Does Your Business Need High Performance Computing?
High performance computing (HPC) is a necessity for organizations that require deep insights into their data. Techopedia defines HPC as a methodology for solving computationally complex problems using either distributed processing or supercomputers. HPC forms the core of many research programs. The data that scientists need to compile and process are far more complex than data that exists outside of the field of research. To this end, the development of HPC systems has focused on Academia as the primary market for their development.
Business also has a need for HPC as more companies realize the kind of power that HPC systems offer them. One of the most interesting applications of HPC systems is alongside Big Data collections. The large volumes within data lakes usually need a lot of processing power to deal with. A business leveraging HPC systems could cut down the amount of time they need to generate actionable results from their data.
Typically, the use-cases of HPC involve some simulation. This usage stems from their use in research labs in the past. As more practical applications for simulation come to light, HPCs can be used to model things like airflow over a wing, or the growth of a financial portfolio. With the right data, an HPC can even allow a business, such as Smith’s Tree Removal, to view their ROI on Google Ads or the performance of a department. Many of the use-cases that make up the bulk of HPC systems today still are firmly within the field of research. However, the advances that universities have made with reducing the cost and increasing the efficiency and scalability of systems bodes well for a future in business.
No Need for Supercomputers
Because of the use of the technology in research labs, HPC usually required a supercomputer to be able to get its job done. The speed of the processor and amount of data it was dealing with, would be difficult, if not impossible, for standard systems to handle in a reasonable amount of time. Numerous specialized HPC systems have come from the big names in supercomputing, such as HPE/Cray, IBM, and Atos. However, distributed processing has made it a lot easier to utilize multiple processing units to split up the work of a single computer.
Distributed processing cores utilize a series of interlinked computers called nodes. Within every one of those nodes, there are multiple processors, each with multiple cores, further breaking up the processing job into more manageable pieces. These systems are termed “cluster-HPC systems” and are useful in providing a distributed methodology for processing data.
As most businesses that have access to a lot of different computers that all perform a separate function, having a distributed system allows larger problems to be broken up into smaller ones. The benefit of this approach is that in the case of a failure of one system, the others will take up the slack and keep processing going.
Adding Compute Resources through The Cloud
One of the current examples of cloud-HPC systems is the University of North Carolina at Chapel Hill (UNC-Chapel Hill). Initially, their HPC system contained clusters, as mentioned before, but as the demands for computing power grew, the group of processors was unable to keep pace with the needs of the university. To address that, the university initiated a system by which they would move their cluster system into the cloud.
The change from cluster-based to cloud-based systems serves as an ideal methodology for scaling computing systems since the system would be able to leverage increased processing power based on the needs of the job. Additionally, managing a cloud system is far less expensive than operating a series of computers in a cluster since multiple hardware failures can impact the cluster significantly. The ability of the cloud to adapt to evolving needs means that no matter what demands the business has, the HPC can change to compensate.
Cloud-HPC systems offer even more flexibility in business applications, enabling a company to have their HPC available to every one of their sub offices. This is critical for dealing with sensitive data. Traditionally, businesses dealing with this sort of data may need to develop a VPN guide for external contractors to ensure that the data doesn’t fall into the wrong hands when being passed from contractor to business, or within different sub-offices. HPC-cloud systems offer computing and storage on the same cloud location, allowing for business operations to remain flexible without impacting productivity.
Supercomputers Are Still a Viable Option
Both the cloud and cluster systems have serious drawbacks when it comes to operating as HPC systems. Clusters are prone to having hardware failures that may take time to track down and deal with. Additionally, because of the number of processors and other hardware used, it can be extremely inefficient with energy usage. The cloud is better for performance, but the information stored on a public cloud leads to concerns about the privacy and security of that data.
Supercomputers provide a useful method of doing HPC, but the amount of money a business needs to invest in installing and maintaining one leads to it often being overlooked. However, as the need for more powerful computers arises to deal with larger streams of data, businesses need to start facing the possibility that these supercomputers may be the best way to process data. According to Computer Weekly, HPE’s acquisition of supercomputer manufacturer Cray has shown that HPC is an important area to invest in when it comes to enterprise-level computing power.
Dealing with HPC Workloads
Workload managers ensure that scheduling and processing are completed, and regardless of the type of HPC system an enterprise decides to use, the workload manager is one of the most critical elements. Workload managers can be used to automate job scheduling and the production of results so that the processing power of the system is efficiently used. Choosing the right workload manager stems from what the organization wants to accomplish from its HPC systems. The best way to do so is to create a simple problem that can easily be defined with outputs that can be used as a test. See what software options are available for this particular case and then narrow down the list of potential software to test as a workload manager.