Building a Scalable UVM-Based Testbench for GPU Compute Units

Abstract

Modern graphics processing units have evolved into complex massively parallel computing engines that demand sophisticated verification methodologies capable of validating thousands of concurrent threads executing across intricate memory hierarchies and specialized execution pipelines. Traditional verification approaches struggle to adequately address the unique challenges posed by Single Instruction, Multiple Thread execution models, dynamic thread scheduling, and complex interactions between compute units and multi-level cache systems. This article presents a comprehensive Universal Verification Methodology-based testbench architecture specifically designed for GPU compute unit verification, addressing critical gaps in existing verification practices through innovative SIMT-aware stimulus generation, integrated memory subsystem modeling, and scalable test generation frameworks. The proposed framework combines established UVM principles with GPU-specific verification techniques, creating a modular and reusable architecture that supports diverse configurations while maintaining systematic coverage collection and intelligent corner case detection. Extensive experimental evaluation across representative GPU workloads demonstrates substantial improvements in verification quality, debug efficiency, and development productivity compared to traditional approaches. The architecture’s parameterized design enables seamless adaptation across different GPU generations while its extensible structure provides a foundation for future verification challenges, including AI accelerators and chiplet-based architectures.