Bitroot Parallel EVM Technology In-Depth Analysis: High-Performance Blockchain Architecture Design and Implementation

Source: Bitroot

Introduction: A Technological Breakthrough to Overcome the Blockchain Performance Bottleneck

Throughout the development of blockchain technology over the past decade, performance bottleneck has always been a core obstacle hindering its large-scale application. With Ethereum only able to process 15 transactions per second and a confirmation time of up to 12 seconds, such performance is clearly insufficient to meet the growing demands of applications. The serial execution model and limited computing power of traditional blockchains severely restrict system throughput. The emergence of Bitroot is precisely to break through this dilemma. Through four technological innovations—Pipeline BFT consensus mechanism, optimistic parallelization EVM, state sharding, and BLS signature aggregation—Bitroot has achieved a breakthrough with 400 milliseconds finality and 25,600 TPS, providing an engineered technical solution for the large-scale application of blockchain technology. This article will elaborate on Bitroot's core technological architecture design principles, algorithm innovations, and engineering practical experience, providing a complete technical blueprint for high-performance blockchain systems.

Chapter 1: Technical Architecture—The Engineering Philosophy of Layered Design

1.1 Five-Layer Architecture

Bitroot adopts the classical layered architectural paradigm, sequentially building five clearly defined and distinct core layers from the bottom up. This design not only achieves good module decoupling but also lays a solid foundation for the system's scalability and maintainability.

The storage layer, as the cornerstone of the entire system, undertakes the task of persisting state data. It uses an enhanced Merkle Patricia Trie structure to manage the state tree, supporting incremental updates and fast state proof generation. Addressing the common problem of state bloat in blockchains, Bitroot introduces a distributed storage system that stores large data shards in the network, while only keeping hash references on-chain. This design effectively alleviates the storage pressure on full nodes, enabling ordinary hardware to participate in network validation.

The network layer builds a robust peer-to-peer communication infrastructure. It utilizes the Kademlia distributed hash table for node discovery and employs the GossipSub protocol for message propagation, ensuring efficient information dissemination in the network. Particularly noteworthy is the optimization of the network layer for large-scale data transmission, with a dedicated mechanism for transferring large data packets that supports packetization and resumption of interrupted transfers, significantly improving data synchronization efficiency.

The consensus layer is the core of Bitroot's performance breakthrough. By integrating the Pipeline BFT consensus mechanism and BLS signature aggregation technology, it achieves pipelined processing of the consensus process. In contrast to traditional blockchains tightly coupling consensus with execution, Bitroot completely decouples the two—where the consensus module focuses on quickly determining transaction order, and the execution module processes transaction logic in the background in parallel. This design allows the consensus to continuously move forward without waiting for transaction execution to complete, greatly enhancing system throughput.

The Protocol Layer is the culmination of Bitroot's technological innovation. It not only achieves full EVM compatibility, ensuring seamless migration of Ethereum ecosystem smart contracts, but more importantly, it implements a parallel execution engine. Through a three-stage conflict detection mechanism, it breaks through the traditional EVM's single-threaded limitation, fully unleashing the computing potential of multi-core processors.

The Application Layer provides developers with a rich toolchain and SDK, lowering the barrier to entry for blockchain application development. Whether it's DeFi protocols, NFT markets, or DAO governance systems, developers can quickly build applications through standardized interfaces without needing an in-depth understanding of the underlying technical details.

graph TB
        subgraph "Bitroot Five-Layer Architecture"
        A[Application Layer<br/>DeFi Protocols, NFT Market, DAO Governance<br/>Toolchain, SDK]
        B[Protocol Layer<br/>EVM Compatibility, Parallel Execution Engine<br/>Three-Stage Conflict Detection]
        C[Consensus Layer<br/>Pipeline BFT<br/>BLS Signature Aggregation]
        D[Network Layer<br/>Kademlia DHT<br/>GossipSub Protocol]
        E[Storage Layer<br/>Merkle Patricia Trie<br/>Distributed Storage]
        end
        A --> B
        B --> C
       C --> D
       D --> E
       style A fill:#e1f5fe
       style B fill:#f3e5f5
       style C fill:#e8f5e8
       style D fill:#fff3e0
       style E fill:#fce4ec

1.2 Design Philosophy: Seeking Optimal Solutions in Trade-offs within Architecture

During the design process, the Bitroot team faced many technical trade-offs, with each decision profoundly impacting the final form of the system.

