Graphics processing units (GPUs), originally developed for accelerating graphics processing, can dramatically speed up computational processes for deep learning. They are an essential part of a modern artificial intelligence infrastructure, and new GPUs have been developed and optimized specifically for deep learning.
Read on to understand the benefits of GPUs for deep learning projects, the difference between consumer-grade GPUs, data center GPUs and GPU servers, and several ways you can evaluate your GPU performance.
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Graphics processing units (GPUs) are specialized processing cores that you can use to speed computational processes. These cores were initially designed to process images and visual data. However, GPUs are now being adopted to enhance other computational processes, such as deep learning. This is because GPUs can be effectively used in parallel for massive distributed computational processes.
Modes of parallelism
The primary benefit of GPUs is parallelism or simultaneous processing of parts of a whole. There are four architectures used for parallel processing implementations, including:
Most CPUs are multi-core processors, operating with an MIMD architecture. In contrast, GPUs use a SIMD architecture. This difference makes GPUs well-suited to deep learning processes which require the same process to be performed for numerous data items.
General purpose GPU programming
Related to GPUs’ original purpose, these processors previously required users to understand specialized languages, like OpenGL. These languages were used only for GPUs, making them impractical to learn and creating a barrier to use.
In 2007, with the launch of the NVIDIA CUDA framework, this barrier was broken, providing wider access to GPU resources. CUDA is based on C and provides an API that developers can use to apply GPU processing to machine learning tasks.
How modern deep learning frameworks use GPUs
Once NVIDIA introduced CUDA, several deep learning frameworks were developed, such as Pytorch and TensorFlow. These frameworks abstract the complexities of programming directly with CUDA and have made GPU processing accessible to modern deep learning implementations.
Learn to use GPUs in popular deep learning frameworks, in our guides about PyTorch GPU and TensorFlow GPU.
GPUs can perform multiple, simultaneous computations. This enables the distribution of training processes and can significantly speed machine learning operations. With GPUs, you can accumulate many cores that use fewer resources without sacrificing efficiency or power.
When designing your deep learning architecture, your decision to include GPUs relies on several factors:
When incorporating GPUs into your deep learning implementations, there are a variety of options, although NVIDIA dominates the market. Within these options, you can choose from consumer-grade GPUs, data center GPUs, and managed workstations.
Consumer GPUs are not appropriate for large-scale deep learning projects, but can offer an entry point for implementations. These GPUs enable you to supplement existing systems cheaply and can be useful for model building or low-level testing.
Data center GPUs are the standard for production deep learning implementations. These GPUs are designed for large-scale projects and can provide enterprise-grade performance.
NVIDIA DGX servers are enterprise-grade, full-stack solutions. These systems are designed specifically for machine learning and deep learning operations. Systems are plug-n-play, and you can deploy on bare metal servers or in containers.
Learn more in our guide to NVIDIA deep learning GPU, which explains how to choose the right GPU for your deep learning projects.
GPUs are expensive resources that you need to optimize for a sustainable ROI. However, many deep learning projects utilize only 10-30% of their GPU resources, often due to inefficient allocation. To ensure that you are using your GPU investments efficiently, you should monitor and apply the following metrics.
GPU utilization metrics measure the percentage of time your GPU kernels are running (i.e. your GPU utilization). You can use these metrics to determine your GPU capacity requirements and identify bottlenecks in your pipelines. You can access this metric with NVIDIA’s system management interface (NVIDIA-smi).
If you find that you are underusing resources, you may be able to distribute processes more effectively. In contrast, maximum utilization means you may benefit from adding GPUs to your operations.
GPU memory access and usage
GPU memory access and usage metrics measure the percentage of time that a GPU’s memory controller is in use. This includes both read and write operations. You can use these metrics to optimize the batch size for your training and gauge the efficiency of your deep learning program. You can access a comprehensive list of memory metrics through the NVIDIA-smi.
Power usage and temperatures
Power usage and temperature metrics enable you to measure how hard your system is working and can help you predict and control power consumption. These metrics are typically measured at the power supply unit and include resources used by compute and memory units, and cooling elements. These metrics are important because excessive temperatures can cause thermal throttling, which slows compute processes, or damage hardware.
Time to solution
Time to solution is a holistic metric that lets you define a desired accuracy level, and see how long it takes you to train your model to reach that level of accuracy. That training time will be different for different GPUs, depending on the model, distribution strategy and dataset you are running. Once you choose a GPU setup, you can use a time to solution measurement to tune batch sizes or leverage mixed-precision optimization, to improve performance.
Run:AI automates resource management and workload orchestration for machine learning infrastructure. With Run:AI, you can automatically run as many compute intensive experiments as needed.
Here are some of the capabilities you gain when using Run:AI:
Run:AI accelerates deep learning on GPU by, helping data scientists optimize expensive compute resources and improve the quality of their models.
Learn more about the Run:AI GPU virtualization platform.
There’s a lot more to learn about deep learning GPUs. To continue your research, take a look at the rest of our guides on this topic.
GPUs can save time and costs when implementing deep learning infrastructure. Learn how to assess GPUs to determine which is the best GPU for your deep learning model. Discover types of consumer and data center deep learning GPUs.
Get started with PyTorch for GPUs – learn how PyTorch supports NVIDIA’s CUDA standard, and get quick technical instructions for using PyTorch with CUDA.
Read more: PyTorch GPU: Working with CUDA in PyTorch
Learn what is the NVIDIA deep learning SDK, what are the top NVIDIA GPUs for deep learning, and what best practices you should adopt when using NVIDIA GPUs.
Field-programmable gate array (FPGA) chips enable you to reprogram logic gates. FPGA for deep learning implementations provide capabilities for optimizing throughput and adapting GPU resources to meet architecture needs. This article explains the difference between FPGAs and GPUs, and how to leverage FPGA for deep learning. Including pros and cons of FPGA technology.
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