GPUs are blazingly fast, but many teams struggle to keep them running at peak performance. A recent poll on AI infrastructure shows that maximizing GPU use is a top priority, and data from Weights & Biases reveals that roughly a third of GPUs are at less than 15% usage, which is low.
The good news is that there are steps you can take to boost GPU utilization when training machine learning models.
Keep reading to explore the problem of GPU utilization and get best practices for improving GPU utilization across your ML processes, including data preprocessing in machine learning.
What is GPU Utilization?
GPU utilization refers to the proportion of GPU processing power used at a specific time. GPUs are costly resources so improving their use and avoiding idle time is critical for AI systems.
How to Measure GPU Utilization
Since GPUs are very costly and energy-intensive resources, ensuring that they’re fully utilized is critical for cost-effectiveness. Teams need to track specific metrics to gain insights that will allow them to redistribute workloads across numerous GPUs, discover idle GPUs, and scale resources for diverse applications, reducing the total infrastructure costs.
The following metrics play a key role in this process:
- Compute Utilization – This metric measures how much of the GPU’s computational capability is being used. High usage indicates the resources are being used to its maximum potential, while low utilization may imply wasted capacity.
- Memory Utilization – Monitoring memory use ensures that workloads use GPU memory effectively, eliminating memory bottlenecks or underutilization.
- Memory Copy Utilization – This metric highlights if data transfer between CPU and GPU is becoming a bottleneck, suggesting better data movement strategies are needed.
Why GPU Utilization Matters in Model Training
Maximum GPU usage is critical for numerous reasons:
- Cost Optimization – Since GPUs are expensive, teams need monitoring tools to discover underutilized resources and guarantee the platform’s efficient operation.
- Cloud Training Efficiency – Cloud-based ML model training has become the standard, with computational resources priced by the hour or minute. To ensure that expensive cloud resources are used efficiently, teams must do their best to reduce idle times for GPU-powered virtual machines.
- ROI for AI Infrastructure – ML models often require several tests before teams can discover the right parameters for producing commercial results. Optimizing GPU use is critical for generating higher returns on expensive infrastructure investments.
- Sustainability – Idle GPUs use massive amounts of electricity and create unnecessary carbon, while AI commercial ventures and research projects languish. Maximizing GPU use aids sustainability initiatives.
Benefits of Monitoring GPU Utilization
| Benefit | Definition |
|---|---|
| Improved Resource Allocation | Monitoring GPU usage allows data scientists and machine learning engineers to find underutilized resources and more efficiently reallocate workloads among available hardware. |
| Increased Performance | Tracking GPU memory utilization helps teams evaluate whether a model requires lower batch sizes or can handle bigger ones without causing out-of-memory issues. |
| Cost Savings | Ensuring that your company only pays for what it requires entails minimizing idle time while increasing throughput during active periods. |
| Fewer Bottlenecks And Improved Workflows | Monitoring GPU utilization can help teams discover data pipeline bottlenecks, such as delayed I/O activities or inadequate CPU resources. |
Improved Resource Allocation
Graphics cards like NVIDIA’s Tesla series and AMD Radeon Instinct are intended to handle computationally intensive activities like deep learning algorithms. However, they come with a hefty price tag. Monitoring GPU usage allows data scientists and machine learning engineers to find underutilized resources and reallocate workloads among available hardware more efficiently.
Increased Performance
A critical part of improving deep learning models is adjusting parameters such as batch sizes, which have a direct impact on training time and memory utilization. Tracking GPU memory utilization helps teams evaluate whether a model requires lower batch sizes or can handle bigger ones without causing out-of-memory issues.
Cost Savings
Monitoring GPU utilization is especially important in cloud-based setups where users are billed for computing resources by the hour or minute (for example, Amazon EC2 instances). Ensuring that your company only pays for what it requires entails minimizing idle time while increasing throughput during active periods.
Fewer Bottlenecks And Improved Workflows
Monitoring GPU utilization can help teams discover data pipeline bottlenecks, such as delayed I/O activities or inadequate CPU resources. Addressing these issues can significantly improve overall performance and efficiency.
This also opens the door to improving workflows by identifying which jobs are best suited for GPUs and which should be assigned to CPUs or other specialized hardware accelerators.
Common Causes of Low GPU Utilization
Low GPU usage might arise owing to a variety of reasons:
CPU Bottlenecks
The CPU may be unable to deliver data quickly enough to the GPU, leading the GPU to idle while waiting for data. This is one of the leading reasons behind poor GPU utilization. Optimizing CPU code and using asynchronous data transfers can help remediate this issue.
Slow Data Access
As the quantity of training data grows, the time spent accessing it starts to affect workload performance and GPU usage.
At the start of each training cycle, the training framework’s data loader (such as the PyTorch data loader or Ray Data) loads datasets from object storage. The datasets are then transported to the GPU instance’s local storage and processed in GPU memory.