The balance between performance and decentralization is an eternal topic in blockchain design. Traditional public blockchains often sacrifice performance in pursuit of extreme decentralization, while high-performance consortium chains compromise decentralization. Bitroot has found a clever balance through a dual-pool staking model: the Validator Pool is responsible for consensus and network security, ensuring the decentralization of the core mechanism; the Computation Pool focuses on task execution, allowing operation on nodes with better performance. Dynamic switching between the two pools ensures both the security and decentralization characteristics of the system, while fully leveraging the computational power of high-performance nodes.

The trade-off between compatibility and innovation also tests design intelligence. Being fully compatible with the EVM means being able to seamlessly connect to the Ethereum ecosystem, but it will also be limited by the design constraints of the EVM. Bitroot has chosen a progressive innovation path—maintaining full compatibility with the core EVM instruction set, ensuring zero-cost migration of existing smart contracts; while introducing new capabilities through an extended instruction set, leaving ample room for future technological evolution. This design not only reduces the cost of ecosystem migration but also opens the door to technological innovation.

The coordination of security and efficiency is particularly important in a parallel execution scenario. Although parallel execution can significantly improve performance, it also introduces new security challenges such as state access conflicts and race conditions. Bitroot employs a three-stage conflict detection mechanism, conducting detection and verification before execution, during execution, and after execution, ensuring that even in a highly parallel environment, the system can maintain consistency and security. This multi-layered protection mechanism allows Bitroot to pursue ultimate performance without sacrificing security.

II. Pipeline BFT Consensus: Breaking Free from Serialization

2.1 The Performance Dilemma of Traditional BFT

Since Byzantine Fault Tolerance (BFT) consensus mechanisms were proposed by Lamport et al. in 1982, they have become the theoretical cornerstone of fault tolerance in distributed systems. However, the classic BFT architecture, while pursuing security and consistency, also exposes three fundamental performance limitations.

Serialization is the primary bottleneck. Traditional BFT requires each block to wait for the previous block to be fully confirmed before starting the consensus process. Taking Tendermint as an example, its consensus includes three stages: Propose, Prevote, Precommit, each stage requiring more than two-thirds of validator node votes, and block height strictly advancing in a serial manner. Even if nodes are equipped with high-performance hardware and sufficient network bandwidth, they cannot use these resources to accelerate the consensus process. Ethereum PoS takes 12 seconds to complete a round of confirmation, while Solana shortens block generation time to 400 milliseconds through the PoH mechanism, but final confirmation still takes 2-3 seconds. This serialization design fundamentally limits the space for improving consensus efficiency.

Communication complexity grows quadratically with the number of nodes. In a network with n validator nodes, each round of consensus requires O(n²) message exchanges—each node needs to send messages to all other nodes while receiving messages from all nodes. When the network scales to 100 nodes, a single round of consensus needs to process nearly ten thousand messages. More critically, each node needs to verify O(n) signatures, with verification overhead increasing linearly with the number of nodes. In a large-scale network, nodes spend a significant amount of time on message processing and signature verification rather than actual state transition computation.

The low resource utilization has plagued performance optimization. Modern servers are generally equipped with multi-core CPUs and high-bandwidth networks, but the design concept of traditional BFT originates from the single-core era of the 1980s. When nodes are waiting for network messages, the CPU is largely idle; and during intensive computation for signature verification, the network bandwidth is not fully utilized. This unbalanced resource utilization has led to suboptimal overall performance—even with better hardware investment, the performance improvement is very limited.

