Revealing NVIDIA Closed-Source Driver Command Streams for CPU-GPU Runtime Behavior Insight

2026-04-29

AI Summary

This work addresses the lack of transparency in NVIDIA’s closed-source user-space driver, which obscures the translation of CUDA API calls into hardware commands and impedes understanding of GPU behavior and performance attribution. The authors propose a novel approach that requires no modification to the proprietary driver, instead leveraging an open-source kernel driver, memory-mapped path instrumentation, and hardware watchpoints on the GPU’s doorbell registers to capture and reconstruct the complete low-level command stream with unprecedented accuracy. This methodology reveals the true DMA patterns and performance characteristics of CUDA data transfers and demonstrates that the low overhead of CUDA Graphs stems from their streamlined and efficient command submission mechanism. By significantly enhancing the interpretability of GPU runtime behavior, this approach establishes a new paradigm for middleware analysis and hardware-software co-design.

Abstract

For NVIDIA GPUs, CUDA is the primary interface through which applications orchestrate GPU execution, yet much of the logic that realizes CUDA operations resides in NVIDIA's closed-source userspace driver. As a result, the translation from high-level CUDA APIs to low-level hardware commands remains opaque, limiting both software understanding and performance attribution. This paper makes that command path visible. We recover the hardware command streams emitted by NVIDIA's closed-source userspace driver with full integrity by leveraging the recently open-sourced kernel driver, instrumenting the memory-mapping path, and installing a hardware watchpoint on the userspace mapping of the GPU doorbell register. This lets us capture complete command submissions at the moment they are committed. Using this methodology, we present two case studies. For CUDA data movement, we identify the DMA submission modes selected by the driver and characterize their raw hardware performance independently of driver overhead through CUDA-bypassing controlled command issuance. For CUDA Graphs, we show that the reduced launch overhead in newer CUDA releases is associated with a smaller command footprint and a more efficient submission pattern. Together, these results show that command-level visibility provides a practical basis for understanding and optimizing GPU middleware behavior, improving performance interpretation, and informing future hardware--software co-design for CUDA and related accelerator stacks.

Problem

• NVIDIA
• closed-source driver
• command stream
• CUDA
• GPU runtime behavior

Innovation

• command stream recovery
• closed-source driver analysis
• GPU hardware visibility
• CUDA bypass
• doorbell register instrumentation