HPC applications are fundamentally different from most enterprise applications in their architecture, scale, and complexity
As the use of big data analytics is expanding across industries and the use cases become clearer, enterprises are either considering or adopting it. Big data analytics generally involves examining large datasets generated through several extraction and collection methods – from eCommerce websites and mobile devices to social media and internet of things (IoT) devices. However, while big data analytics offers a solution and aids in analysing humongous quantities of data, implementing it on a large scale requires new technologies to work with the data involved.
Current Challenges and Solution
According to a BI analytics firm Sigma Computing report, 63 per cent of employees report they could not gather insights in the required timeframe. Most companies only analyse 12 per cent of the data they have, and 73.4 per cent report business adoption of significant data initiatives as a top challenge. Many companies with high compute requirements ran and still run their workloads on on-premises data centres. High data processing power is crucial for converting data into insights for decision-makers.
But creating a high performance computing (HPC) capacity in-house can be a slow process, requiring highly specialised workers and unexpected costs. Thanks to advancements in interconnects, storage, data transport, and other components essential for HPC, a motivation for moving towards cloud technologies has increased over the past few years
Today, several big cloud solution providers namely IBM, Amazon Web Services, Microsoft Azure, Hewlett Packard Enterprise (HPE), and Google Cloud Platform, and provide access to supercomputing infrastructure, built-in data centres, usually in the form of a collection of interconnected servers that work together in parallel to solve problems. HPC-as-a-service or HPCaaS is a new cloud service category that provides the hardware, software, and expertise, an all-in-one solution to process workloads, including big data analytics.
HPC applications are fundamentally different from most enterprise applications in their architecture, scale, and complexity. A single HPC application spans from hundreds to thousands of processor cores in many cases. But now, the value-capture potential from cloud-based HPC strategies may outpace traditional HPC value-capture models present. HPC service providers have opened up their market further by targeting low-end buyers who previously could not afford the capital expenditure of traditional HPC. The Allied Market Research says that the HPC-as-a-service market is expected to generate as much as $17 billion in revenues by 2026, growing at a compound annual growth rate of 13.3 per cent from 2019 to 2026.
HPC finds its use case widely today, a recent example being German smoke and fire damage simulation company Hhpberlin, which adopted HPC-as-a-service as it outgrew their in-house large-scale computing resources.
Potential Benefits And Hazards
HPC-as-a-service offerings come with several added benefits and advantages. Many services currently boast capabilities that are impossible to replicate on-premises, such as dynamic or highly automated management of HPC specialised compute and storage environments.
HPC-as-a-service offerings also tend to integrate and adapt to newer technologies faster, as its cloud infrastructure upgrade cycle is two times faster than the fastest on-premises HPC upgrade cycle. Hewlett Packard Enterprise’s GreenLake offering provides a pay-per-use model for IT operators, with numerous options to either upgrade or scale on-demand. HPE manages the created infrastructure, but companies can locate HPC servers at their data centre. Meanwhile, Lenovo’s recently announced TrueScale lets companies leverage internal infrastructure to tap into existing resources and allow HPC schedulers to access and provide resources as needed.
Gartner reports a fundamental shift in the present market, observing that more than 97 per cent of HPC engagements now are devising strategies to leverage hybrid cloud, HPC on-premises plus cloud and increasingly to develop cloud-native HPC capabilities. Nevertheless, such developments do not suggest that the evolving HPC-as-a-service model is flawless.
Having big data analytics workloads run in the cloud and on-premises can pose security challenges. The cost of licencing HPC in the cloud can also vary depending on the type of instances requested and being used. The high volume of data used in HPC cannot be moved around easily within the cloud.
HPC-as-a-service is currently in its relatively early stages and might face resistance from risk-averse organisations concerned about placing any of their proprietary, sensitive, or private data on the cloud. However, continued technological innovations, such as a hybrid model that allows reaping the essential benefits of the cloud without sacrificing privacy or security, can be the future for HPC.
HPC-as-a-service can provide faster data processing with high accuracy, and due to its low investment costs, it is emerging as an alternative to on-premises clusters for HPC. However, despite all the advantages of adopting HPC-as-a-service, there are still specific perceived barriers preventing enterprises from realising their true potential.
Organisations leaning on HPC-as-a-service to grow their business and accelerate product and service development will need to be constantly educated on its benefits, which may help break down the common roadblocks. The current work suggests that there is definitely substantial headroom for growth.
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