2.2 Pipelining: The Art of Parallel Processing

The core innovation of Pipeline BFT is to pipeline the consensus process, allowing blocks at different heights to undergo parallel consensus. This design inspiration comes from the modern processor's instruction pipeline technology—when one instruction is in the execution phase, the next instruction can be simultaneously in the decode phase, and the instruction after that is in the fetch phase.

A four-stage parallel mechanism is the foundation of Pipeline BFT.

The consensus process is decomposed into four independent stages: Propose, Prevote, Precommit, and Commit. The key innovation is that these four stages can overlap in execution: when block N-1 enters the Commit stage, block N simultaneously performs Precommit; when block N enters Precommit, block N+1 simultaneously performs Prevote; when block N+1 enters Prevote, block N+2 can start Propose. This design allows the consensus process to operate continuously like a pipeline, with multiple blocks at different stages being processed in parallel at any given time.

In the Propose stage, the leader node proposes a new block containing a transaction list, block hash, and reference to the previous block. To ensure fairness and prevent single points of failure, the leader is elected through a Verifiable Random Function (VRF) lottery. The randomness of the VRF is based on the hash value of the previous block, ensuring that no one can predict or manipulate the leader election result.

The Prevote stage is where validating nodes preliminarily acknowledge the proposed block. Upon receiving the proposal, nodes validate the block's legitimacy—whether the transaction signatures are valid, state transitions are correct, and the block hash matches. After validation, nodes broadcast prevote messages containing the block hash and their signature. This stage is essentially a straw poll to detect if there are enough nodes in the network that acknowledge this block.

The Precommit stage introduces stronger commitment semantics. When a node collects over two-thirds of prevotes, it believes that the majority of nodes in the network accept this block and, therefore, broadcasts a precommit message. Precommit signifies commitment—once a node sends a precommit, it cannot vote for other blocks at the same height. This one-way commitment mechanism prevents double-voting attacks, ensuring the security of the consensus.

The Commit stage is the final confirmation. When a node collects over two-thirds of pre-commits, it is confident that this block has received network consensus, so it formally commits it to the local state. At this point, the block achieves finality and becomes irreversible. Even in the event of network partitions or node failures, committed blocks will not be reversed.

 graph TB
        title Pipeline BFT Pipeline Parallel Mechanism
        dateFormat X
        axisFormat %s
        section Block N-1    Propose    :done, prop1, 0, 1
        Prevote    :done, prev1, 1, 2
        Precommit  :done, prec1, 2, 3
        Commit     :done, comm1, 3, 4 
        section Block N
        Propose    :done, prop2, 1, 2
        Prevote    :done, prev2, 2, 3
        Precommit  :done, prec2, 3, 4
        Commit     :active, comm2, 4, 5
        section Block N+1
        Propose    :done, prop3, 2, 3
        Prevote    :done, prev3, 3, 4
        Precommit  :active, prec3, 4, 5
        Commit     :comm3, 5, 6
        section Block N+2
        Propose    :done, prop4, 3, 4
        Prevote    :active, prev4, 4, 5
        Precommit  :prec4, 5, 6
        Commit     :comm4, 6, 7

The state machine replication protocol ensures the consistency of a distributed system. Each validator node independently maintains a consensus state, including the current height, round, and stage. Nodes achieve state synchronization through message exchange—when they receive messages from a higher height, nodes understand they are behind and need to expedite processing; when they receive messages from different rounds at the same height, nodes decide whether to enter a new round.

The state transition rules are carefully designed to ensure the security and liveness of the system: when a node receives a valid proposal at height H, it transitions to the Prevote step; upon collecting enough Prevotes, it transitions to the Precommit step; after collecting enough Precommits, it submits the block and transitions to height H+1. If the step transition is not completed within the timeout period, the node increments the round and starts over. This timeout mechanism prevents the system from permanently stalling in exceptional circumstances.

