Speeding Up the Blockchain: Parallel Validation for Faster Rewards

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Author: Denis Avetisyan

New research demonstrates that leveraging multi-core processors can dramatically accelerate blockchain validation and construction, boosting validator performance and profitability.

This review explores techniques for scheduling and conflict resolution in parallel blockchain transaction processing, showcasing the potential of heuristics to achieve near-optimal results.

Achieving high throughput in blockchain systems is often constrained by the sequential nature of block validation and construction, despite the prevalence of multi-core processors. This paper, ‘Exploiting Multi-Core Parallelism in Blockchain Validation and Construction’, systematically investigates how validators can leverage parallelism during these critical processes while preserving blockchain semantics. Through Mixed-Integer Linear Programming (MILP) formulations and fast heuristics, the authors demonstrate that optimized scheduling and transaction selection can significantly reduce makespan and maximize validator reward-showing substantial speedups over sequential execution. Can these techniques be further refined to accommodate increasingly complex blockchain architectures and dynamic network conditions?

The Inherent Bottleneck of Sequential Validation

A fundamental constraint on contemporary blockchain networks lies in their largely sequential processing of transactions. Each block, the container for transaction data, is typically built by validators who confirm and order transactions one after another. This linear approach, while ensuring consensus and security, creates a bottleneck as the number of transactions increases. The system’s capacity is fundamentally limited by the time it takes to create and confirm each block, regardless of the computational power available. Consequently, as demand for blockchain applications grows, this sequential nature restricts the network’s overall throughput – the number of transactions it can reliably process per unit of time – and hinders its ability to scale to meet broader adoption. This inherent limitation necessitates exploration of methods to increase transaction processing speed, such as parallelization and sharding, to overcome this scalability challenge.

The functionality of many blockchains relies on a ‘mempool’ – a waiting area for transactions before they are confirmed and added to a block. As demand for a blockchain network increases, this mempool can become congested with pending transaction requests. This rapid accumulation creates a bottleneck, leading to significant delays as transactions wait their turn for processing. Consequently, users often resort to increasing ‘gas usage’ – a fee paid to prioritize their transaction – in an attempt to expedite confirmation times. However, this creates a competitive environment where higher fees don’t necessarily guarantee faster processing, and can ultimately make the network prohibitively expensive to use, particularly during periods of high activity. The resulting congestion and escalating fees represent a core challenge to blockchain scalability and wider adoption.

The fundamental limitation of current blockchain architectures-sequential transaction processing-directly impacts the usability of decentralized applications. As transaction volume increases, the network’s capacity to confirm these requests diminishes, creating a bottleneck that slows down every interaction. This isn’t merely a matter of inconvenience; delayed confirmations hinder real-world applications like decentralized finance (DeFi), supply chain management, and even gaming, where responsiveness is paramount. Consequently, the inability to scale effectively restricts the broader adoption of blockchain technology, preventing it from reaching its full potential as a truly disruptive force across various industries and limiting the speed at which complex smart contracts can operate and deliver value.

Overcoming the throughput limitations of blockchain necessitates a departure from strictly sequential transaction validation. Current research explores sophisticated transaction selection algorithms that prioritize those with lower fees or greater network impact, effectively ordering the mempool for optimized block construction. Furthermore, innovations in parallel processing – including sharding, where the blockchain is divided into smaller, concurrently processed segments – and advancements in Layer-2 scaling solutions aim to alleviate congestion and boost transaction speeds. These approaches don’t simply increase the number of transactions a block can hold, but fundamentally change how transactions are processed, potentially unlocking significantly higher levels of scalability and responsiveness for decentralized applications and fostering wider adoption of blockchain technology.

Mapping Dependencies: The Language of Conflict

Parallel transaction processing is hindered by inherent dependencies arising from shared access to blockchain state. Transactions attempting to read or write the same data create conflicts, necessitating serialization to maintain data consistency. These dependencies are not limited to direct overlaps; transitive relationships also exist – if Transaction A modifies data read by Transaction B, and Transaction B modifies data read by Transaction C, then A and C are also dependent, even without direct interaction. Identifying all such dependencies is computationally expensive but essential for determining a valid execution order and preventing data corruption when multiple transactions are processed concurrently.

