Limitations of on-premises HPC
HPC applications are often based on complex models trained on a large amount of data, which require high-performing hardware such as Graphical Processing Units (GPUs) and software for distributing the workload among different machines. Some applications may need parallel processing while others may require low-latency and high-throughput networking. Similarly, applications such as gaming and video analysis may need performance acceleration using a fast input or output subsystem and GPUs. Catering to all of the different types of HPC applications on-premises might be daunting in terms of cost and maintenance.
Some of the well-known challenges include, but are not limited to, the following:
- High upfront capital investment
- Long procurement cycles
- Maintaining the infrastructure over its life cycle
- Technology refreshes
- Forecasting the annual budget and capacity requirement
Due to the above-mentioned constraints, planning for an HPC system can be a grueling process, Return On Investment (ROI) for which might be difficult to justify. This can be a barrier to innovation, with slow growth, reduced efficiency, lost opportunities, and limited scalability and elasticity. Let’s understand the impact of each of these in detail.
Barrier to innovation
The constraints of on-premises infrastructure can limit the system design, which will be more focused on the availability of the hardware instead of the business use case. You might not consider some new ideas if they are not supported by the existing infrastructure, thus obstructing your creativity and hindering innovation within the organization.
Reduced efficiency
Once you finish developing the various components of the system, you might have to wait in long prioritized queues to test your jobs, which might take weeks, even if it takes only a few hours to run. On-premises infrastructure is designed to capitalize on the utilization of expensive hardware, often resulting in very convoluted policies for prioritizing the execution of jobs, thus decreasing your productivity and ability to innovate.
Lost opportunities
In order to take full advantage of the latest technology, organizations have to refresh their hardware. Earlier, the typical refresh cycle of three years was enough to stay current, to meet the demands of HPC workloads. However, due to fast technological advancements and a faster pace of innovation, organizations need to refresh their infrastructure more often, otherwise, it might have a larger downstream business impact in terms of revenue. For example, technologies such as Artificial Intelligence (AI), ML, data visualization, risk analysis of financial markets, and so on, are pushing the limits of on-premises infrastructure. Moreover, due to the advent of the cloud, a lot of these technologies are cloud native, and deliver higher performance on large datasets when running in the cloud, especially with workloads that use transient data.
Limited scalability and elasticity
HPC applications rely heavily on infrastructure elements such as containers, GPUs, and serverless technologies, which are not readily available in an on-premises environment, and often have a long procurement and budget approval process. Moreover, maintaining these environments, making sure they are fully utilized, and even upgrading the OS or software packages, requires skills and dedicated resources. Deploying different types of HPC applications on the same hardware is very limiting in terms of scalability and flexibility and does not provide you with the right tools for the job.
Now that we understand the limitations of doing HPC on-premises, let’s see how we can overcome them by running HPC workloads on the cloud.
