GPU Metrics - Power¶
In the previous blog, we discussed why tracking and reporting GPU SM Clock metrics matters. In this blog, we will dive deeper into another critical GPU metric i.e. GPU Power.
Important
Navigate to documentation for Rafay's integrated capabilities for Multi Cluster GPU Metrics Aggregation & Visualization.
Why is it Important?¶
The power consumption metric is critically important for GPUs for several key reasons. The important ones are described below.
Thermal Management and Throttling¶
GPUs consume significant power, and higher power consumption leads to increased heat generation. If a GPU overheats, it can trigger thermal throttling, where the GPU automatically reduces its clock speeds (including SM clocks) to prevent damage.
Monitoring power consumption helps to ensure that cooling systems are adequate and that the GPU operates within safe thermal limits, avoiding performance degradation.
Performance Optimization¶
Power consumption often correlates with performance efficiency. By analyzing power usage relative to workload and performance, developers can identify whether the GPU is being efficiently utilized.
For example, high power usage with low computational output could indicate inefficiencies such as memory bottlenecks, thermal throttling, or suboptimal code execution. Conversely, reducing power consumption while maintaining high performance signals optimized resource usage.
Energy Efficiency and Operational Costs¶
In data centers and large-scale AI/ML environments, GPUs are often used in clusters, where power consumption directly affects operational costs. Energy-efficient GPUs reduce electricity bills and cooling requirements, leading to significant cost savings. For example, optimizing a GPU's power consumption during long training or inference jobs can substantially reduce expenses over time.
Hardware Longevity¶
High power consumption over prolonged periods can cause hardware degradation, potentially shortening the lifespan of a GPU due to constant high temperatures. Monitoring power consumption helps prevent overloading the hardware, ensuring a longer lifespan for GPUs, especially in continuous-use scenarios like cloud computing and high-performance computing (HPC).
Real Life Scenarios¶
Here are two real-life scenarios where proactively monitoring GPU power metrics were useful for the organization.
Data Center for AI Model Training¶
A tech company operating a large data center for AI model training noticed increasing operational costs, primarily driven by GPU power consumption. The center runs multiple GPUs in parallel to train massive neural networks for natural language processing (NLP) tasks.
Impact By monitoring the power consumption metrics, the company found that some GPUs were consuming excessive power without a proportional increase in computational performance. Upon investigation, they realized that several models were not optimized for the hardware, leading to inefficient use of power. Additionally, they discovered that certain models were consuming power even when the GPUs were underutilized during phases of data pre-processing.
Cloud Gaming Service¶
A company offering cloud gaming services relies on powerful GPUs in data centers to render games in real-time and stream them to users. As user demand increased, the company noticed that their GPUs were consuming more power than expected, which significantly increased their operational costs and caused occasional overheating issues.
Impact: By closely monitoring the GPU power consumption metrics, they discovered that certain GPUs were consuming excessive power during less demanding gaming sessions. In some cases, the GPUs were running at near-maximum power even for games that didn’t require high-performance rendering. Additionally, during peak usage hours, power spikes led to thermal throttling, which impacted game performance and user experience.
How Rafay Helps with GPU Power Metrics¶
As we learnt in the prior blog, Rafay automatically scapes GPU metrics and aggregates them centrally in a time series database at the Controller. This data is then made available to authorized users via intuitive charts and dashboards. Shown below is an illustrative image of GPU Power usage metrics for a Nvidia GPU.
Here is a video that showcases how an administrator can use the integrated GPU dashboards to monitor and visualize GPU metrics. All the data they require is literally just a click away.
Conclusion¶
By adjusting GPU power consumption based on actual need and by optimizing workloads, organizations can reduce power consumption significantly while maintaining high performance. This will also result in significantly lower electricity bills and reduced cooling costs for the data center.
Sign up for a free Org if you want to try this OR request for a demo OR see us in person at our booth at upcoming events