The use of a Graphics Processing Unit (GPU) together with a CPU to accelerate computation in applications.
GPU computing in cloud computing refers to the practice of using Graphics Processing Units (GPUs), in combination with traditional CPUs, to process data. GPUs, originally designed for rendering graphics in video games, are highly efficient at performing parallel operations, making them ideal for tasks such as machine learning, image processing, and scientific computation.
GPU computing works by offloading compute-intensive portions of the application to the GPU, while the remainder of the code still runs on the CPU. This is known as heterogeneous or hybrid computing. Key aspects of GPU computing include:
A bioinformatics company, BioInfo, is developing a machine learning model to analyze genomic data. The large size of the data and the complexity of the model make the computation highly intensive. By leveraging GPU computing in the cloud, BioInfo can process the data much faster than using CPUs alone. This accelerates their research and allows them to deliver results more quickly.
The practice of aggregating computing power in a way that delivers much higher performance than one could get out of a desktop computer.
The ability to quickly expand or decrease computer processing, memory, and storage resources to meet changing demands.