A Conflict Graph is a directed graph used to model dependencies between transactions in a blockchain system. Each node in the graph represents a unique transaction attempting to modify the blockchain state. A directed edge connects two transactions if they access and potentially conflict over the same data within that state. Specifically, an edge from transaction A to transaction B indicates that transaction A reads or writes data also accessed by transaction B, creating a dependency that must be resolved before either transaction can be safely included in a block. The presence of edges therefore defines a partial order on transaction execution, ensuring data consistency and preventing conflicting updates to the blockchain.

Accurate identification of transaction conflicts is paramount to both the safety and performance of block creation. Incorrectly scheduling conflicting transactions – those attempting to modify the same state variables concurrently – can lead to data inconsistencies and invalid blockchain states, requiring block rollbacks or forks. Efficient conflict resolution minimizes serialization delays, maximizing throughput by allowing genuinely independent transactions to be processed in parallel. The scheduling process must therefore prioritize transactions with no identified conflicts, and resolve or order those with dependencies before inclusion in a block to guarantee deterministic state transitions and maintain blockchain integrity.

The utilization of a conflict graph as a formal framework for transaction dependency reasoning enables a precise, mathematically-grounded analysis of potential conflicts. By representing transactions as nodes and shared state accesses as edges, the graph explicitly defines the relationships that necessitate serialization. This representation facilitates the application of graph theory algorithms to determine transaction ordering, identify independent transactions suitable for parallel execution, and detect circular dependencies that would indicate contention. The formal nature of the graph allows for automated conflict resolution strategies and provable guarantees regarding transaction safety and consistency, moving beyond heuristic-based scheduling approaches.

Navigating Complexity: Heuristics for Transaction Scheduling

Heuristic algorithms represent a pragmatic approach to transaction scheduling, addressing the inherent computational complexity of identifying optimal transaction sequences. The problem of transaction selection is typically formulated as an optimization challenge – maximizing throughput, minimizing latency, or achieving a balance between these objectives – but exhaustive search for the absolute best solution is often infeasible due to the combinatorial explosion of possibilities as the number of pending transactions increases. Consequently, heuristic methods prioritize speed and scalability by employing simplified rules or guidelines to identify ‘good enough’ solutions within a reasonable timeframe. These algorithms do not guarantee optimality but provide a viable pathway to efficient transaction processing in dynamic and high-volume environments where real-time responsiveness is critical. The trade-off between solution quality and computational cost is a defining characteristic of heuristic-based transaction scheduling.

The Reward-Greedy Baseline transaction scheduling algorithm operates by prioritizing transactions strictly based on their associated reward value, without considering potential conflicts with other transactions. This approach selects the transaction offering the highest immediate reward at each step, irrespective of whether executing that transaction would preclude the execution of other profitable transactions due to resource contention or dependency issues. Consequently, while simple to implement, the Reward-Greedy Baseline does not optimize for overall system throughput or maximize the cumulative reward achievable, as it fails to account for the negative impact of transaction conflicts on the execution of subsequent transactions.

The Conflict-Aware Greedy Heuristic operates by iteratively selecting transactions based on a combined metric of reward and conflict potential. Utilizing the Conflict Graph, the algorithm assesses the number of conflicts each transaction would introduce if executed, effectively penalizing transactions that block a significant number of others. The selection process prioritizes transactions with high reward values and low conflict counts, aiming to maximize overall system throughput. This contrasts with the Reward-Greedy Baseline, which solely considers reward and is thus susceptible to selecting transactions that create substantial blocking, leading to performance degradation. The algorithm dynamically updates the Conflict Graph after each transaction execution to reflect the current state of resource contention.

Sealevel is the execution model employed by the Solana blockchain and operates on a declared-access principle. This means that transactions do not explicitly specify the accounts they will write to; instead, they declare all potential read and write accesses upfront. The system then uses this declaration to parallelize transaction execution, identifying and resolving conflicts before execution begins. This pre-execution conflict detection, combined with a prioritized scheduling approach, allows Solana to achieve high throughput. Transactions failing to meet resource requirements or experiencing conflicts are terminated before consuming resources, contributing to deterministic execution and preventing indefinite blocking. Unlike traditional sequential execution models, Sealevel enables concurrent processing of transactions that access different, non-conflicting accounts.

