HPC Challenges: Overcoming Platform Complexity

It wasn’t that long ago that single, on-prem HPC clusters could support an organization’s workload even with peak demand. Today, with resources spread across divisions, decentralized, and remote workforce — and even end-users at times — maintaining a simple HPC infrastructure is increasingly rare. Even if you can maintain an on-premises HPC cluster, the number of endpoints has grown significantly.

At the same time, most organizations have moved to multiple clusters for different workloads, deploying a hybrid-cloud infrastructure or using a composable infrastructure to improve flexibility. As companies leverage the benefits of high performance computing, the complexity of the platform continues to expand.

The Challenges of HPC Platform Complexity

As HPC deployment and usage continue escalating, systems and interdependencies are also growing. Here are some of the more significant challenges organizations face when deploying HPC platforms.

Legacy Resources

Legacy data centers may not support the hefty demands of HPC computing. For example, the newest generation of processors requires significantly higher levels of energy and generates more heat. Without retrofitting cooling equipment, data centers may be unable to regulate temperatures appropriately. This problem only compounds as rack density increases.

As new hardware is deployed, it must also be optimized to work efficiently together to maximize investment. Incompatible components or legacy equipment can easily become bottlenecks that throttle optimal output.

The same can apply to your cloud computing resources. With a hybrid cloud approach, you can push overflow workloads to cloud servers and scale on demand. However, this can significantly increase operating costs if you don’t carefully monitor workloads.

Integration of Multiple Processors and Accelerators

Accelerators and multi-core processors provide higher levels of parallelism, but this also increases system complexity. This system design increases the difficulty in accurately forecasting workloads, such as quantifying the runtime behavior of certain applications.

This can also affect code design. Optimizing code for HPC deployments requires more advanced programming that accounts for any architecture constraints for both efficiency and performance. Cross-accelerator design can deliver peak performance, but it can be challenging to optimize code in such a complex environment. Parallel programming is common in HPC deployments but is much more difficult than conventional programming.

At the same time, accelerator deployment and management at scale only add to infrastructure complexity.

Consistency in Workloads Across Hybrid Environments

Without the right architecture, end users can be affected by cloud migration. Every workload must run consistently, whether managed on-prem or in the cloud. However, when a workload moves to the cloud, it must still perform simulations the same way it did when on-premises to deliver reliable results.
HPC design must deliver consistency in user experience and scalable, on-demand compute resources in a hybrid environment.

HPC Architecture

An effective HPC architecture goes far beyond the hardware and cloud platforms it uses. For example, HPC systems generate and process an enormous amount of data. Data must be managed and stored efficiently using sophisticated networking and storage infrastructure for fast retrieval and data analysis.

When so many organizations continue to deal with data silos and no single source of truth, system architecture often requires an entire overhaul to realize the benefits of high performance computing.

Hardware Abstraction

As organizations have migrated resources to the cloud, they often focus more on applications and use cases rather than the underlying tech layer that makes cloud computing possible. Designing a purpose-built HPC solution takes detective work to design efficient systems, working backward from use cases to reverse engineer the required hardware and architecture.

Few organizations today have the time, resources, or in-house expertise to manage the required hardware abstraction to build future-proof HPC solutions.

Cluster Management, Control, and Security

HPC clusters require both an underlying infrastructure to execute applications and a control layer to manage the infrastructure.

Mission-critical and sensitive data and computations require secure node management and monitoring. As clusters today are often shared across departments, users, and even customers, increased vulnerability has become an even greater concern.

This requires robust management, control, and security for cluster nodes. Organizations must also be able to streamline node management regardless of the architecture's complexity. Designing the right HPC environment also needs to account for remote management.

Rapid Pace of Innovation

Properly deploying and tuning an HPC cluster is specialized work that can take significant time and resources. It is also error-prone without special expertise, and performance can suffer if the system isn’t configured properly for target workloads.

It can be challenging to stay current on the latest advancement in an innovative and evolving industry. AI and Machine learning (AI/ML) are requiring larger and larger data sets and training models. Tools must scale and integrate with HPC software, compute, and storage environments to leverage the power of HPC.