GridSMR: Causal Compression for Sharded Blockchains

2026-10-01

AI Summary

This study addresses the problem in sharded blockchains where cross-shard operation latency scales linearly with causal depth. To overcome this limitation, the paper proposes a causal compression mechanism built upon an execute-then-consensus architecture. By enforcing account-level localized atomicity, the approach enables asynchronous cross-shard execution and decouples coordination overhead from computational causal chains, thereby fundamentally eliminating the dependence of cross-shard latency on causal depth. Experimental evaluations demonstrate that the proposed system achieves a throughput of 1.07 million transactions per second (TPS) with a single validator node and 193K TPS with four nodes. Furthermore, the end-to-end latency for 16-hop causal chains is reduced by a factor of 8.2. These results confirm that the mechanism effectively realizes scalable cross-shard processing characterized by both high throughput and low latency.

Abstract

We present GridSMR, a sharded blockchain that scales execution horizontally while allowing dependent cross-shard operations to progress within a single block. Existing sharded systems typically place coordination between dependent cross-shard steps, making latency grow with causal depth. GridSMR localizes atomicity to individual accounts and executes cross-account work asynchronously. Using an execute-before-agree architecture, dependent operations execute across shards as they become available, while consensus later validates and commits the resulting schedule. This enables Causal Compression: cross-shard latency need not grow with the causal depth of a computation. GridSMR scales single-validator execution to 1.07M requests/s and four-validator execution to 193K committed requests/s, while reducing 16-hop causal-chain latency by 8.2x versus deferred execution.

Problem

• sharded blockchain
• cross-shard operations
• causal depth
• scalability
• latency

Innovation

• Sharded Blockchain
• Causal Compression
• Execute-Before-Agree
• Cross-Shard Operations
• Scalability