The intelligent message scheduling ensures the correctness of message processing. Pipeline BFT has implemented a Height-based Priority Message Queue (HMPT), which calculates the priority of messages based on the message's block height, round, and step. Messages with higher heights have higher priority, ensuring that the consensus can continue to progress; within the same height, rounds and steps also affect the priority, preventing outdated messages from interfering with the current consensus.

The message processing strategy is also carefully designed: messages from the future (height greater than the current height) are cached in the pending queue, waiting for nodes to catch up with the progress; messages at the current height are processed immediately, driving the consensus forward; severely outdated messages (height much lower than the current height) are directly discarded to avoid memory leaks and invalid computations.

2.3 BLS Signature Aggregation: Cryptographic Dimensionality Reduction

In traditional ECDSA signature schemes, verifying n signatures requires O(n) time complexity and storage space. In a network with 100 validating nodes, each consensus round needs to verify 100 signatures, occupying approximately 6.4 KB of signature data. As the network scales, signature verification and transmission become significant performance bottlenecks.

BLS signature aggregation technology has brought a cryptographic breakthrough. Based on the BLS12-381 elliptic curve, Bitroot has achieved true O(1) signature verification—regardless of the number of validating nodes, the aggregated signature size remains constant at 96 bytes, requiring only one pairing operation for verification.

The BLS12-381 curve provides a 128-bit security level, meeting long-term security requirements. It defines two groups, G1 and G2, and a target group GT. G1 is used to store public keys, with elements occupying 48 bytes; G2 is used to store signatures, with elements occupying 96 bytes. This asymmetric design optimizes verification performance— the computational cost of G1 elements in pairing operations is lower, and placing public keys in G1 precisely utilizes this characteristic.

The mathematical principles of signature aggregation are based on the bilinearity property of the pairing function. Each validating node signs the message with its private key, generating a signature point in group G2. After collecting multiple signatures, the aggregate signature is obtained by adding the points in a group operation. The aggregate signature remains a valid point in the G2 group, with a constant size. For verification, a single pairing operation is performed to check if the aggregate signature and aggregate public key satisfy the pairing equation, thus validating the authenticity of all original signatures.

The threshold signature scheme further enhances the security and fault tolerance of the system. By using Shamir's Secret Sharing, the private key is divided into n shares, requiring at least t shares to reconstruct the original private key. This means that even if t-1 nodes are compromised, the attacker cannot obtain the full private key; meanwhile, as long as t honest nodes are online, the system can operate normally.

The implementation of secret sharing is based on polynomial interpolation. A t-1 degree polynomial is generated, with the private key as the constant term and other coefficients randomly chosen. Each participant receives the value of the polynomial at a specific point as a share. Any t shares can reconstruct the original polynomial through Lagrange interpolation, thereby obtaining the private key; less than t shares cannot obtain any information about the private key.

During the consensus process, validating nodes use their own share to sign messages, generating signature shares. After collecting t signature shares, the complete signature is obtained through weighted aggregation using Lagrange interpolation coefficients. This scheme ensures security while achieving O(1) verification complexity—validators only need to verify the aggregated single signature, without having to individually verify each share signature.

2.4 Consensus and Execution Separation: The Power of Decoupling

Traditional blockchains tightly couple consensus and execution, leading to mutual constraints. Consensus must wait for execution to complete before progressing, and execution is limited by the serialization requirement of consensus. Bitroot breaks through this bottleneck by separating consensus and execution.

Asynchronous processing architecture is the foundation of the separation. The consensus module focuses on determining transaction order and reaching consensus quickly; the execution module parallelly processes transaction logic in the background, performing state transitions. The two communicate asynchronously through a message queue—consensus results are passed to the execution module via the queue, and execution results are fed back to the consensus module through the queue. This decoupled design allows consensus to continue moving forward without waiting for execution to finish.

