Efficiency Analysis for AI Applications in HPC Systems. Case Study: K-Means

Currently, many AI applications require large-scale computing and memory to solve problems. The combination of AI applications and HPC sometimes does not efficiently use the available resources. Furthermore, a lot of idle times is caused by communication, resulting in increased runtime. This paper describes a methodology for AI parallel applications that analyses the performance and efficiency of these applications running on HPC resources to make decisions and select the most appropriate system resources. We validate our proposal by analysing the efficiency of the K-Means application obtaining an efficiency of 99% on a target machine.