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:

• Parallel Processing: GPUs can execute thousands of threads simultaneously, making them great for tasks that can be broken down into parallel workloads.
• High Bandwidth: GPUs have high memory bandwidth, which is beneficial for tasks that require high throughput.
• Accelerated Computing: By offloading compute-intensive tasks to the GPU, applications can run faster on the CPU.

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.