Resource isolation further optimizes performance. The consensus module and the execution module use independent resource pools to avoid resource contention. The consensus module is equipped with a high-speed network interface and dedicated CPU cores, focusing on network communication and message processing; the execution module has ample memory and multi-core processors, focusing on compute-intensive state transitions. This specialized division of labor allows each module to fully utilize hardware performance.

Batch processing magnifies the pipeline effect. The leader node bundles multiple block proposals into batches for overall consensus. Through batching, the consensus overhead of k blocks is distributed, significantly reducing the average confirmation latency per block. Additionally, BLS signature aggregation technology complements batching perfectly—regardless of how many blocks are in a batch, the aggregate signature size remains constant, and verification time is close to constant.

2.5 Performance Breakthrough: From Theory to Practice

In a standardized testing environment (AWS c5.2xlarge instance), Pipeline BFT demonstrates outstanding performance:

Latency Performance: In a 5-node network, the average latency is 300 milliseconds, increasing to only 400 milliseconds in a 21-node network. The latency grows slowly as the number of nodes increases, confirming excellent scalability.

Throughput Performance: The final test results reach 25,600 TPS, achieved through Pipeline BFT and state sharding technology for a high-performance breakthrough.

Performance Improvement: Compared to traditional BFT, latency has been reduced by 60% (1 second to 400 milliseconds), throughput has increased 8 times (3,200 to 25,600 TPS), and communication complexity has been optimized from O(n²) to O(n²/D).

III. Optimistic Parallelization of EVM: Unleashing Multi-Core Compute Power

3.1 The Historical Burden of EVM Serialization

At the inception of the Ethereum Virtual Machine (EVM), a global state tree model was adopted to simplify system implementation—where all accounts and contract states are stored in a single state tree, and all transactions must be strictly executed serially. While this design was acceptable in the early days of relatively simple blockchain applications, the rise of complex applications such as DeFi and NFTs has made serialized execution a performance bottleneck.

State access conflicts are the fundamental reason for serialization. Even if two transactions operate on completely unrelated accounts—such as Alice transferring to Bob and Charlie transferring to David—they still have to be processed serially. Because the EVM cannot pre-determine which state transactions will access, it must conservatively assume that all transactions may conflict, thus enforcing serial execution. Dynamic dependencies exacerbate the complexity of the issue. Smart contracts can dynamically compute the addresses to access based on input parameters, and these dependencies cannot be determined at compile time. For example, a proxy contract may call different target contracts based on user input, and its state access pattern is entirely unpredictable before execution. This makes static analysis almost impossible, rendering secure parallel execution unachievable.

The high cost of rollbacks makes optimistic parallelization challenging. If conflicts are discovered after attempting optimistic parallel execution, all affected transactions must be rolled back. In the worst-case scenario, the entire batch needs to be re-executed, resulting in a waste of computational resources and a significant impact on user experience. Minimizing the scope and frequency of rollbacks while ensuring security is a key challenge for parallelizing the EVM.

3.2 Three-Stage Conflict Detection: Balancing Security and Efficiency

Bitroot employs a three-stage conflict detection mechanism that maximizes parallel execution efficiency while ensuring security. These three stages perform detection and validation before execution, during execution, and after execution, establishing a multi-layered security defense network.

Stage One: Pre-execution Screening reduces conflict probability through static analysis. A dependency analyzer parses transaction bytecode to identify the states that may be accessed. For a standard ERC-20 transfer, it can precisely identify accesses to the sender's and receiver's balances; for complex DeFi contracts, it can identify at least the main state access patterns.

An improved Counting Bloom Filter (CBF) provides a fast screening mechanism. Traditional Bloom Filters only support adding elements and not removing them. The CBF implemented by Bitroot maintains a counter for each position, supporting dynamic addition and removal of elements. The CBF occupies only 128KB of memory, uses 4 independent hash functions, and controls false positive rate to below 0.1%. Through the CBF, the system can quickly determine if two transactions may have a state access conflict.

Smart grouping strategy organizes transactions into batches that can be executed in parallel. The system models transactions as nodes in a graph, where a directed edge is drawn between two transactions that may conflict. A greedy coloring algorithm is used to color the graph, allowing transactions of the same color to safely run in parallel. This method ensures correctness while maximizing parallelism.

Stage Two: In-execution Monitoring performs dynamic detection during transaction execution. Even if a transaction passes pre-execution screening, it may still access states beyond the prediction during actual execution, requiring runtime conflict detection.

A fine-grained read-write lock mechanism provides concurrency control. Bitroot has implemented address- and slot-based locks instead of coarse-grained contract-level locks. Read locks can be held by multiple threads simultaneously, allowing concurrent reads; write locks can only be held by a single thread and exclude all read locks. This fine-grained locking mechanism ensures security while maximizing parallelism.

Versioned state management implements optimistic concurrency control. It maintains a version number for each state variable, records the version of the read state during transaction execution, and checks if all read state versions remain consistent after execution. If the version numbers change, indicating a read-write conflict, a rollback and retry are necessary. This mechanism borrows from database Multi-Version Concurrency Control (MVCC) and is equally effective in a blockchain context.

Dynamic conflict resolution employs a granular rollback strategy. When a conflict is detected, only the directly conflicted transaction is rolled back, not the entire batch. Through precise dependency analysis, the system can identify which transactions depend on the rolled-back transaction, minimizing the rollback scope. The rolled-back transaction is re-enqueued for execution in the next batch.

Phase Three: Post-Execution Verification to Ensure Final State Consistency. After all transactions have been executed, the system performs a global consistency check. By computing the Merkle Tree Root Hash of the state changes and comparing it with the expected state root, the system ensures the correctness of the state transition. At the same time, it verifies the version consistency of all state changes to ensure there are no missing version conflicts.

The state merging process adopts a two-phase commit protocol to ensure atomicity. In the prepare phase, all execution engines report their execution results but do not submit them. In the commit phase, once the coordinator confirms the consistency of all results, a global commit is made. If any execution engine reports a failure, the coordinator initiates a global rollback to ensure state consistency. This mechanism draws inspiration from the classic design of distributed transactions, ensuring the reliability of the system.

 lowchart TD
    A[Transaction Batch Input] --> 
    B[Phase One: Pre-Execution Screening]    B --> 
    C{Static Analysis<br/>Conflict Detection (CBF)}    
    C -->|No Conflict| D[Smart Grouping<br/>Greedy Coloring Algorithm]    
    C -->|Potential Conflict| E[Conservative Grouping<br/>Serial Execution]        
    D --> F[Phase Two: Execution Monitoring]    
    E --> F    
    F --> G[Fine-Grained Read-Write Locks<br/>Versioned State Management]    
    G --> H{Conflict Detected?}    
lowchart TD
    A[Transaction Batch Input] --> 
    B[Phase One: Pre-Execution Screening]    B --> 
    C{Static Analysis<br/>Conflict Detection (CBF)}    
    C -->|No Conflict| D[Smart Grouping<br/>Greedy Coloring Algorithm]    
    C -->|Potential Conflict| E[Conservative Grouping<br/>Serial Execution]        
    D --> F[Phase Two: Execution Monitoring]    
    E --> F    
    F --> G[Fine-Grained Read-Write Locks<br/>Versioned State Management]    
    G --> H{Conflict Detected?}    
    

3.3 Scheduling Optimization: Keeping Every Core Busy

The effectiveness of parallel execution depends not only on parallelism but also on load balancing and resource utilization. Bitroot has implemented multiple scheduling optimization techniques to ensure efficient operation of each CPU core.

The work-stealing algorithm addresses the issue of load imbalance. Each worker thread maintains its own double-ended queue and executes tasks taken from the front of the queue. When a thread's queue is empty, it randomly selects a busy thread and "steals" a task from the back of its queue. This mechanism achieves dynamic load balancing, avoiding scenarios where some threads are idle while others are busy. Tests have shown that work stealing increased CPU utilization from 68% to 90% and improved overall throughput by approximately 22%.

NUMA-aware scheduling optimizes memory access patterns. Modern servers utilize Non-Uniform Memory Access (NUMA) architecture, where memory access latency across NUMA nodes is 2-3 times higher than local access. Bitroot's scheduler detects the system's NUMA topology, binds worker threads to specific NUMA nodes, and prioritizes tasks that access local memory. Additionally, based on the hash of the account address, the state is partitioned across different NUMA nodes, ensuring that transactions accessing specific accounts are scheduled to execute on the corresponding node. NUMA-aware scheduling reduced memory access latency by 35% and increased throughput by 18%.

Dynamic parallelism adjustment adapts to different workloads. Higher parallelism is not always better –

Excessive parallelism can intensify lock contention, ultimately reducing performance. Bitroot monitors real-time metrics such as CPU utilization, memory bandwidth usage, and lock contention frequency to dynamically adjust the number of threads involved in parallel execution. When CPU utilization is low and lock contention is not severe, the parallelism is increased; when lock contention is high, parallelism is reduced to minimize contention. This adaptive mechanism allows the system to automatically optimize performance under varying workloads.

3.4 Performance Breakthrough: From Theory to Practical Validation In a Standardized Testing Environment, Optimistic Parallelized EVM Demonstrates Significant Performance Improvement:

Simple Transfer Scenario: With a 16-thread configuration, throughput increased from 1,200 TPS to 8,700 TPS, achieving a 7.25x speedup with a conflict rate below 1%.

Complex Contract Scenario: In DeFi contract scenarios with a conflict rate of 5-10%, 16 threads still achieved 5,800 TPS, a 7.25x improvement over the serial 800 TPS.

AI Computation Scenario: With a conflict rate below 0.1%, throughput increased from 600 TPS to 7,200 TPS using 16 threads, resulting in a 12x speedup.

Latency Analysis: End-to-end average latency is 1.2 seconds, with parallel execution taking 600 milliseconds (50%), state merging taking 200 milliseconds (16.7%), and network propagation taking 250 milliseconds (20.8%).

4. State Sharding: The Ultimate Solution for Horizontal Scalability

4.1 State Sharding Architecture Design

State sharding is the core technology Bitroot employs to achieve horizontal scalability, splitting the blockchain state into multiple shards for parallel processing and storage.

Sharding Strategy: Bitroot uses an account-based hash sharding strategy to distribute account states across different shards. Each shard maintains an independent state tree and facilitates shard-to-shard communication through an inter-shard communication protocol.

Shard Coordination: Shard coordinators are used to manage transaction routing and state synchronization across shards. Coordinators are responsible for breaking down cross-shard transactions into multiple sub-transactions to ensure inter-shard consistency.

State Synchronization: An efficient inter-shard state synchronization mechanism is implemented, reducing synchronization overhead through incremental sync and checkpoint techniques.

4.2 Cross-Shard Transaction Processing

Transaction Routing: An intelligent routing algorithm directs transactions to the appropriate shard, minimizing cross-shard communication overhead.

Atomicity Guarantee: Cross-shard transactions' atomicity is ensured through a two-phase commit protocol, ensuring that transactions either all succeed or all fail.

Conflict Detection: A mechanism for detecting cross-shard conflicts is implemented to prevent state inconsistencies between shards.

5. Performance Comparison and Scalability Validation

5.1 Comparison with Leading Blockchains

Confirmation Time: Bitroot's 400-millisecond finality is on par with Solana, far surpassing Ethereum's 12 seconds and Arbitrum's 2-3 seconds, supporting real-time and high-frequency transactions.

