Computational resources are one of the key limiting factors for access to multiple rapidly developing sectors, including data analysis and AI training. No matter how advanced an algorithm is, it is limited by the ability of a computer to perform the necessary calculations. Both researchers and developers are often hindered by the lack of access to sufficiently robust equipment. HPC is meant to solve that, offering both an efficient parallel computational infrastructure, as well as a system for dynamically sharing and allocating resources for multiple tasks.

Purpose and Applications

The main purpose of HPC is to solve complex computational problems that standard computers cannot handle. This is especially important in the fast-growing field of artificial intelligence. To manage these powerful resources efficiently, we use SLURM (Simple Linux Utility for Resource Management). SLURM is an open-source job scheduler that allocates resources, manages queues, and optimizes system usage across the cluster. With SLURM, researchers can run demanding task like training large language models such as GPT-4 and Claude, which use billions of parameters. Speech recognition systems for real-time translation and virtual assistants also rely on HPC clusters, learning from thousands of hours of multilingual audio. It also supports advanced computer vision projects, including analyzing millions of medical images or autonomous driving sensor and camera data.