Resources for Professionals Interested in High Performance Computing

For aspiring and early career HPC professionals, this rapidly evolving HPC/AI environment presents considerable challenges, including demands for specialized AI infrastructure skills, increasingly complex architectures and libraries, and a shift toward lower-precision computing

Once used almost exclusively by governments and researchers, high-performance computing (HPC) is now converging with AI and offering enticing possibilities for users across industry and commercial sectors.

At Supercomputing 2025’s “Future of HPC” panel, moderator Wim Slagter recalls how Todd Simons, an HPC expert at Rolls-Royce Americas, described exascale computing access as being akin to accessing “a time machine … you’re jumping into the future of where computers will be years from now and getting your applications ready.”

For aspiring and early career HPC professionals, this rapidly evolving HPC/AI environment presents considerable challenges, including

  • Demands for specialized AI infrastructure skills
  • Increasingly complex architectures and libraries
  • A shift toward lower-precision computing

Even in tumultuous times, however, knowledge remains an essential power.

Here, you’ll learn…

  • Which HPC skills are foundational today? Master fundamentals such as parallel and distributed computing, AI–simulation workflow integration, and performance optimization, with an eye toward next-gen system designs.
  • What trends are shaping HPC’s future? CPU-based clusters are giving way to hybrid heterogeneous architectures, and the classical vs. quantum mindset is shifting to classical + quantum possibilities.
  • What challenges is the HPC field facing? Urgent issues include AI integration, power consumption, and system scalability and complexity.
  • What are promising career paths in HPC? HPC skills are netting salary boosts in many sectors; in-demand roles include cluster engineers, parallel computing engineers, and HPC–AI systems architect.
  • Which ethical challenges are most urgent? Topping the list are energy consumption, equitable HPC access, and accountability.
  • How can I stay up-to-date on HPC news and research? Access the latest standards, SME insights, and industry trends.

Learn more about the field at The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC)

HPC: The Fundamentals

The shift toward hyperscale AI platforms is reshaping the HPC landscape across hardware, software, and economics. As Daniel Reed and colleagues put it in their article on “Scientific Computing in an AI World,” HPC’s challenge is to “ride the wave” of AI-driven infrastructure while also building the future, including through ambitious, next-generation system designs that

  • Use energy-aware algorithms to reduce energy per validated scientific outcome
  • Innovate architectures focused on memory and interconnect efficiency
  • Build software stacks optimized for hybrid AI/simulation workflows

HPC Skills to Focus on Now

  • Parallel and distributed computing: Designing software that efficiently scales across CPUs, GPUs, clusters, and heterogeneous systems.
  • AI/simulation workflow integration: Bridging machine learning and traditional simulation.
  • Performance optimization and profiling: Including identifying bottlenecks in memory, storage, and networking to improve efficiency at scale.
  • Energy- and resource-aware computing: Optimizing for both performance and energy efficiency.

What Are Key Trends in HPC?

As Matt Vincent emphasizes in the Data Center Frontier report, Present and Future HPC Trends, Forecast, and Implications Within Data Centers, AI and HPC workloads are increasingly hosted side by side. This is one of several developments creating new opportunities—and challenges—in HPC today.

Heterogeneous and AI-Accelerated Architectures

HPC systems are moving from CPU-based clusters to hybrid heterogeneous architectures combining CPUs, GPUs, and specialized AI processors. The goal is to maximize performance and power efficiency for AI-driven workloads for tasks including

  •  scientific simulation
  • AI training/inference
  • data analytics
  • real-time modeling

What Do Hybrid Heterogeneous Architectures Mean for HPC?

  • Increased convergence between HPC and AI infrastructure
  • Increased use of AI-assisted simulation and modeling
  • Demand for software that can run across multiple processor types
  • Increased commercial relevance of HPC beyond academia and government labs

What Are the Technical Challenges of Heterogeneous Architectures?

  • Programming across heterogeneous hardware
  • Portability issues between vendors and architectures
  • Memory bandwidth bottlenecks
  • Efficient scheduling of mixed CPU/GPU workloads
  • Energy and cooling requirements at exascale scale
  • Maintaining numerical precision in AI/HPC hybrid workflows

Where Can I Learn More?

Hybrid Quantum–HPC

In the hybrid quantum-HPC paradigm, classical supercomputers work closely with quantum processors (QPUs) and treat the quantum component as an accelerator for complex tasks.

These hybrid architectures combine

  • CPUs for orchestration and control
  • GPUs for acceleration and error correction
  • QPUs for highly specialized computational tasks

As “Towards A Unified Quantum Platform” notes, 71% of HPC centers globally plan to deploy quantum computing in some form in 2026, shifting away from the classical vs. quantum model to emerging classical + quantum possibilities.

What Does Hybrid Quantum–HPC Mean for HPC?

In addition to the growth of quantum-centric supercomputing, this trend is likely to result in

  • Workflows combining simulation, AI, and quantum optimization
  • Development of quantum-aware schedulers and middleware
  • Increased investment in HPC infrastructure that supports quantum systems

Where Can I Learn More?

Exascale Computing and the Energy/Data Bottleneck

HPC systems are entering an exascale era where performing at a quintillion calculations per second (or more) may soon be the norm. Such raw compute power creates considerable challenges, not least in

  • Energy consumption
  • Storage throughput
  • Data movement
  • Interconnect efficiency

As systems scale upward, moving and storing data often costs more than computation itself.

