Summary

In this chapter, we started by understanding HPC fundamentals and its importance in processing massive amounts of data to gain meaningful insights. We then discussed the limitations of running HPC workloads on-premises, as different types of HPC applications will have different hardware and software requirements, which becomes time-consuming and costly to procure in-house. Moreover, it will hinder innovation as developers and engineers are limited to the availability of resources instead of the application requirements. Then, we talked about how having HPC workloads on the cloud can help in overcoming these limitations and foster collaboration across global teams, break barriers to innovation, improve architecture design, and optimize performance and cost. Cloud infrastructure has made the specialized hardware needed for HPC applications more accessible, which has led to innovation in this space across a wide range of industries. Therefore, in the last section, we discussed some emerging workloads in HPC, such as in life sciences and healthcare, supply chain optimization, and AVs, along with real-world examples.

In the next chapter, we will dive into data management and transfer, which is the first step to running HPC workloads on the cloud.