block-STM and Optimal Scheduling | Uplatz Blog, accessed on December 21, 2025, https://uplatz.com/blog/a-systematic-analysis-of-parallel-execution-in-deterministic-systems-from-optimistic-concurrency-to-block-stm-and-optimal-scheduling/
  2. Aptos Validator Server Requirements – Knowledgebase – BaCloud.com, accessed on December 21, 2025, https://www.bacloud.com/en/knowledgebase/280/aptos-node-and-validator-requirements-2025.html
  3. Solana vs Sui (2025): Architecture, Execution Models & Security Compared – Three Sigma, accessed on December 21, 2025, https://threesigma.xyz/blog/ecosystem/sui-vs-solana-guide
  4. Comparison – Sui Documentation, accessed on December 21, 2025, https://docs.sui.io/sui-compared
  5. A simple reason why SUI is attracting attention from investors | TobiNews on Binance Square, accessed on December 21, 2025, https://www.binance.com/en-IN/square/post/33377029461442
  6. Sei Parallelization Engine: Multi-core Blockchain Execution | Sei Docs, accessed on December 21, 2025, https://docs.sei.io/learn/parallelization-engine
  7. Block-STM: Scaling Blockchain Execution by Turning Ordering Curse to a Performance Blessing | alphaXiv, accessed on December 21, 2025, https://www.alphaxiv.org/overview/2203.06871v3
  8. Block-STM: Accelerating Smart-Contract Processing – Chainlink Blog, accessed on December 21, 2025, https://blog.chain.link/block-stm/
  9. Block-STM arXiv:2203.06871v3 [cs.DC] 25 Aug 2022 – Aptos Labs, accessed on December 21, 2025, https://aptoslabs.com/pdf/2203.06871.pdf
  10. MIT Open Access Articles Block-STM: Scaling Blockchain Execution by Turning Ordering Curse to a Performance Blessing, accessed on December 21, 2025, https://dspace.mit.edu/bitstream/handle/1721.1/148274/3572848.3577524.pdf?sequence=1&isAllowed=y
  11. Forerunner: Constraint-based Speculative Transaction Execution for Ethereum – Microsoft, accessed on December 21, 2025, https://www.microsoft.com/en-us/research/wp-content/uploads/2021/09/3477132.3483564.pdf
  12. Block-STM: How We Execute Over 160k Transactions Per Second on the Aptos Blockchain, accessed on December 21, 2025, https://medium.com/aptoslabs/block-stm-how-we-execute-over-160k-transactions-per-second-on-the-aptos-blockchain-3b003657e4ba
  13. Asynchronous Execution: Monad’s “Agree First, Execute Later” Approach – General, accessed on December 21, 2025, https://forum.monad.xyz/t/asynchronous-execution-monad-s-agree-first-execute-later-approach/192
  14. Monad First Look: Hyperscaling the EVM – Figment, accessed on December 21, 2025, https://www.figment.io/insights/monad-first-look-hyperscaling-the-evm/
  15. How Monad Works, accessed on December 21, 2025, https://www.monad.xyz/announcements/how-monad-works
  16. Parallel Execution | Monad Developer Documentation, accessed on December 21, 2025, https://docs.monad.xyz/monad-arch/execution/parallel-execution
  17. [CryptoTimes] Blockchain & Technology Enabling Parallel Execution – DeSpread Research, accessed on December 21, 2025, https://research.despread.io/crypto-times-parallel-execution/
  18. A Developer’s Guide to Monad: EVM-Compatible L1 Architecture and Implementation, accessed on December 21, 2025, https://blog.quicknode.com/monad-developer-guide/
  19. Monad Accused of Copying Aptos Technology Without Credit – BeInCrypto, accessed on December 21, 2025, https://beincrypto.com/aptos-vs-monad-tech-controversy/
  20. Aptos vs Monad? – News, accessed on December 21, 2025, https://forum.aptosfoundation.org/t/aptos-vs-monad/15432
  21. Aptos accuses Monad of plagiarizing technology! The community is polarized, where is the boundary between open source and rights? | 加密城市 Crypto City on Binance Square, accessed on December 21, 2025, https://www.binance.com/en/square/post/20677284919537
