This article is based on reporting originally published at cio.com (May 2020). Market figures are attributed to Hyperion Research.
The Growing Intersection of AI and High-Performance Computing
Artificial intelligence is no longer a research curiosity—it is deeply embedded in everyday business operations. From natural-language chatbots in customer service centers to autonomous vehicles in logistics and distribution, AI-driven capabilities are reshaping how organizations function at every level.
This rapid adoption has created an equally rapid surge in demand for high-performance computing (HPC). Training deep learning models, running real-time AI inferencing, and performing analytics on massive datasets all require enormous computational resources—resources that traditional enterprise infrastructure was never designed to provide at scale.
HPC-Enabled AI: A Fast-Growing Market Segment
According to Hyperion Research, workloads including machine learning, deep learning, inferencing, training, graph analytics, and high-performance data analytics (HPDA) have all experienced robust growth in both budget allocations and organizational focus. Hyperion projected that HPC-enabled AI would grow at more than 30 percent annually to reach a $3.5 billion market by 2024.
Hyperion also noted a structural shift in where these workloads actually run. The percentage of HPC workloads executing in the cloud reached approximately 20% in 2019, a significant jump that reflects changing infrastructure priorities across the industry.
Why HPC Workloads Are Moving to the Cloud
Several converging factors explain why cloud environments are becoming a viable—and sometimes preferred—platform for HPC and AI workloads:
- Hybrid and multi-cloud maturity: Cloud service providers (CSPs) now offer hybrid and multi-cloud options specifically designed to give HPC users a near-seamless computing environment that spans on-premises hardware and cloud resources. This reduces the friction historically associated with migrating HPC jobs off dedicated clusters.
- Elastic capacity for complex workloads: As HPC applications grow in complexity—particularly when new AI techniques are layered onto traditional modeling and simulation workflows—the ability to burst into the cloud provides a more flexible and responsive computing environment without requiring permanent capital investment in hardware.
- Unified management across providers: Multi-cloud capabilities allow HPC users to manage resources from multiple CSPs through a single management plane. This is especially attractive to global enterprises that rely on different cloud providers in different geographic regions for latency, compliance, or cost reasons.
- Data gravity: For some AI workflows, the training data already resides in a cloud environment—collected from IoT sensors or assembled from multiple cloud-hosted datasets. Moving that data to an on-premises cluster can be expensive and slow. Running the workload where the data lives is often more practical.
The Role of HPC Expertise in Cloud Adoption
One barrier to cloud adoption for HPC teams has historically been the knowledge gap: HPC practitioners are experts in parallel computing and job scheduling, but may be less familiar with cloud instance selection, network topology, and cost optimization. Recognizing this, many CSPs now offer dedicated HPC support teams, in-house experts, and professional services to help users select the right instance types and configurations for specific workload profiles.
Hardware and software vendors have also built dedicated facilities to close this gap. Hyperion Research noted Dell’s HPC & AI Innovation Lab as one example of an environment that combines purpose-built hardware and software infrastructure with a staff of engineers, computer scientists, and domain experts engaged in the HPC community. The goal is to help end users understand best practices for solving HPC and AI problems, whether they are running AI alongside traditional simulation workloads or exploring entirely new application domains.
What This Means for DevOps and Platform Engineering Teams
For DevOps and platform engineering teams, the convergence of AI, HPC, and cloud infrastructure introduces new operational requirements:
- GPU and accelerator orchestration: AI training jobs typically require GPU instances. Teams need pipelines that can provision, schedule, and tear down GPU nodes efficiently—tools like Kubernetes with GPU operator support or cloud-native batch services become important.
- Cost management at scale: HPC workloads can consume enormous amounts of compute in short bursts. Spot or preemptible instances, combined with checkpointing strategies, are common approaches to managing cost without sacrificing throughput.
- Data pipeline reliability: When data gravity drives workload placement, robust, observable data pipelines between storage systems and compute clusters become a critical infrastructure concern.
- Hybrid connectivity: Organizations that retain on-premises HPC clusters while bursting to the cloud need low-latency, high-bandwidth connectivity—typically through dedicated interconnects or SD-WAN solutions—to avoid bottlenecks.
Key Takeaways
The movement of HPC workloads into cloud environments is being driven by the computational demands of AI, the maturity of hybrid cloud offerings, and the practical reality of data gravity. As CSPs invest more heavily in HPC-specific support and tooling, the barrier to running demanding AI and analytics workloads in the cloud continues to drop. For engineering teams, this shift means adapting infrastructure practices to support elastic, accelerator-driven, data-intensive workloads—while maintaining the cost controls and observability that production environments require.