A GPU (graphics processing unit) is a specialized microprocessor which has been optimized to perform mathematical calculations and display graphics. It uses parallelism to achieve tremendous speeds over the limited functions that a GPU supports. The focus on fast, repetitive mathematical calculations makes their use applicable for many computing disciplines in addition to rendering graphics.
The power inherent in cloud GPU implementations is largely dependent on the quality and performance capabilities of the chips used to construct the system. Nvidia and Advanced Micro Devices (AMD) are currently the top GPU manufacturers.
Double-Digit GPU Growth Predicted
There are a number of industries and technologies that are driving the growth of the cloud GPU market. They need the power afforded by these specialized computing environments. Some examples include:
- Entertainment and gaming;
- 3D modeling of automotive parts;
- Artificial intelligence;
- Machine and deep learning applications;
- Displaying data generated by the Internet of Things (IoT);
- Augmented and virtual reality applications;
- Advanced imaging delivery.
Cloud service providers are offering GPU as a service to a wide variety of customers. The cloud GPU market is expected to enjoy double-digit growth for the next five years and to exceed five billion dollars by 2024.
Cloud GPU Offerings
Cloud providers are expanding the GPU options available to their customers in order to address the diversity of applications for which these services are being put to use. Here is a sample of some cutting-edge GPU cloud offerings.
Google has a varied portfolio of cloud GPU offerings for scientific computations, machine learning, and 3D visualization. They will tailor the environment to your specific computing needs by using the Nvidia GPUs best suited for the task. Virtual workstations that can run graphically intensive programs are available as are flexible GPU counts to power your applications sufficiently. Billing is on a per-second usage basis and Google GPUs are tightly integrated with the other facets of their cloud platform.
Amazon Elastic Container Service (ECS)
Amazon recently elevated its support for computationally intensive applications running on their EC2 GPU instances. You can flexibly assign specific numbers of GPUs to a container and ECS will perform pinning to enable workload isolation and provide optimal performance. The platform is built to maintain GPU resources for deep learning applications in the same way that memory or storage is handled.
Nvidia DeepStream and Microsoft Azure IoT Edge
This symbiosis of the DeepStream SDK for Nvidia GPUs with Azure IoT Edge allows the creation of complex systems for remote monitoring and management that deliver real-time insights. These systems can be used in large physical infrastructures and will be instrumental in moving toward smarter roads, cities, and buildings.
As the demand for high-quality imaging and intelligent applications continues to grow, so will the innovations that cloud providers offer their customers. GPUs will comprise the foundation upon which many new technologies will be built. Their availability to cloud clients is a prime example of the power of cloud computing.