  22. Causal Order – HackQuest, accessed on December 21, 2025, https://www.hackquest.io/glossary/Causal-Order
  23. Sui Blockchain: A Clear Guide To Its Core Concepts – Webisoft Blog, accessed on December 21, 2025, https://webisoft.com/articles/sui-blockchain/
  24. So long, Solana? The Rise of Blockchain’s Parallel Universes | Crypto Blog | Cyber Capital, accessed on December 21, 2025, https://www.cyber.capital/blog/so-long-solana-the-rise-of-blockchains-parallel-universes
  25. Blockchain Scalability in 2025: Are We Finally Solving the Throughput Problem? – LCX, accessed on December 21, 2025, https://www.lcx.com/blockchain-scalability-in-2025-are-we-finally-solving-the-throughput-problem/
  26. Sui Lutris: A Blockchain Combining Broadcast and Consensus – arXiv, accessed on December 21, 2025, https://arxiv.org/pdf/2310.18042
  27. Empirical Analysis of Transaction Conflicts in Ethereum and Solana for Parallel Execution, accessed on December 21, 2025, https://arxiv.org/html/2505.05358v1
  28. MEV Resistance | Flow Developer Portal, accessed on December 21, 2025, https://developers.flow.com/build/cadence/basics/mev-resistance
  29. MEV Spam: The Hidden Blockchain Scalability Crisis | by SYNCRONE I DeFi PMS – Medium, accessed on December 21, 2025, https://medium.com/coinmonks/mev-spam-the-hidden-blockchain-scalability-crisis-b6f89ded4c2a
  30. Side-channel attack – Wikipedia, accessed on December 21, 2025, https://en.wikipedia.org/wiki/Side-channel_attack
  31. Memory Under Siege: A Comprehensive Survey of Side-Channel Attacks on Memory – arXiv, accessed on December 21, 2025, https://arxiv.org/html/2505.04896v1
  32. Constant PC Crashing after error message: The hypervisor did not enable mitigations for side channel vulnerabilities for virtual machines because HyperThreading is enabled. To enable mitigations for virtual machines, disable HyperThreading. – Microsoft Learn, accessed on December 21, 2025, https://learn.microsoft.com/en-us/answers/questions/5661653/constant-pc-crashing-after-error-message-the-hyper
  33. KB4457951: Windows guidance to protect against speculative execution side-channel vulnerabilities – Microsoft Support, accessed on December 21, 2025, https://support.microsoft.com/en-us/topic/kb4457951-windows-guidance-to-protect-against-speculative-execution-side-channel-vulnerabilities-ae9b7bcd-e8e9-7304-2c40-f047a0ab3385
  34. Denial of Service (DoS) Attacks in Smart Contracts – QuillAudits, accessed on December 21, 2025, https://www.quillaudits.com/blog/web3-security/denial-of-service-on-smart-contracts
  35. How Blockchain DDoS Attacks Work – Halborn, accessed on December 21, 2025, https://www.halborn.com/blog/post/how-blockchain-ddos-attacks-work
  36. Monad Launches Validator-Focused Testnet-2, Unveils Requirements And Selection Criteria | Mpost Media Group on Binance Square, accessed on December 21, 2025, https://www.binance.com/en/square/post/24006186094818
  37. Hardware Requirements | Monad Developer Documentation, accessed on December 21, 2025, https://docs.monad.xyz/node-ops/hardware-requirements
  38. Node Requirements – Aptos Documentation, accessed on December 21, 2025, https://aptos.dev/network/nodes/validator-node/node-requirements
  39. Sui Node and validator requirements 2025 – Knowledgebase – BaCloud.com, accessed on December 21, 2025, https://www.bacloud.com/en/knowledgebase/279/sui-node-and-validator-requirements-2025.html
  40. Validator Deployment and Configuration – Sui Documentation, accessed on December 21, 2025, https://docs.sui.io/guides/operator/validator/validator-config