GPU Server

GPU computing refers to the use of graphics processing units (GPUs) for tasks that go beyond traditional graphics processing, such as complex calculations and data processing tasks. Originally developed for the acceleration of graphics displays in games and applications, GPUs have proven to be extremely powerful when handling parallel processes.

Unlike the CPU (Central Processing Unit), which is optimized for general purposes and processes a large number of tasks serially, GPUs are designed to perform many simple calculations simultaneously. A GPU consists of thousands of smaller cores that work together to process massive amounts of data in parallel. This architecture makes them particularly efficient for computationally intensive applications where large amounts of data need to be processed simultaneously.

This is particularly beneficial in areas such as scientific simulations where precise and extensive calculations are required. Artificial intelligence and machine learning also benefit enormously from GPU computing, as the training processes for neural networks that run through huge amounts of data are significantly accelerated. In data analysis, GPU computing also enables faster processing and evaluation of big data, which is essential for real-time analysis and time-critical decisions.

By utilizing the parallel processing capabilities of GPUs, companies and research institutions can process large amounts of data much faster and solve complex problems in less time. This not only leads to more efficient workflows, but also to faster innovation cycles and better results in a variety of application areas.

Agenda:

• NVIDIA Datacenter Solutions - Overview and Vision
• GPU accelerated desktop solutions: Win10 as a driver for GPUs in the data center
• Customer examples
• Artificial intelligence: Definition
• Areas of application for artificial intelligence
• Customer examples
• First steps in the field of AI