This topic describes the typical scenarios of Elastic High Performance Computing (E-HPC) clusters. You can select resource types based on different scenarios.
You can use E-HPC in combination with numerical forecast models to calculate and analyze meteorological and environmental data. This way, you can forecast weather conditions and environmental changes.
E-HPC can be used in the exploration industry to analyze the related data, and simulate the geological structure of the survey area. Then, you can use this data to accurately locate specific energy resources.
Bioinformatics: You can use E-HPC to sequence a large number of biological genomes and perform the related tasks. Then, you can obtain the information of genomes and the result of data analysis to address the challenges in biology and medicine.
Dynamics simulation: You can use E-HPC to simulate large-scale molecular dynamics. The simulation can be used to analyze and predict the interactions between protein and lipids and to track the changes of the two substances.
Drug development: E-HPC can be used by drug developers to perform concurrent high-throughput screening on a large number of small molecule libraries.
Scientific research and education
E-HPC can provide supercomputing services for governments, universities, and supercomputing centers. These can be used to support numerical simulation and simulation verification during scientific researches. This way, education experts and scientific researchers no longer waste time and resources to learn about processors or supercomputers. In this case, these experts and researches can focus on research.
In manufacturing industry, you can use E-HPC clusters to analyze complex engineering structures and mechanical structures. You can also simulate and optimize product structure and performance based on a large amount of data. E-HPC applies to multiple industries. This includes manufacturing simulation such as intelligent automotive, aerospace, mechanics, and construction.
E-HPC supports concurrent, large-scale computing tasks that run on multiple clusters. One mainstream example is the graphics rendering that is required in industries, such as film, television, and animation.