What Does Exascale Computing Mean for HPC?

Among the implications, exascale computing results in

  • Greater emphasis on energy-efficient architectures
  • Expansion of liquid cooling and advanced interconnect technologies
  • Increased focus on storage hierarchy and memory optimization
  • More regional/national investment in HPC infrastructure

Technical Challenges

  • Massive power consumption
  • Thermal management and cooling complexity
  • Storage bandwidth bottlenecks
  • Fault tolerance across millions of parallel processing elements
  • Efficient data movement between compute nodes
  • Reliability and resilience in very large distributed systems

Where Can I Learn More?

What Are the Challenges in HPC Today?

As is true across the tech spectrum, the increasing intertwining of AI in HPC entails multiple challenges, including the following.

Energy Consumption and Cooling

Power delivery, cooling, and carbon footprint have become limiting factors on HPC, especially exascale and AI-focused systems, which often require tens of megawatts to operate efficiently. Research is therefore focusing on improvements in power management, liquid cooling, and energy-aware scheduling to keep systems sustainable.

Where Can I Learn More?

Scalability and System Complexity

HPC increasingly combines CPUs, GPUs, accelerators, huge memory pools, and fast interconnects across thousands of nodes. This makes efficiently scaling applications difficult due to communication delays, memory bottlenecks, and hardware failures. All of these issues escalate as systems grow larger. The complexity of these exascale systems leaves software and programming models struggling to keep up.

Where Can I Learn More?

Integrating AI Workloads With Traditional HPC

AI and machine-learning workloads are changing how HPC systems are designed and used. While traditional scientific simulations require high numerical precision, AI workloads prioritize massive parallelism and accelerator performance. Combining both goals efficiently on shared infrastructure creates challenges in many areas, including scheduling, storage, networking, software frameworks, and resource management.

Where Can I Learn More?

Building a Career in HPC Today

HPC’s rapid convergence with AI is making GPU systems engineering, distributed computing, storage architecture, and performance optimization among the fastest-growing technical specialties in computing today.

In addition to growth in traditional HPC markets such as government and defense, HPC adoption in the life science sector is expected to grow dramatically over the next few years to accelerate drug discovery for various diseases.

According to Fortune Business Insights, the global HPC market was valued at US$59.33 billion in 2025 and is projected to grow to US$128.84 billion by 2034. This growth is fueled by several factors, including the increasing number of data centers and cloud services, and adoption of quantum computing and cloud-based HPC.

All of these changes bode well for the HPC job market. Following are some key roles to consider.

HPC Systems Administrator/Cluster Engineer

HPC Software Developer/Parallel Computing Engineer

AI Infrastructure/GPU Platform Engineer

HPC Data/Storage Engineer

HPC–AI Systems Architect

Ethical Issues in HPC

  • In its 2025 Energy and AI report, the International Energy Agency notes that “without energy, there is no AI.” While AI might well be able to eventually help us to transform energy use in beneficial ways, in the near term, it simply needs more. Much more. As IEA points out, while a typical data center consumes as much electricity as 100,000 households, the next-generation campuses being built to support AI’s growth will demand 20 times that amount.Energy is just one ethical issue HPC must face, but it is a massive one that intertwines with many others.

Energy Consumption of HPC–AI Infrastructure

“Compute efficiency” has grown from a merely technical concern to an ethical one. Training frontier AI models increasingly requires hyperscale GPU clusters and large data centers; without sustainable energy strategies, those data centers will strain regional power grids and increase carbon emissions in ways that are currently difficult to calculate.

Whether unlimited scaling of computational workloads is environmentally sustainable is just one of the questions that researchers, governments, and other stakeholders are grappling with here.

Other key ethical questions:

  • Who will pay the environmental costs of hyperscale computing?
  • Should organizations be required to report compute-related emissions and water use?
  • Should sustainability issues put limits on AI/HPC growth?

Where Can I Learn More?

Concentration of Computing Power

A relatively small group of hyperscalers, governments, universities, and major tech firms control most advanced HPC and AI infrastructure. This strategic advantage impacts scientific research, economic competitiveness, defense capabilities, and AI development itself.

This increasing concentration of compute results in “compute inequality” as smaller institutions, developing nations, independent researchers, and nonprofits—and the range of ideas they offer and interests they serve—are excluded from meaningful participation in frontier research.

Key ethical questions:

  • Who gets access to advanced computational infrastructure?
  • Does concentration of compute reduce scientific openness and innovation?
  • Could compute access become a geopolitical or economic gatekeeping mechanism?

Where Can I Learn More?

Responsible Use of HPC

Among the things that advanced surveillance systems, autonomous weapons research, cyber operations, large-scale biometric analysis, and AI-driven decision-making systems have in common? They are all increasingly controlled by HPC systems.

HPC infrastructure enables increasingly autonomous AI models to operate at unprecedented scale and speed. Ethically, it’s no longer a question of whether these systems can be built; they are here and the issues only begin with accountability to governance.

Key Ethical Questions

  • What limits should exist on military and surveillance applications of HPC?
  • How should accountability function in autonomous systems?
  • Can democratic oversight keep pace with large-scale computational capability?

Where Can I Learn More?

Resources: The HPC Knowledge Hub

Stay up-to-date with the latest on learning technologies by accessing our Tech News blog, which is updated daily with the insights, trends, and research related to all things computing. Among recent related articles are the following: