Optimizing Merkle Tree Operations in a High-performance Blockchain Virtual Machine: Architectural Approaches and Performance Analysis

Abstract

To analyze architectural and algorithmic approaches for optimizing Merkle tree operations in a high-performance blockchain virtual machine and to assess their role in achieving high transaction throughput. This study was designed as a structural–functional and comparative analytical investigation. It examined how the execution model, authenticated data structures, cryptographic primitives, and storage subsystem interact to influence throughput, proof size, and state-update efficiency. The study was conducted through analysis of publicly available Rust implementations and technical materials related to a high-performance blockchain virtual machine during the preparation of this research.Core modules of the virtual machine, transaction manager, cryptographic subsystem, and storage layer were analyzed. Binary Merkle Tree and Sparse Merkle Tree algorithms, as well as the Quick Merkle Database design, were reconstructed from implementation details. The study assessed insertion, update, and proof-generation complexity, write amplification on solid-state drives, and benchmark-based throughput under workloads dominated by simple transfers and memory-resident hot state. The findings showed that the system replaces a monolithic Merkle Patricia Trie with a hybrid model using Binary Merkle Trees for immutable data and Sparse Merkle Trees for mutable state. Sparse Merkle Tree proofs were reduced from 8192 bytes to several hundred bytes in sparse cases through bitmask compression and omission of default siblings. Additional gains were achieved through implicit default values, precomputed zero hashes, and single-leaf optimization. Deterministic parallel scheduling based on the Unspent Transaction Output model and strict access lists enabled conflict-free multi-core execution. The Quick Merkle Database reduced write amplification through in-memory merkleization and append-only logging. Under favorable workloads, the architecture sustained about 21,000 transactions per second on commodity multi-core processors. Specialized Merkle structures, deterministic parallel execution, and append-only memory-indexed storage together provide an effective strategy for improving blockchain scalability while preserving verifiable state transitions.