Measuring Progress: Optimization and the Pursuit of Efficiency

A precise mathematical formulation of the transaction scheduling problem was established using Mixed Integer Linear Programming (MILP). This approach defines the problem’s constraints and objectives – minimizing makespan and maximizing reward – with linear equations and integer variables, allowing for a theoretically optimal solution to be determined. While computationally intensive for larger problem instances, the MILP formulation serves as a crucial benchmark against which the performance of newly developed heuristics can be rigorously evaluated, providing a definitive measure of solution quality and approximation ratios. By defining a ground truth through MILP, researchers gain confidence in the effectiveness and scalability of their heuristic algorithms, ensuring practical improvements over existing methods.

Directly solving the Mixed Integer Linear Program (MILP) formulation of the transaction scheduling problem presents a significant computational challenge, particularly as the number of transactions and resources increases. To address this, researchers often turn to LP Relaxation, a technique that involves relaxing the integer constraints of the MILP – allowing variables to take on fractional values. This relaxation transforms the problem into a Linear Program (LP), which can be solved much more efficiently. Critically, the solution to the LP provides an upper bound on the optimal solution to the original MILP; while not necessarily the exact optimal integer solution, it establishes a benchmark against which the performance of approximate algorithms, such as heuristics, can be evaluated. By comparing heuristic results to this LP-derived bound, one can gauge how close the heuristic’s solution is to the provable optimum, offering valuable insight into its approximation quality.

The efficacy of the developed conflict-aware heuristics is rigorously assessed through comparison with the solutions obtained via LP relaxation, a technique that establishes upper bounds on the optimal makespan. This approach allows for a quantifiable measure of approximation quality; the heuristics consistently achieve solutions ranging from 74% to 100% of the LP-relaxation upper bound. This near-optimal performance indicates the heuristics’ ability to effectively navigate the complex transaction scheduling problem and identify high-quality solutions, even in scenarios where finding the absolute optimum is computationally prohibitive. The close proximity to the LP-relaxation results validates the heuristics’ design and confirms their potential for practical implementation in real-time systems.

The developed heuristics demonstrate a compelling ability to approach optimal solutions for transaction scheduling. Performance evaluations reveal a substantial reduction in makespan – achieving improvements of 1.57x with two processor cores and exceeding 2.2x with eight cores when contrasted with sequential execution. This scalability is further evidenced by the near-linear speedup observed as core count increases. Beyond efficiency, the heuristics also significantly enhance reward, delivering gains exceeding 2x compared to sequential methods. Critically, this high level of performance is achieved with a runtime consistently under one second across all tested configurations, representing a considerable advantage over the computational demands of Mixed Integer Linear Programming (MILP) solvers.

The pursuit of optimized blockchain validation, as detailed in this study, inherently acknowledges the relentless march of time and the decay of initial efficiency. Just as systems age, so too do blockchain networks face increasing burdens with growing transaction volumes. Grace Hopper famously stated, “It’s easier to ask forgiveness than it is to get permission.” This resonates with the paper’s exploration of heuristics; a pragmatic approach prioritizing speed and near-optimal performance over absolute, mathematically perfect solutions. The acceptance of a slight deviation from perfection-asking forgiveness for minor inaccuracies-allows for significant gains in makespan and reward, demonstrating that even in rigorous systems, adaptability and practical compromise are essential for graceful aging and sustained viability. The conflict resolution strategies employed are, in essence, attempts to manage the inevitable entropy of a dynamic, parallel system.

What Lies Ahead?

The pursuit of parallelism in blockchain validation, as demonstrated by this work, is not a quest for perpetual acceleration. Rather, it’s an acknowledgement of entropy. Every block processed brings the system closer to its eventual heat death, and clever scheduling merely delays-not defeats-that inevitability. Technical debt, in this context, resembles erosion; gains achieved through optimization are continuously countered by the increasing complexity of the system itself. The near-optimal performance of heuristics is less a triumph of design and more an illustration of the limitations inherent in achieving true optimality within a fundamentally chaotic environment.

Future investigation should move beyond merely reducing makespan. The focus must shift towards understanding the systemic consequences of aggressive parallelization. How does increased throughput impact network congestion, and what are the emergent properties of a blockchain operating at sustained peak capacity? The question is not simply ‘how fast?’ but ‘how gracefully does it degrade?’.

Ultimately, the rare phases of temporal harmony-the moments of high uptime and efficient validation-are fleeting. Research must confront the inevitable: the system will not scale indefinitely. The true challenge lies in building blockchains that age gracefully, accepting the inherent limitations of distributed consensus, and designing for resilience rather than relentless optimization.

Original article: https://arxiv.org/pdf/2602.03444.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-02-04 19:25