How to Bring High Performance Computing and AI to Your Campus with CDW

As universities and higher education institutions plan to bring HPC to their research teams, they may face the following operational and technical challenges.

Skills and operational readiness gap

Mid‑sized and emerging research institutes may need specialist expertise in operating an HPC environment. As an HPC cluster involves hundreds of CPUs running in parallel, it requires advanced skills to ensure it runs simulations and calculations at an efficient rate. Without these skills, the cluster may present operations risks.  

Infrastructure complexity and right‑sizing

When it comes to infrastructure decisions, it’s important that the HPC cluster is able to support the research objectives. The design must consider various research needs such as massive storage, high-bandwidth RAM and GPU clusters. If the project fails to accurately map these needs, it may drive costs up significantly or may not deliver the desired performance at all.

Shared access, fairness and governance

In a research setup, multiple departments and research groups must share common resources. This requires thoughtful tenancy, quotas, fair scheduling policies and clear service definitions, so users get predictable results without manual gatekeeping.

Balance power and cooling requirements

Modern HPC systems have components in high-density environments, requiring energy efficient power/cooling profiles. Institutions must plan whether to retrofit existing rooms, deploy more traditional air‑cooled racks or sequence future liquid‑cooling deployments while aligning these choices with realistic timelines.

The following roadmap approach can help Canadian higher education institutions structure their HPC and AI adoption plans. From identifying the best use cases to data governance, it outlines the key steps institutions can take in their HPC journey.

As an institute plans to install their first HPC cluster, they must think of the key initiatives they plan to use it for. This will help inform suitable technology decisions around how much data centre capacity they need and what kind of components would fit the bill.

For instance, if a biomedical research centre wants to run a data inference pipeline (data-intensive), its requirements will differ from those of a centre training AI models (computation-intensive) in-house.

Therefore, it’s important to define the workloads and use cases up front and build a preliminary design based on them.

Key considerations for upfront HPC cluster requirements

• Identify primary research domains that you intend to use the cluster for, such as genomics, climate modeling or deep learning, etc.
• Map expected workloads in terms of simulation vs. training, batch vs. interactive jobs, so the compute architecture can be designed accordingly.
• Understand user profiles that will engage with the cluster such as undergraduates, graduate students, postdocs and research staff.
• Establish performance metrics that will help estimate system performance.

As Canadian institutions consider their HPC strategy, one of the crucial decisions is where the workloads will run. Should they invest in on‑premises clusters, leverage cloud elasticity or adopt a hybrid model? This choice impacts cost, scalability and compliance.

While the decision will depend on the individual needs of an institution, it’s helpful to understand how to approach this decision.

Scenario 1: A university running a steady genomics workload may benefit from an on‑premises cluster as the dataset may go beyond petabytes in size. While this may have high up-front CAPEX, it will enable researchers to achieve desirable results.

Scenario 2: A research group experimenting with AI models might prefer cloud for short‑term GPU access without upfront capital expenses. It may simplify ease of access to compute resources and provide the ability to leverage the latest software and apps in the cloud more effectively.  

Therefore, evaluating deployment models early prevents costly misalignment and ensures flexibility for future growth.

Key considerations in choosing the right infrastructure model

• Assess workload patterns to identify whether you need steady vs. bursty performance, compute‑intensive or storage‑heavy and so on.
• Consider compliance and data residency requirements.
• Compare cost models: CAPEX for on‑prem vs. OPEX for cloud.
• Explore hybrid strategies for seasonal peaks or collaborative projects.

“It’s crucial to understand both the workloads you plan to deploy and how the infrastructure will support them,” Traves observed. “This impacts choices about the size and type of infrastructure you'll need as well as your approach to managing it effectively.”

Most HPC clusters come with high power requirements, which raises the need for resource efficiency right from the start. The goal is to right‑size for current needs while enabling modular growth.

Overbuilding components such as network interconnects or storage for hypothetical future workloads can waste funds, while underinvesting in compute can stall research initiatives.

Therefore, institutions must architect the HPC cluster in a way that meets the capacity needs of their research goals in a balanced manner.

Key considerations for resource‑efficient design

• Match CPU/GPU type and ratios to workload profiles.
• Size networking fabrics for actual communication patterns.
• Align storage tiers (scratch NVMe, parallel file systems, archival) to I/O behaviour.
• Implement orchestration tools for fair scheduling and efficient utilization.

Shared HPC resources may quickly become hard to manage without proper governance. Multiple departments competing for compute cycles, unclear data policies and security gaps can derail the productivity of researchers working on these systems.

As HPC clusters often process large amounts of data, it’s critical to enforce data privacy and usage policies. The cluster must enable isolation for each project and provide ways to clearly allocate resources for each project.

Post-secondary institutions must establish clear frameworks for fairness, security and sustainability. This can help avoid situations where one research group may monopolize resources, leaving others idle.

Key considerations for governance and data management

• Define access policies: quotas, job limits and departmental partitions.
• Apply security best practices: least‑privilege access, patching, compliance alignment.
• Set data lifecycle rules: retention, archival reproducibility standards.
• Provide onboarding documentation and templates for researchers.

“It’s crucial to ensure that the right management tools are in place to stand up and support the environment on an ongoing basis and that the universities have access to the tools for achieving data governance,” said Traves.

While universities and research institutions have a solid understanding of their research initiatives, they may need assistance from a technology standpoint. CDW can help bridge these HPC implementation gaps.

The complexities involved in architecting and deploying an HPC cluster can burden internal IT teams. Moreover, they may not have diverse skills to manage a cluster, which may affect its performance in the long run.

CDW Canada can help assess your HPC needs with greater clarity and build a solution that allows you to meet your research goals. CDW’s infrastructure experts are adept at designing solutions for higher education institutions, having worked with some of Canada’s top universities.

Key considerations for leveraging expert partnerships

• Use reference architectures and proven recipes tailored to academic workloads.
• Engage experts for project management and logistics.
• Plan roadmaps for upgrades and next‑gen accelerators.
• Provide training and handoff for day‑two operations.

“At the end of the day, It will come down to identifying what problem we are trying to solve? Once this is understood we can help our customers right-size the solution, especially if infrastructure is involved.” said Clarence Lee, Head of HPC Strategy, National, CDW Canada.

“This approach ensures that we are helping our customers optimize their HPC investments and enables us to help them navigate the rapidly changing technology landscape.”

CDW helps higher education institutions bridge the gap between research ambitions and technical execution.

Starting with a deep understanding of your workloads and goals, CDW designs right-sized HPC and AI solutions using proven reference architectures and open-source tools for orchestration and cluster management.

Our team manages end-to-end deployment from power and cooling assessments to multivendor integration and project logistics, ensuring a smooth rollout. We also guide institutions through procurement vehicles like OECM and funding programs such as CFI, accelerating acquisition while maintaining compliance and cost efficiency.