If data cannot find its way to the GPU quickly enough to keep up with its computations, GPU cycles are lost (data stall or I/O stall). Research from IBM and other companies confirms the impact of data loading on overall training time and performance.
Slow data access can stem from issues like:
- Object storage failing to provide adequate throughput to completely overload GPUs.
- The “many small files” issue
- Data replication overhead
- GPU clusters being positioned far from where data is stored:
Memory Bottlenecks
If your application demands a high memory bandwidth, the GPU may spend significant time waiting for data to be moved to or from memory. To decrease this limitation, try optimizing memory access patterns and optimize your use of compute resources.
Inefficient Parallelization
Compute resources like GPUs perform best when they can run several threads simultaneously. If your application is not adequately parallelized or the workload cannot be uniformly divided over all GPU cores, it may result in low GPU usage.
Low Compute Intensity
Some jobs may not be computationally intense, and so may not fully utilize the GPU’s processing capacity. If the work requires a large amount of conditional logic or other activities unsuited to parallel computing, the GPU may not be effectively exploited.
Single vs Double Precision
GPUs frequently have differing performance characteristics for single and double-precision calculations. If your code performs double-precision computations when the GPU is optimized for single precision, utilization may decrease.
Synchronization And Blocking Activities Cuasing Idle GPUs
Explicit synchronization actions, memory allocation, and some forms of memory transfer may cause GPUs to be idle.
Investigating these issues can help you understand the reason behind poor GPU utilization and guide you in fixing your code and system configuration to change that.
How to Monitor and Increase GPU Utilization for Model Training
Monitor GPU Utilization
Deep learning applications require effective monitoring and management of GPU usage – otherwise, improving GPU utilization is just impossible.
There are various tools and strategies you can use to monitor GPU utilization, optimize resource allocation, and reduce training time.
One example is the NVIDIA System Management Interface (Nvidia), a command-line application that comes with NVIDIA graphics card drivers and provides real-time statistics on various GPU features, including temperature, power consumption, memory utilization, and more.
Increase GPU Utilization
Here are a few tactics for teams looking to boost GPU utilization:
| Tactic | Description |
|---|---|
| Adjusting batch sizes | One way to increase GPU utilization is to change the batch size during model training. Larger batch sizes may increase memory usage while improving total performance. Testing different batch sizes is the best way to discovering the optimal mix of memory utilization and speed. |
| Mixed precision training | This approach to increasing GPU performance employs lower-precision data types such as float16 instead of float32 while conducting operations on Tensor Cores. It reduces computing time and memory needs while maintaining accuracy. |
| Distributed training | Distributing your task over numerous GPUs or even nodes might enhance resource utilization by parallelizing calculations. Frameworks like TensorFlow’s MirroredStrategy and PyTorch’s DistributedDataParallel make it easier to include distributed training methodologies in your projects. |
How to Optimize GPU Utilization with lakeFS
Getting the correct data fast enough to GPUs is key for maximizing utilization.
lakeFS is an open-source solution for versioning data that brings various benefits to teams working on machine learning models.
For example, using lakeFS Mount you can easily mount the right version of data to GPU machines to streamline and optimize the process.
lakeFS Mount mounts a lakeFS repository, or a location within a repository as a file system on your local workstation, allowing you to access data in the object store as if it were part of your local file system. This configuration will enable you to interact with lakeFS-versioned datasets as if they were local but with some significant variations in managing the data.
Currently, lakeFS Mount is read-only, which means you may browse and read data but not make changes. The data remains in the object store, and lakeFS retrieves it efficiently via a two-step approach that includes metadata fetching and local caching.
Working with huge datasets locally gives you a lot more control over your operations and workflows. In addition to making large datasets accessible locally, lakeFS mount comes with the following benefits:
- Git integration – When you mount a route in a Git repository, the data version is immediately tracked and linked to your code. When testing earlier code versions, you receive the equivalent data version, which prevents local-only successes.
- Speed – Performance is ensured because lakeFS gets commit metadata into a local cache in sub-milliseconds, allowing you to start working immediately without waiting for massive datasets to download.
- Intelligent approach – lakeFS Mount makes effective use of cache by precisely anticipating which things will be accessed. This provides granular pre-fetching of metadata and data files before processing begins.
- Consistent data versions – Working locally increases the risk of employing out-of-date or erroneous data versions for machine learning data preparation. Mount allows you to deal with consistent, immutable versions, so you always know which data version you’re using.
Conclusion
When using GPUs for model training or inference, maximizing performance per dollar spent just makes sense. Understanding your level of GPU usage is critical for this, as higher GPU utilization implies fewer GPUs are required to service high-traffic applications.
Data access speed and reliability are key to boosting GPU utilization, and lakeFS is a good solution for addressing this problem.
