HPC Users’ Reactions to the Convergence of HPC and AI
Welcome to the third entry in this series on AI. The first one was an introduction and series overview, and the next discussed the aspirational goal of artificial general intelligence, AGI. Now it’s time to zero in on another timely topic—HPC users’ reactions to the convergence of HPC and AI.
AI Relies Heavily on HPC Infrastructure and Talent
HPC and AI are symbiotes, creations locked in a tight, mutually beneficial relationship. Both live on a similar, HPC-derived infrastructure and continually exchange advances—siblings maintaining close contact.
- HPC infrastructure enables the AI community to develop sophisticated algorithms and models, accelerate training and perform rapid analysis in solo and collaborative environments.
- Shared infrastructure elements originating in HPC include standards-based clusters, message-passing (MPI and derivatives), high-radix networking technologies, storage and cooling technologies, to name a few.
- MPI "forks" used in AI (e.g., MPI-Bcst, MPIAllreduce, MPI_Scatterv/Gatherv) provide useful functions well beyond basic interprocessor communication.
HPC and Hyperscale AI: The Data Difference
Social media giants and other hyperscalers were in a natural position to get the AI ball rolling in a serious way. They had lots of readily available customer data for exploiting AI. In sharp contrast, some economically important HPC domains, such as healthcare, still struggle to collect enough usable, high-quality data to train large language models and extract new insights.
Addressing the Data Shortage
The HPC-AI community is working to remedy the data shortage in multiple ways:
- A growing ecosystem of organizations is creating realistic synthetic data, which promises to expand data availability while providing better privacy protection and avoidance of bias.
- The community is developing better inferencing—guessing ability. Bigger inferencing "brains" should produce desired models and solutions with less training data.
- The recent DeepSeek news showed, among other things, that impressive AI results can be achieved with smaller, less-generalized (more domain-specific) models that require less training data—along with less time, money, and energy use.
Beneficial Convergence or Scary Collision?
Attitudes of HPC center directors and leading users toward the HPC-AI convergence differ greatly. All expect mainstream AI to have a powerful impact on HPC, but expectations range from confident optimism to varying degrees of pessimism.
Concerns That May Keep Optimists and Pessimists Up at Night
Here are things in the HPC-AI convergence that seem to concern optimists and pessimists alike:
- Inadequate access to GPUs. GPUs have been in short supply. A concern is that the superior purchasing power of hyperscalers—the biggest customers for GPUs—may make it difficult for Nvidia, AMD, and others to justify accepting orders from the HPC community.
- Pressure to Overbuy GPUs. Some HPC data center directors, especially in the government sector, told us that AI "hype" is so strong that their proposals for next-generation supercomputers had to be replete with mentions of AI. This later forced them to follow through and buy more GPUs—and fewer CPUs—that their user community needed.
- Difficulty Negotiating System Prices. More than one HPC data center director reported that, given the GPU shortage and the superior purchasing power of hyperscalers, vendors of GPU-centric HPC systems have become reluctant to enter into customary price negotiations with them.
- Continuing Availability of FP64. Some HPC data center directors say they’ve been unable to get assurance that FP64 units will be available for their next supercomputers several years from now.
Preliminary Conclusion
It’s early in the game and already clear that AI is here to stay—not another "AI winter." Similarly, nothing is going to stop the HPC-AI convergence. Even pessimists foresee strong benefits for the HPC community from this powerful trend. HPC users in government and academic settings are moving full speed ahead with AI research and innovation, while HPC-reliant industrial firms are predictably more cautious but already have applications in mind.
FAQs
Q: What are the benefits of the HPC-AI convergence?
A: The HPC-AI convergence brings together the strengths of both communities, enabling the development of sophisticated algorithms and models, accelerating training, and performing rapid analysis in solo and collaborative environments.
Q: What are the concerns surrounding the HPC-AI convergence?
A: Concerns include inadequate access to GPUs, pressure to overbuy GPUs, difficulty negotiating system prices, and the continuing availability of FP64.
Q: How will the HPC community adapt to the HPC-AI convergence?
A: The HPC community will adapt to the HPC-AI convergence by leveraging the strengths of both communities, developing new applications, and innovating in areas such as realistic synthetic data and better inferencing.
