It wasn’t very long ago that high-performance computing (HPC) was almost always thought as a technology for the research institution, government sector, or maybe even for a hand-picked few largest of corporations. The ability of these super-complex and super-fast machines to process humongous sets of data was thought non-critical for enterprises. So HPC has always had the same effect on enterprises what a nerd has on a bunch of university kids.
However, with the convergence of technology and proliferation of data, it is imperative for businesses to extract insights and at a very fast pace from big data to be able to meet customer demands at near-real-time speed. What scientists and researchers were doing in the past to fast track their experiments and research analysis, businesses are doing today to improve their performance. All this makes HPC shift from being the elite nerd to the kid next door.
HPC has a growing appeal today and industry data backs this up as well. According to Hyperion Research, the HPC market for businesses is predicted to grow at a compound annual growth rate of 9.8% from 2017 to 2022. There are ample reasons that can be attributed to this compounded growth, but the most important one is the growing need for systems that can handle AI technologies such as deep learning. This according to market insights is making high-performance computing a must-have for the enterprise market.
While there is a growing demand for HPC, driven in large part by ever-increasing demands for more accurate and faster simulations, for greater insights into ever-larger datasets, and to meet new regulatory requirements, whether for increased safety or for reduced financial risk. There is a simultaneous windfall mentality driving the corporate leaders to play catch up. In today’s global environment, organizations need to be highly competitive to strive for better outcomes. Whether it is improved financial performance, shorter product development cycles, a better understanding of molecular-level interactions, or more efficient ways to simulate the behavior of materials at Nano-scale. There is no time to waste for the IT leaders; while the competition is out there discovering and implementing game-changing technology to further their business.
For IT and business leaders, it's time to get up to speed on HPC. At stake is the ability of their companies to make the right decisions: They must enable their organizations to grab hold of transformative insights while avoiding foolish forays into unexplored territory with uncertain returns.
For leaders who are just embarking on their journey to understanding and adapting to HPC; it is recommended that you look at the industries where HPC has already gained strong traction and explore the ways in which these industries have applied HPC to further their business objectives.
Use Cases Of HPC Across Industries:
Banking: Banks have used HPC for pricing exotic financial instruments, optimizing their portfolios of mortgage-backed securities and managing firm-wide global credit and debt risks. Fraud detection is an extremely important area for the financial industry. Millions of dollars could be saved if suspicious transactions are stopped before they occur. To do this, millions of transactions across the globe will have to be analyzed in real-time. The problem is that algorithms will need to find unknown patterns within that data, which could indicate a fraudulent transaction.
It requires not only analyzing millions of transaction, but also many other data sources that offer the right context for a certain transaction. For example, is a certain credit card transaction valid or not. That not only depends on the transaction itself but also for example on the location or the moment of the transaction.
One company that deals with these volumes are PayPal. On a daily basis, they deal with 10+ million logins, 13 million transactions and 300 variables that are calculated per event to find a potentially fraudulent transaction. Thanks to High-Performance Data Analytics, PayPal saved in their first year of production over $700 million in fraudulent transactions that they would not have detected previously.
Pharmaceuticals: Drug design and discovery is a tedious process. It can easily require several years or longer before a drug hits the market. This is due to rigorous testing in labs on animals and later on humans before it is made available to the masses.
High-Performance Computing in combination with Big Data enables the pharmaceutical industry to find, for example, the right proteins for a certain drug among millions of compounds. This can be done thanks to simulation analysis and test a plethora of varieties on thousands of different virtual patients. As a result, drug discovery can be reduced with multiple years, eventually saving a lot of lives.
Energy Sector: The energy sector has long been a user of the most powerful computers for seismic analysis in their search for accessible oil and gas deposits.
The massive data quantities required to gain an understanding of geologic formations far below the earth's surface led Devon Energy Corp. to move a seismic HPC application to the cloud to get faster results at a reasonable cost. At the same time, Devon is implementing deep learning to gain both higher speed and greater accuracy.
They took a new approach that parcels out work so that it can be performed simultaneously across many servers on Microsoft's Azure cloud. The new approach saves time and money. Instead of processing all the data one job at a time in sequence, it now has thousands of different jobs working on data at the same time.
Just like these three industries have utilized HPC to drive business results for themselves, there are use cases in other verticals and industries that are worth exploring.
Automobile: Running the kinds of robust, scalable simulations that enable more comprehensive system engineering requires greater computing power than most engineers have available at their workstations or on-premise clusters. Using high-powered computing software located in the cloud is emerging as an ideal solution. Three independent research teams recently conducted experiments to evaluate the efficacy of HPC in the cloud for conducting automotive design simulations.
- Vehicle Crash Using ANSYS LS-DYNA in the Opin Kerfi Cloud
- Modeling Airflow Through an Intake Manifold with ANSYS FLUENT on Microsoft Azure
- Airbag Simulation with ANSYS LS-DYNA on UberCloud and Microsoft Azure
Engineering Services: Increasingly, manufacturing companies implement engineering simulations, modeling, and 3D design workload capabilities on-premise. While some enterprise-level manufacturers tap the power of their large-scale HPC implementations, smaller HPC systems support commonly-used applications like Ansys Mechanical*, FLUENT*, LS-DYNA* or other open-source software. Others are turning to HPC Cloud providers such as Amazon Web Services* or Microsoft Azure* for help.
We at Locuz with years of domain expertise offer comprehensive solutions for High-Performance Computing based on loosely coupled clusters, SMP, accelerator-based systems, High-performance storage, application parallelization. We have designed domain-specific solutions considering the challenges and business requirements of respective industry verticals. In addition, our services can help organizations to optimize and overcome obstacles to parallelism by adopting revolutionary approaches to High-Performance Computing. We understand organizations need to be highly competitive to strive for better outcomes and therefore offer unique and comprehensive solutions around HPC that are backed on our decade long experience in Grid Computing, MPP's, SMP's, Enterprise storage and Parallel file systems and Virtualization.
Source Credit: Industry-specific use cases have been taken from research on HPC use cases available on various forumsRuna Tripathy August 05, 2019 0 2