CROSC: Compilation-Runtime Joint Optimization for Fast Smart Contract Execution

State access is a critical part of smart contract execution which seriously affects the efficiency of smart contract execution in the mainstream Ethereum blockchain. To reduce state access latency, existing studies typically require manual source code modifications, which in practice may deliver limited performance gains and shift the burden to developers. In this paper, we propose CROSC to reduce state access latency and improve smart contract execution efficiency by a compilation-runtime joint optimization approach. CROSC consists of three key parts: 1) a runtime memory management mechanism named Fast State Memory (FastSM) to fully utilize the working memory and provide the context for the contract compiler; 2) a State Variable Address Relocation (SVAR) strategy to minimize costly persistent storage operations by precisely redirecting state variable access targets during compilation; 3) a one-shot unpacking design that eliminates frequent decoding overhead for low-bitwidth state variables. Preliminary experimental results highlight that, compared with the baseline compilation and runtime system of Ethereum, CROSC can achieve 2.5× and 7.5× speedups for single state load and store operations, respectively. CROSC reduces state access latency by up to 81.3%, and overall contract execution latency by 32.9% on average across 14 typical types of smart contracts. Extended evaluations on ERC20 and ERC721 token standard contracts show that CROSC delivers significant benefits in critical areas while remaining unobtrusive for less intensive state operations.

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Publication Details

Journal
IEEE Transactions on Computers
Published
2025-06-27
DOI
https://doi.org/10.1109/tc.2025.3583734
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
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article

CROSC: Compilation-Runtime Joint Optimization for Fast Smart Contract Execution

Yaozheng Fang, Wenqiang Cui, Ye Lu, Jinni Yang et al.
IEEE Transactions on Computers
Blockchain Technology Applications and Security
article

CROSC: Compilation-Runtime Joint Optimization for Fast Smart Contract Execution

Yaozheng Fang, Wenqiang Cui, Ye Lu, Jinni Yang, Surong Dai
article en

Abstract

State access is a critical part of smart contract execution which seriously affects the efficiency of smart contract execution in the mainstream Ethereum blockchain. To reduce state access latency, existing studies typically require manual source code modifications, which in practice may deliver limited performance gains and shift the burden to developers. In this paper, we propose CROSC to reduce state access latency and improve smart contract execution efficiency by a compilation-runtime joint optimization approach. CROSC consists of three key parts: 1) a runtime memory management mechanism named Fast State Memory (FastSM) to fully utilize the working memory and provide the context for the contract compiler; 2) a State Variable Address Relocation (SVAR) strategy to minimize costly persistent storage operations by precisely redirecting state variable access targets during compilation; 3) a one-shot unpacking design that eliminates frequent decoding overhead for low-bitwidth state variables. Preliminary experimental results highlight that, compared with the baseline compilation and runtime system of Ethereum, CROSC can achieve 2.5× and 7.5× speedups for single state load and store operations, respectively. CROSC reduces state access latency by up to 81.3%, and overall contract execution latency by 32.9% on average across 14 typical types of smart contracts. Extended evaluations on ERC20 and ERC721 token standard contracts show that CROSC delivers significant benefits in critical areas while remaining unobtrusive for less intensive state operations.

IEEE Transactions on ComputersVol. 74(9)
Nankai University (CN)
Openalex Percentile: Top 78%
Blockchain Technology Applications and Security
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CROSC: Compilation-Runtime Joint Optimization for Fast Smart Contract Execution — Yaozheng Fang, Wenqiang Cui, et al. · IEEE Transactions on Computers (2025) | TGRS Research Map | TGRS