Traditional blockchain transaction processing models, such as Solana’s, employ account-level access pattern analysis to batch transactions and prevent dirty reads through strict isolation. This ensures parallel execution, but it conservatively treats all account accesses within a transaction as concurrent, leading to underutilized parallelism.
InfiniSVM enhances this model by introducing fine-grained sequence prediction of read-write operations. By analyzing the temporal ordering of account accesses, InfiniSVM constructs an optimized locking schedule, allowing concurrent execution of transactions accessing the same accounts, provided their read-write sequences do not conflict. This dynamic scheduling model reduces lock contention while maintaining serializability guarantees.
InfiniSVM leverages a simulation stage to obtain estimated read-write sequences before transactions enter the scheduling phase.
The optimal scheduling problem is NP-hard, resembling a bin-packing problem. To achieve sub-millisecond scheduling times, InfiniSVM employs:
Shortest Makespan First (SMF) Algorithm
Parallel Scheduler Ensemble
By implementing execution-aware scheduling and parallel scheduler ensembles, InfiniSVM achieves:
This fine-grained, predictive approach represents a major advancement in high-performance blockchain transaction processing.