The term high performance computing is used both broadly and specifically. It can simply refer to methods that have been used to draw on computing power more innovatively in order to meet the sophisticated needs of business, engineering, science, healthcare, and other areas. In that form, HPC involves gathering large volumes of computing resources and providing them in a manner that is a significant improvement over the speed and reliability of a desktop computer. High-performance computing has historically been a specialty within computer science that is dedicated to the field of supercomputers – a major subfield of which is parallel processing algorithms (to allow various processors to handle segments of the work) – although supercomputers have stricter parameters, as indicated below.
Within any context, HPC is understood to involve the use of parallel processing algorithms to allow for better speed, reliability, and efficiency. While HPC has sometimes been used as an interchangeable term to supercomputing, a true supercomputer is operating at close to the top possible rate for the current standards, while high performance computing is not so rigidly delineated. HPC is generally used in the context of systems that achieve 1012 floating-point operations per second – i.e. greater than a teraflop. Supercomputers are moving at another tier, Autobahn pace – sometimes exceeding 1015 floating-point operations per second – i.e. more than a petaflop.
Through a virtual high performance computing (vHPC) system, whether in-house or through public cloud, you get the advantage of one software stack and operating system for the system – which comes with distribution benefits (performance, redundancy, security, etc.). You can share resources through vHPC environments. It enables a setting in which researchers and others can bring their own software to a project since computer resources are sharable. You can give individual professionals their own segments of an HPC ecosystem for their specific data correlation, development, and test purposes. Workload settings, specialized research software, and individually optimized operating systems are all possible. You are able to store images to an archive and test against them.
Virtualization of HPC makes it much more user-friendly on a case-by-case basis: anyone who wants high performance computing for a project specifies the core programs they need, number of virtual machines, and all other parameters, through an architecture that you have already vetted. By choosing flexibility in what you offer, you can enforce your internal data policies. Data security is improved. Also, by using this avenue, you are are able to keep your data out of silos.
2018 reports: HPC growing in cloud
Hyperion Research and Intersect360, both industry research firms, revealed in 2018 that an inflection point was reached within the market, per cloud thought-leader David Linthicum. In other words, we are at a moment when the graph is going to look much more impressive for the field. It already is impressive, though: organizations are rushing to this technology. There was 44% market growth in high performance cloud between 2016 and 2017 as it expanded to $1.1 billion. Meanwhile, the rest of the HPC industry, generally onsite physical servers, did not grow at even close to that pace over the same period.
Why is HPC being used increasingly? There are certain projects that especially need a network with ultra-low latency and ultra-high bandwidth, allowing you to integrate various clusters and nodes and optimize efficiency for the speed of HPC. The simple reason that the market for high performance computing has been increasing is speed. In order to target complex scenarios, HPC unifies and coordinates electronic, operating systems, applications, algorithms, computer architecture, and similar components.
These setups are necessary in order to conduct work as effectively and fast as possible within various specialties; with applications such as climate models, automated electronic design, geographical data systems, gas and oil industry models, biosciences datasets, and media and entertainment analyses. Finance is another environment in which HPC is highly demanded.
Why HPC is headed to cloud
A couple key reasons that cloud is being used for HPC are the following:
- cloud platform features – The capabilities of cloud platforms are becoming increasingly important since people looking for HPC are now as interested in the features as they are in the performance. The features that are available in the cloud make the infrastructure of the cloud more compelling, essentially, since that’s where they are available.
- aging onsite hardware – Cloud is becoming standard for HPC in part because more money and effort is being invested in keeping cloud systems cutting-edge. The majority of onsite HPC hardware is simply not as strong as what you can get in a public cloud setting. That is the case in part because IT budget limitations have made it impossible for companies to keep HPC equipment up-to-date. Cloud is much more affordable than maintaining your own system. Since cloud is budget-friendly, that means it is getting more business and is, in turn, able to keep refitting and upgrading its systems.
HPC powering AI, and vice versa
The fact is that enterprises are incorporating HPC now much more fervently than in the past (when it was primarily used in research), as noted by Lenovo worldwide AI chief Bhushan Desam. Broader growth is due to AI applications. Actually these two technologies are working synergistically: AI is fueling the growth of HPC, but HPC is also boosting access to AI capabilities. It is possible to figure out what data means and act in just a few hours rather than a week because of HPC components such as graphic processing units (GPUs) and InifiniBand high-speed networks. Since HPC divides massive tasks into tiny pieces and analyzes in that piecemeal manner, it is a perfect fit for the complexities of AI required by finance, healthcare, and other sectors.
An example benefit of HPC is boosting operational efficiency and optimizing uptime through engineering simulations and forecasting within manufacturing. In order for doctors to achieve diagnoses faster and more effectively, doctors benefit from running millions of images through AI algorithms, powered by HPC.
Autonomous driving fueled by HPC AI
To dig further within AI across industry, high performance computing is being used to research and develop self-driving vehicles, allowing them to move around on their own and create maps of their surroundings. Vehicles could be used within industry to perform dirty and dangerous tasks, freeing people to perform safer and more valuable jobs.
OTTO Motors is an Ontario-based autonomous vehicle manufacturer that has clients within ecommerce, automotive, healthcare, and aerospace. In order to get these vehicles up to speed prior to being launched in the wild, the firm runs simulations that require petabyes of data. High performance computing is used in that preliminary testing phase, as all the kinks are fixed. It is then used within the AI of the vehicles as they continue to operate post-deployment. “Having reliable compute infrastructure [in the form of HPC] is critical for this,” said OTTO CTO Ryan Gariepy.
Robust infrastructure to back HPC
High performance computing allows for the faster completion of projects via parallel processing through clusters – increasingly virtualized and run within public cloud. A core piece of moving a workload to cloud is choosing the right cloud platform provider. At Total Server Solutions, our infrastructure is so comprehensive and robust that many other top tier providers rely on our network to keep them up and running. See our high-performance cloud.