ZKProphet: Optimizing Zero-Knowledge Proof Performance on GPU Architectures ∞ Research

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Briefing

This paper addresses the critical performance limitations of Zero-Knowledge Proofs (ZKPs) on Graphics Processing Units (GPUs), which are crucial for scaling private and verifiable computing in blockchain ecosystems. The foundational breakthrough is the systematic identification of the Number-Theoretic Transform (NTT) as the dominant bottleneck in ZKP generation, accounting for up to 90% of latency after prior Multi-Scalar Multiplication (MSM) optimizations. This re-characterization shifts the focus of hardware acceleration research towards NTT, implying that future blockchain architectures can achieve significantly faster, more efficient ZKP verification through targeted GPU optimization, thereby unlocking greater scalability and broader adoption of privacy-preserving technologies.

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Context

Prior to this research, the prevailing challenge in accelerating Zero-Knowledge Proofs (ZKPs) on GPUs centered on optimizing Multi-Scalar Multiplication (MSM), which was widely considered the primary computational bottleneck. While significant progress was made in parallelizing MSM, a comprehensive understanding of subsequent performance limitations, particularly concerning the scalability of ZKPs on modern GPU architectures, remained elusive. This theoretical limitation hindered further advancements in proof generation efficiency, impacting the practical deployment of ZKPs in high-throughput decentralized systems.

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Analysis

The core mechanism of this paper, ZKProphet, involves a comprehensive performance study to precisely locate and characterize execution bottlenecks within Zero-Knowledge Proof (ZKP) generation on GPUs. The research fundamentally differs from previous approaches by demonstrating that, following substantial speedups in Multi-Scalar Multiplication (MSM), the Number-Theoretic Transform (NTT) emerges as the critical performance limiter. Conceptually, ZKProphet dissects the ZKP proving process, revealing that existing NTT implementations frequently under-utilize GPU resources and lack architectural feature leverage, while the underlying arithmetic operations are constrained by the GPU’s 32-bit integer pipeline and data dependencies. This analysis provides a clear logical pathway for optimizing ZKPs by shifting focus to NTT and runtime parameter tuning.

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Parameters

Core Concept ∞ Zero-Knowledge Proofs on GPUs
New System/Study Name ∞ ZKProphet
Primary Bottleneck Identified ∞ Number-Theoretic Transform (NTT)
Key Computational Kernel ∞ Multi-Scalar Multiplication (MSM)
Hardware Limitation ∞ GPU 32-bit Integer Pipeline
Proposed Optimization ∞ Runtime Parameter Tuning

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Outlook

This research opens new avenues for optimizing Zero-Knowledge Proof (ZKP) hardware acceleration, particularly by re-prioritizing Number-Theoretic Transform (NTT) implementations. The next steps will likely involve developing novel NTT algorithms specifically designed for GPU architectures, leveraging asynchronous compute and memory operations, and exploring alternative data representations. In 3-5 years, these advancements could unlock significantly faster ZKP generation, enabling more scalable and private blockchain solutions, widespread adoption of verifiable computation in cloud environments, and the development of new privacy-preserving applications across various industries, from finance to artificial intelligence.

This research provides a decisive shift in the foundational understanding of Zero-Knowledge Proof performance, directing future optimization efforts toward critical, previously underestimated computational bottlenecks.

Signal Acquired from ∞ arxiv.org