Pipelonk: Accelerating End-to-End Zero-Knowledge Proof Generation on GPUs for PLONK-Based Protocols

Abstract

Zero-knowledge proofs (ZKPs) are cryptographic protocols that allow verification of statements without disclosing the underlying information. Among them, PLONK-based ZKPs are particularly notable for offering succinct, non-interactive proofs of knowledge with a universal trusted setup, leading to widespread adoption in blockchain and cryptocurrency applications. Nonetheless, their broader deployment is hindered by long proof-generation times and substantial memory demands. While GPUs can accelerate these computations, their limited memory capacity introduces significant challenges for efficient end-to-end proof generation.

This paper presents Pipelonk, a GPU-accelerated framework for end-to-end PLONK proof generation with two key contributions. First, Pipelonk introduces a segmentable operator library that offloads all operations, including those not trivially parallelized, to GPUs through new designs. Each operator supports segmented execution, allowing inputs to be divided into smaller segments processed independently, thus enabling large-scale computations on memory-constrained devices. Second, Pipelonk provides a pipeline executor that overlaps computation and data transfer. It globally schedules compute- and memory-intensive tasks while preserving data and security dependencies, balances transfer-latency hiding against peak memory, and adaptively selects per-operator segment sizes by modeling memory capacity and computational characteristics to maximize compute-transfer overlap. Evaluation shows that Pipelonk runs efficiently on devices with 8GB to 80GB memory, achieving an average speedup of 10.7× and up to 19.4× over the state-of-the-art baseline.