High-Performance Computing (HPC) is no longer a niche luxury. The market is expected to reach £28.3 billion by 2020, having grown 11 percent in recent years. And it’s not just scientific and academic institutions that are investing their money.
HPC’s mainstream adoption is revolutionising the way enterprises conduct business, especially in time-critical situations. As new technologies continue to emerge and develop, HPC remains the driving force behind competitive, data-driven industry.
Each cluster (collection of servers) consists of a select group of nodes, all communicating via a series of interconnects, which allows multiple processing cores to manage collaborative or independent workloads simultaneously.
Commodity cluster (standard servers with high-speed interconnects).
HPC cloud computing (compute cycles-as-a-service over the internet).
Grid computing (local HPC clusters connected on a national or international scale).
Who uses HPC?
HPC is used across a number of different industries and verticals, including engineering, medicine and financial services. In situations that require high levels of computation, HPC provides the capacity to out-compute and outcompete your rivals.
In recent years, the cost of HPC has reduced dramatically, with advances in commodity clusters minimising the price of hardware components. While dedicated supercomputers might still be the preserve of the super-rich, HPC is now a viable business option for enterprise-level companies.
Affordable HPC will open more avenues for enterprises and allow them to make use of cutting-edge technologies. This guide will look at the three main areas HPC is helping to improve:
The Internet of Things (IoT)
Big data & High Performance Data Analytics (HPDA)
Machine learning & AI
With these new approaches to HPC, competitive businesses can produce scalable, real-time models at minimal cost. Here’s what the future has in store.
A sixth sense: the Internet of Things
IoT isn’t a new concept, but with the democratisation of HPC, it’s set to take its place at the centre of the business stage.
By 2020, Gartner predicts there will be 20.8 billion smart devices in operation around the globe. To cope with the enormous influx of data from these sensors and appliances, ambitious companies are looking to more robust forms of CPUs.
Leading the way is the Graphical Processing Unit (GPU). Although these processors were traditionally designed for use in 3D modelling, they have been adapted to perform other mathematically-intensive tasks, due to their superior number of cores.
This makes them perfect for use in IoT workloads, where vast streams of structured and unstructured data are processed. Since they require less energy to run than the equivalent number of CPUs, they help reduce operational costs.
Industry Insights: Distribution and Customer Spending
HPC is already a big success in logistics, where sensors gather information about distribution performance. The industry demands a high quality of service, with time and cost the most important variables.
Through the use of parallel processing (the ability to distribute a workload across multiple cores), logistics companies are able to collate data such as vehicle faults and weather conditions to reduce idle time.
Similarly, HPC and IoT can be implemented in the banking sector to monitor customer spending habits. Online banking applications collect data over the internet and enable firms to store information on every aspect of a purchase, helping them understand trends in spending.
Companies need to process this data in real-time to provide actionable insight and, without HPC, workload bottlenecks can occur due to overloaded servers.
Analyse this: Big Data & HPDA
With so many connected devices, companies need a way to process petabytes of raw data. HPC is enabling early adopters to cut through the noise Big Data creates and identify those all-important trends and patterns. IDC predicts the global volume of data will hit 44 zettabytes by 2020, highlighting the need for powerful data analytics engines.
HPDA and the Cloud
High-Performance Data Analytics (HPDA) will be central to querying and classifying new forms of data. Companies can analyse data from IoT devices, CRM systems, stock markets and more with increased speed, performance and scalability.
To achieve this, HPDA uses grid computing to fuel parallel-processing and distributed data storage.
As more and more businesses adopt the cloud, grid computing is becoming virtual, allowing enterprises to run HPDA on-demand. Opting for a cycle-as-a-service platform means you only pay for the computing power you’re using at any given time.
Not only does this reduce CAPEX, it also enables you to scale instantly by building or tearing down virtual infrastructure. Sharing resources in this way makes it easier to store and analyse vast quantities of data that would otherwise require the configuration of expensive hardware.
Industry Insights: Fraud Detection and Personalised Healthcare
While HPDA is still in its infancy, it’s already making waves in the financial services industry. With tighter regulations placed on banks and brokerage firms, finding ways to detect and predict fraud is a crucial part of staying compliant.
Companies such as PayPal are using HPDA to analyse millions of transactions, considering variables such as location and number of transactions in a given timeframe to determine whether they’re fraudulent.
Another sector prospering from the use of HPDA is healthcare. Identifying specific compounds in streams of potential variations makes drug discovery simpler.
The combination of Big Data and HPC will allow pharmaceutical companies to administer a more personalised service, using simulated patients as a test-run for new forms of medicine.
Robo-FLOP: Machine Learning & AI
Of all the potential uses of HPC, machine learning and AI are the most fascinating. Cheaper, more robust processing chips have brought software-defined learning to the masses and turned it into an important tool for modern businesses.
In 2015, NVIDIA CEO, Jen Hsun Huang claimed ‘machine learning is high-performance computing’s first killer app for consumers.’ And, two years on, he’s been proved right.
Machine learning can help both your business and your customers improve the efficiency and performance of their daily operations. Again, GPUs are providing the power to run the complex algorithms needed to deal with the high-value FLOP (floating point operations per second) calculations that underpin intelligent neural networks.
It’s the high-speed interconnects between HPC nodes that enable machine learning to function.
Real-time analysis and understanding rely on faster throughput and data sharing capabilities. The best HPC clusters use networks such as 10 Gigabit Ethernet and InfiniBand to increase computation traffic between nodes.
With multiple cores working on the same calculation, it’s important that files and data are shared throughout the network as quickly and efficiently as possible.
Industry Insights: Stock Valuation and Mis-sold Insurance
92 percent of businesses believe machine learning will impact them in some capacity. For example, in the stock exchange, traders can use machine learning to forecast trends in stock prices.
HPC improves the results of these predictions by ensuring the calculations are run in near real-time.
Insurance will also benefit from deep learning algorithms. Regulatory compliance is at the forefront of industry processes.
As machine learning improves, so will a firm’s ability to understand interactions between sales agents and customers. This will give them more control over mis-selling and ensure negligent behaviour doesn’t undermine financial success.
Making HPC Work for Your Business
The beauty of HPC is its ability to adapt to a range of different industries and workloads. However, choosing the right datacentre architecture for your business enables you to get more from your HPC cluster.
Software-defined infrastructure has made it possible to tailor the number and function of cores available for certain tasks, increasing speed and performance, optimising network connectivity, and reducing the risk of potential bottlenecks.
When investing in HPC technology, a roadmap is key:
Shop around for the commodity components that best meet your specific requirements. While some will be incompatible, choosing resources from a variety of vendors will help you design bespoke infrastructure that maximises ROI.
Benchmark your applications. Test out actual HPC clusters before you commit to buying. Proof of concept (POC) is the only way you’ll know which configurations work best for your business.
Work with a partner, not a wholesaler. Purchasing HPC components can be daunting, so pick a company who will work with you to find the perfect software and hardware solutions for your needs.
Summary: an HPC Strategy
As IoT, HPDA and machine learning become more ubiquitous in the world of business, companies that embrace the power of HPC will see greater overall success.
Whether you decide to opt for on-premises commodity clusters or try your hand at HPC cloud computing, planning from the ground up is essential.
With the right HPC strategy, you have the opportunity to increase the scalability, performance and profitability of your business. To out-compute is to out-compete and, with RedPixie’s approach to HPC, increased innovation is within your reach.