GPU utilization can be a misleading metric

The article discusses the limitations of using GPU Utilization as a primary metric for assessing GPU performance in machine learning tasks. While GPU Utilization, often measured via tools like nvidia-smi, can indicate high usage, it does not necessarily reflect the actual computational efficiency of the GPU. The authors highlight that one can achieve 100% GPU utilization by merely reading or writing to memory without performing any computations, which can be misleading. Instead, they advocate for using Model FLOPS (MFUs) as a more accurate measure of performance, as it compares the observed throughput to the GPU's theoretical maximum. The authors found that their model training was only achieving about 20% MFUs despite 100% GPU utilization, indicating significant room for improvement. They emphasize the importance of monitoring SM Efficiency, which measures the active streaming multiprocessors during GPU operations, to identify inefficiencies in model execution. By optimizing their training loop and implementing fused kernels, they achieved a 4x speedup in training time and increased MFUs to 38%. The article concludes by recommending that AI teams track both SM Efficiency and GPU Utilization for a more comprehensive understanding of GPU performance.

- GPU Utilization can be misleading as it does not reflect actual computational efficiency.

- Model FLOPS (MFUs) is a better metric for assessing GPU performance.

- Monitoring SM Efficiency helps identify inefficiencies in model execution.

- Optimizing training loops and using fused kernels can significantly improve performance.

- AI teams should track both SM Efficiency and GPU Utilization for better insights.