Accelerating Merkle Patricia Trie with GPU

Abstract

Merkle Patricia Trie (MPT) is a type of trie structure that offers efficient lookup and insert operators for immutable data systems that require multi-version access and tamper-evident controls, such as blockchains and verifiable databases. The performance of these systems is critically dependent on the throughput of the underlying index structure MPT. In this paper, we present a novel approach to accelerate MPT by leveraging the massive parallelism of GPU. However, achieving it is challenging as (i) lock-free data structures are difficult to implement and (ii) traditional fine-grained locking does not scale on GPU. To address them, we first analyze the technical challenges of accelerating MPT via GPU, including node splitting conflicts and hash computing conflicts caused by parallel insert operations. We then propose a lock-free algorithm PhaseNU and a lock-based algorithm LockNU on GPU to resolve the node splitting conflict. We also devise a decision model for users to choose the proper one for different workloads. We next propose a GPU-based hash-compute algorithm PhaseHC to avoid hash computing conflicts. Last, we demonstrate the effectiveness of our proposed techniques by: (i) integrating them into both the real-world blockchain system Geth and verifiable database LedgerDB, and demonstrating its superiority with corresponding workloads; and (ii) conducting extensive experimental studies on two real-world datasets and one synthetic dataset. Our proposed solutions significantly outperform the deployed MPT solution in Geth in all datasets.