What Is GPU Utilization?
GPU utilization refers to the percentage of a graphics card's processing power being used at a particular time. Graphics Processing Units (GPUs) are specialized hardware components engineered to manage complex mathematical calculations necessary for rendering graphics and executing parallel computing tasks. Recently, GPUs have gained popularity for their ability to accelerate machine learning and deep learning processes.
This is part of a series of articles about Multi GPU.
Why Is Monitoring GPU Utilization Important?
Keeping track of GPU utilization is essential for several reasons:
Improved Resource Allocation
Graphics cards, like NVIDIA's Tesla series or AMD Radeon Instinct, are specifically designed to tackle computationally demanding tasks, such as deep learning algorithms. However, these GPUs can be costly investments for organizations. Monitoring their utilization enables data scientists and machine learning engineers to identify underused resources and reallocate workloads more effectively across available hardware.
One crucial aspect of optimizing deep learning models is tweaking parameters like batch sizes, which directly influence training duration and memory usage. Tracking GPU memory usage helps determine if a model needs smaller batch sizes or if it can take advantage of larger ones without triggering out-of-memory errors.
Saving Costs in Cloud Environments
In cloud-based environments where users are billed for compute resources by the hour or minute (e.g., AWS EC2 instances), monitoring GPU usage becomes even more vital. Ensuring that your organization only pays for what it needs means reducing idle times while maximizing throughput during active periods.
Preventing Bottlenecks and Enhancing Workflows
Keeping an eye on GPU usage can help identify data pipeline bottlenecks, such as slow I/O activities or insufficient CPU resources. Addressing these issues can substantially boost overall performance and efficiency.
It also allows teams to optimize workflows by pinpointing tasks that are better suited for GPUs and tasks that should be assigned to CPUs or other specialized hardware accelerators.
Related content: Read our guide to GPU scheduling
Reasons for Low GPU Utilization
Low GPU utilization can occur due to a number of factors. Here are some common reasons:
- CPU bottleneck: The CPU may not be able to supply data fast enough to the GPU, causing the GPU to idle while it waits for data. This is one of the most common causes of low GPU utilization. Optimizing CPU code and using asynchronous data transfers can help to mitigate this.
- Memory bottleneck: If your application requires a large amount of memory bandwidth, the GPU may spend a lot of time waiting for data to be transferred to or from memory. You can try to optimize memory access patterns to reduce this bottleneck.
- Inefficient parallelization: GPUs work best when they can execute many threads in parallel. If your application is not properly parallelized, or if the workload cannot be evenly distributed across all the GPU cores, this could lead to low GPU utilization.
- Low compute intensity: Some tasks may not be very computationally intensive, and may not fully utilize the GPU's processing power. If the task involves a lot of conditional logic or other operations that are not well-suited to parallel processing, the GPU may not be fully utilized.
- Use of single precision vs. double precision: GPUs often have different performance characteristics for single-precision and double-precision calculations. If your code uses double-precision calculations, but the GPU is optimized for single-precision, this could lead to lower utilization.
- Synchronization and blocking operations: Certain operations can block the GPU and cause it to idle. This includes explicit synchronization operations, as well as operations like memory allocation or certain types of memory transfer.
Investigating these factors can help you identify why your GPU utilization is low, and can guide you in optimizing your code and system setup to improve utilization.
Monitoring and Improving GPU Utilization for Deep Learning
Effectively monitoring and controlling GPU utilization is essential for deep learning applications, as it significantly influences model performance.
Various tools and techniques can assist you in monitoring GPU usage, optimizing resource distribution, and ultimately reducing training times. For example, NVIDIA System Management Interface (nvidia-smi), a command-line utility included with NVIDIA graphics card drivers, offers real-time data on multiple GPU aspects, such as temperature, power usage, memory consumption, and more.
Nvidia-smi and similar tools allow users to efficiently monitor GPU resources while executing deep learning tasks:
- Adjusting batch sizes: One method to boost GPU utilization is by modifying the batch size during model training. Larger batch sizes may increase memory consumption but can also improve overall throughput. Testing various batch sizes can help find the ideal balance between memory usage and performance.
- Mixed precision training: Another strategy for enhancing GPU efficiency is mixed precision training, which uses lower-precision data types like float16 instead of float32 when performing calculations on Tensor Cores. This method decreases both computation time and memory demands without compromising accuracy.
- Distributed training: Spreading your workload over multiple GPUs or even multiple nodes can further improve resource usage by parallelizing computations. Frameworks such as TensorFlow's MirroredStrategy or PyTorch's DistributedDataParallel simplify the implementation of distributed training approaches in your projects.
Besides these techniques, specialized solutions like Run:ai can aid in automating resource management and optimizing GPU usage across your entire infrastructure.
GPU Utilization with Run:ai
The Run:ai platform allows you to utilize your GPU compute, so no compute is left idle. The easy to navigate dashboard gives you the ability you to set policies and rules, and schedule, allocate, and fraction the compute you’re already using, optimizing your resources, and saving the need to purchase more GPUs to run and train your AI models.