Despite the success in various scenarios, blockchain systems, especially EVM-compatible ones that serially execute transactions, still face the significant challenge of limited throughput. Concurrent transaction execution is a promising technique to accelerate transaction processing and increase the overall throughput. Existing concurrency control algorithms, however, fail to obtain enough speedups in real-world blockchains due to the high-contention workloads.

In this talk, I will propose a novel operation-level concurrency control algorithm designed for blockchains.
The core idea behind our algorithm is that only operations depending on conflicts should be executed serially, while all other conflict-free operations can be executed concurrently. Therefore, in contrast to the traditional approaches, which block or abort the entire transaction when encountering conflicts, our algorithm introduces a redo phase to resolve conflicts at the operation level by re-executing conflicting operations only. We also develop a set of data dependency tracking mechanisms to achieve precise identification and speedy re-execution for conflicting operations. We implement a prototype named ParallelEVM based on Go Ethereum and evaluate ParallelEVM using real-world Ethereum blocks. The evaluation results show that ParallelEVM achieves an average speedup of 4.28×. If combined with state prefetching techniques, ParallelEVM can further accelerate the transaction execution by 7.11×.