Addressing the Challenges for High Performance GPU Testing

Addressing the Challenges for High Performance GPU Testing

Co-authors, NVIDIA:
Joe Sarmiento – Director Test Engineering
Mahmut Yilmaz – Director DFX Methodology
Derek Lee – Test Engineering Manager

Authors, Menlo Micro:
Harry Liu – Director, RF Product Marketing
Stewart Yang – Sr. Principal System Application Engineer

Introduction

The rapid advancement of artificial intelligence (AI) and machine learning (ML) is driving unprecedented demand for computing power, with GPUs (Graphics Processing Units) now serving as the backbone of modern high-performance computing.

From accelerating large language models to powering scientific simulations and autonomous systems, GPUs deliver the extraordinary parallel processing performance today’s workloads demand.

In tandem with the rise of AI, high-speed interconnect technologies such as PCI Express (PCIe) are evolving at a rapid pace.

Each new generation—Gen4, Gen5, Gen6 and now Gen7—has doubled bandwidth, dramatically increasing the complexity of both system design and production
validation.

As GPUs transition from PCIe Gen5 to Gen6 and beyond, the demand for robust, scalable, and cost-effective production test strategies has become more critical than ever.

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