AdaH-Chain: Deterministic capacity-aware GNNs sharding in heterogeneous blockchains

Abstract

Modern blockchain sharding protocols increasingly deploy Graph Neural Networks (GNNs) to optimize state partitioning. However, executing these learning heuristics across heterogeneous networks introduces two funda-mental challenges. First, hardware-dependent floating-point arithmetic evaluates inconsistently across disparate instruction set architectures. This inconsistency triggers arithmetic state divergence and permanent consensus failures. Second, existing methods enforce uniform load distribution across shards. This ignores physical hardware disparities and forces powerful nodes to idle while waiting for resource-constrained validators, creating severe straggler bottlenecks. To address these challenges, we propose AdaH-Chain, a deterministic capacity-aware sharding framework. First, we design a Cross-ISA Deterministic Execution Engine. It replaces unsafe floating-point operations with a symmetric fixed-point quantization mechanism to mathematically guarantee bit-wise consensus safety. Second, we introduce a Capacity-Aware GNN Sharding algorithm. This mechanism maps extracted transaction subgraphs proportionally to verified hardware throughput limits, effectively eliminating the straggler effect. We evaluate AdaH-Chain using real transaction traces containing one million accounts from the Ethereum Mainnet. Experimental results confirm that the fixed-point engine preserves 94.7%of the original topological clustering accuracy while ensuring a 0% consensus divergence rate. By structurally aligning workloads with hardware capacities, AdaH-Chain delivers a peak throughput of 4,800 TPS, achieving a 1.9× gain over state-of-the-art learning protocols.