GPU Metrics - Framebuffer¶
In the previous blog, we discussed why tracking and reporting GPU power usage matters. In this blog, we will dive deeper into another critical GPU metric i.e. GPU Framebuffer usage.
Important
Navigate to documentation for Rafay's integrated capabilities for Multi Cluster GPU Metrics Aggregation & Visualization.
Why is it Important?¶
Technically, framebuffer is just a synonym for VRAM. It functions exactly like regular system RAM, but it is dedicated for data being processed by the GPU. When your VRAM is maxed out, the GPU would need to dump something else to make room. It can take a couple of seconds to remove the old data and load the new one. Swapping data in and out takes time and will add delays to the training or inference workflows.
The GPU framebuffer plays a significant role in both training and inference in machine learning and deep learning tasks, especially when dealing with high-resolution data like images or videos. Let us look at how and why it matters.
Training¶
During training, especially for computer vision tasks, large datasets of images or videos are passed through the GPU for processing. If these datasets are high-resolution, they consume significant amounts of memory in the framebuffer. Monitoring and optimizing framebuffer usage is critical to ensure that the GPU can store and process batches of training data efficiently.
If the framebuffer usage exceeds its capacity during training, the GPU may need to offload data to system memory, which is slower. This can cause training bottlenecks and significantly slow down the overall training time. Managing framebuffer resources ensures optimal performance and prevents memory overflow.
Training models often involves data augmentation (like flipping, rotating, or cropping images), which requires additional memory to store the modified data. Efficient framebuffer usage ensures that these augmented datasets can be handled without causing slowdowns or crashes.
Inference¶
Inference, especially in real-time applications such as object detection or video processing, relies on the GPU to quickly process and display predictions. The framebuffer stores the data being processed and the visual output being rendered. Inefficient framebuffer management during inference can lead to latency, reducing the speed at which predictions or decisions are made.
For inference tasks in autonomous systems, video analytics, or multi-camera setups, multiple streams of visual data may be processed simultaneously. These streams consume significant framebuffer resources. Optimizing usage ensures that the GPU can handle multiple inputs without causing delays or degrading performance.
In batch inference, where multiple inputs are processed at once, the GPU needs sufficient framebuffer memory to store and process all incoming data. Monitoring and managing framebuffer usage helps ensure that large batches can be handled efficiently, improving throughput for batch-based inference tasks.
Real Life Scenarios¶
Let us look at a few real-life scenarios where framebuffer metrics and usage issues impacted training in machine learning and deep learning.
Training a Self-Driving Car Vision Model¶
A self-driving car company is training a deep learning model to identify road objects (cars, pedestrians, signs) using high-resolution video feeds from multiple cameras. Each frame is processed by a neural network to detect and classify objects in real-time.
Issue
During training, the team noticed that the training process was extremely slow. By monitoring GPU framebuffer usage, they realized that the high-resolution video frames were consuming a large portion of the framebuffer, and the model was frequently offloading data to system memory, leading to training bottlenecks and excessive GPU memory swapping.
Solution
To optimize framebuffer usage, the team adjusted the batch size to reduce memory consumption and employed image augmentation techniques that generate lower-resolution images for part of the training. This adjustment significantly reduced the strain on the framebuffer, allowing for faster training times while still maintaining high model accuracy.
Training a Medical Image Segmentation Model¶
A medical research lab is using GPUs to train a model for segmenting tumors in 3D medical images like CT or MRI scans. These images are high-resolution and require large amounts of memory to process each 3D volume during training.
Issue
As the dataset size grew, the team faced out-of-memory (OOM) errors due to insufficient framebuffer space on the GPUs. The high-resolution 3D images were consuming excessive framebuffer memory, preventing the model from processing batches efficiently. As a result, the training process was frequently interrupted, making it difficult to complete the training on time.
Solution
The researchers reduced the size of each 3D image by using image downsampling techniques and patch-based training, where the model processes smaller sections of the images at a time. This significantly reduced the framebuffer usage, allowing for efficient batch processing and uninterrupted training sessions.
How Rafay Helps with GPU Framebuffer Monitoring¶
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 Framebuffer usage metrics for a Nvidia GPU.
Here is a video that showcases how an administrator can use the integrated GPU dashboards to aggregate, monitor and visualize GPU metrics. All the data they require is literally just a click away.
Conclusion¶
Both training and inference rely heavily on the GPU's ability to handle large datasets. The GPU's framebuffer is crucial for storing these datasets during processing. Optimizing framebuffer usage ensures maximum throughput and prevents performance degradation due to memory bottlenecks. Efficient framebuffer usage can also lead to reduced power consumption, improving cost-efficiency in large-scale GPU clusters, which is important in both training and inference phases.
In summary, GPU framebuffer is a critical GPU metric. It should be closely monitored especially for managing data storage during high-performance computations.
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