Validation of GPU Computation in Decentralized, Trustless Networks

TL;DR

This paper investigates the challenges of verifying GPU computations in decentralized, trustless networks and proposes novel probabilistic verification frameworks to ensure computational integrity without relying on trusted hardware or deterministic execution.

Contribution

It introduces new probabilistic verification methods adapted for GPU workloads, including a binary trusted node model and a trustless ternary consensus framework.

Findings

Identified limitations of existing verification approaches for GPU computations.

Developed probabilistic verification frameworks suitable for untrusted, decentralized networks.

Demonstrated the effectiveness of these frameworks in ensuring computational integrity.

Abstract

Verifying computational processes in decentralized networks poses a fundamental challenge, particularly for Graphics Processing Unit (GPU) computations. Our investigation reveals significant limitations in existing approaches: exact recomputation fails due to computational non-determinism across GPU nodes, Trusted Execution Environments (TEEs) require specialized hardware, and Fully Homomorphic Encryption (FHE) faces prohibitive computational costs. To address these challenges, we explore three verification methodologies adapted from adjacent technical domains: model fingerprinting techniques, semantic similarity analysis, and GPU profiling. Through systematic exploration of these approaches, we develop novel probabilistic verification frameworks, including a binary reference model with trusted node verification and a ternary consensus framework that eliminates trust requirements. These methodologies establish a foundation for ensuring computational integrity across untrusted networks while addressing the inherent challenges of non-deterministic execution in GPU-accelerated workloads.