Throughput: The final test results achieved 25,600 TPS, leveraging Pipeline BFT and state sharding technology to attain high performance while maintaining EVM compatibility, demonstrating outstanding performance.

Cost Advantage: Gas fees are only 1/10 to 1/50 of Ethereum's, comparable to Layer 2 solutions, significantly enhancing application economics.

Ecosystem Compatibility: Full EVM compatibility ensures seamless migration at zero cost from the Ethereum ecosystem, allowing developers to effortlessly leverage high performance.

5.2 Scalability Test Results

Final Test Results: 25,600 TPS, 1.2 Seconds Latency, 85% Resource Utilization, validating the effectiveness of Pipeline BFT and State Sharding technologies.

Performance Comparison: Compared to traditional BFT achieving 500 TPS at the same scale, Bitroot has seen a 51x performance improvement, demonstrating the significant benefits of technological innovation.

Six, Application Scenarios and Technical Outlook

6.1 Core Application Scenarios

DeFi Protocol Optimization: Through parallel execution and quick confirmation, supporting high-frequency trading and arbitrage strategies, with over 90% reduction in Gas fees, fostering the prosperous development of the DeFi ecosystem.

NFT Market and Gaming: High throughput supporting mass NFT batch minting, low-latency confirmation providing a user experience close to traditional gaming, promoting NFT asset liquidity.

Enterprise Applications: Supply chain transparency management, digital identity authentication, data rights, and transactions, providing blockchain infrastructure for enterprise digital transformation.

6.2 Technical Challenges and Evolution

Current Challenges: State bloat issue requiring continuous optimization of storage mechanisms; complexity of cross-shard communication needing further improvement; security in a parallel execution environment requiring ongoing auditing.

Future Directions: Machine learning optimization of system parameters; hardware acceleration integrating TPUs, FPGAs, and other specialized chips; cross-chain interoperability to build a unified service ecosystem.

6.3 Technical Value Summary

Core Breakthroughs: Pipeline BFT achieves 400ms confirmation, 30x faster than traditional BFT; EVM optimistic parallelization sees a 7.25x performance improvement; state sharding supports linear scalability.

Practical Value: Full EVM compatibility ensures zero-cost migration; 25,600 TPS throughput and over 90% cost reduction validated through benchmark testing; building a complete high-performance blockchain ecosystem.

Standard Contributions: Driving the establishment of industry technical standards; constructing an open-source technology ecosystem; transforming theoretical research into engineering practices, providing a viable path for the large-scale application of high-performance blockchains.

Conclusion: Initiating a New Era of High-Performance Blockchains

Bitroot's success lies not only in technological innovation but also in translating innovation into practical engineering solutions. Through the three major technological breakthroughs of Pipeline BFT, EVM optimistic parallelization, and state sharding, Bitroot has provided a comprehensive technical blueprint for high-performance blockchain systems.

In this technical solution, we see a balance between performance and decentralization, a unification of compatibility and innovation, and a coordination between security and efficiency. The wisdom of these technical trade-offs is not only reflected in the system design but also embodied in every detail of engineering practice.

More importantly, Bitroot has laid a technical foundation for the popularization of blockchain technology. Through high-performance blockchain infrastructure, anyone can build complex decentralized applications and enjoy the value brought by blockchain technology. This popularized blockchain ecosystem will drive blockchain technology from a technical experiment to large-scale application, providing global users with more efficient, secure, and reliable blockchain services.

With the rapid development of blockchain technology and the continuous expansion of application scenarios, Bitroot's technical solution will provide important technical references and practical guidance for the development of high-performance blockchains. We have reason to believe that in the near future, high-performance blockchains will become a crucial infrastructure for the digital economy, providing robust technical support for the digital transformation of human society.

This article is from a contributor and does not represent the views of BlockBeats.