Harikrishnan Muthukrishnan, Principal IT Developer at Florida Blue and Global Expert in Pega PRPC Architecture and Administration.
High-performance computing (HPC) refers to the use of supercomputers, server clusters and specialized processors to solve complex problems that exceed the capabilities of standard systems.
HPC has come a long way since Seymour Cray’s CDC 6600 and the pioneering supercomputers of the 1960s. Once confined to government labs and research universities, HPC now powers industries from aerospace to precision medicine.
For the recent explosion of artificial intelligence and machine learning, HPC has provided the backbone for training and running LLM models efficiently. The convergence of HPC and AI, often referred to as AI-HPC, is driving breakthroughs across various industries. NVIDIA’s rapid rise to a $4.16 trillion market cap underscores the surging demand for HPC and its influence across sectors.
As a global expert in Pega PRPC, low-code system architecture and healthcare IT administration, I’ve spent over a decade modernizing mission-critical systems in the U.S. health insurance industry. I had the opportunity to learn and grow with the AI-HPC revolution and implement low-code solutions across sectors. In this article, I’ll explore the powerful convergence of high-performance computing (HPC) and low-code healthcare solutions.
Why Low-Code Platforms In Healthcare Benefit From HPC
Traditional healthcare IT systems operated in transactional, relatively low-volume environments. Today’s digital-first health organizations face continuous streams of unstructured and structured data from health insurance sales and claims, electronic health records (EHRs), medical devices, wearables and research pipelines.
I’ve seen HPC enhance low-code solutions by:
• Accelerating Data Pipelines: Parallel processing enables near-real-time ignition and analysis of massive datasets, such as those driven by wearable devices, for care models.
• Enabling AI/ML At Scale: HPC-powered AI models are integrated into low-code workflows by most, if not all, leading low-code platform providers, enhancing predictive analytics, real-time fraud detection and real-time clinical decision support.
• Enhancing Simulation And Modeling: From drug discovery to personalized treatment simulations, HPC enables complex calculations to be accessible within user-friendly, low-code interfaces in healthcare implementations.
• Improving Patient Experience: Faster insights can lead to shorter diagnosis-to-treatment times, self-healing low-code applications, claims adjudication at scale and more proactive care management through low-code workflows.
Global HPC Challenges Impacting Healthcare And Other Industries
Let’s take a look at a few of the challenges I’ve seen organizations face regarding HPC.
1. Scalability And Performance Bottlenecks
AI training, big data analytics and real-time decision making require scaling across HPC systems. I/O throughput, memory bandwidth and network latency often limit performance, particularly when compute power exceeds the speeds of storage and interconnect.
In healthcare, these bottlenecks can slow down time-sensitive workloads, such as emergency diagnostics or genomics pipelines in precision medicine, resulting in delays.
To address this, organizations should do the following:
• Adopt HPC data-centric architectures and integrate AI-optimized near-HPC storage with NVMe-over-fabrics, edge and in-network computing.
• Implement hybrid HPC strategies. Sensitive workloads stay on-prem, while AI model training can extend to cloud platforms with HPC-grade interconnects.
• Use parallel data pipelines and optimize the movement of large datasets to reduce latency across AI and simulation workloads in hybrid cloud environments.
2. Energy Efficiency And Sustainability
Modern HPC facilities consume megawatts of power, which drives operational costs and environmental impact. Cooling and hardware inefficiencies can undermine ROI. Sustainable HPC architectures are crucial for maintaining the accessibility and cost-effectiveness of AI-driven healthcare.
In these cases, I’d suggest organizations do the following:
• Deploy liquid cooling and immersion systems to efficiently manage thermals.
• Invest in energy-efficient CPUs and GPUs and utilize carbon-aware job scheduling to shift workloads to more environmentally friendly power sources.
• Apply AI to energy optimization, including dynamic power scaling and predictive cooling.
3. Complexity And Fragmentation
The use of diverse hardware architectures, middleware stacks and programming models increases the complexity of integration. Specialized skills in MPI, CUDA and heterogeneous programming are scarce, which hinders the adoption of these technologies in healthcare IT teams.
To overcome this, businesses should do the following:
• Bridge the skills gap through continuous training, mentorship and academic partnerships to upskill IT staff through structured, practical and accessible learning programs.
• Simplifying the HPC software stack requires adopting standardized middleware, open-source frameworks and modern cluster management tools to improve interoperability, streamline workflows and reduce administrative complexity.
• Address fragmentation through flexible architecture, robust data management, secure storage and AI-driven automation to enhance interoperability and predictive insights
The New Paradigm
Recent advances are making HPC accessible beyond elite research facilities:
• Cloud-Native HPC: Hyperscalers now offer on-demand HPC clusters with elastic scaling, reducing capital costs.
• AI-Optimized Architectures: From what I’ve seen in the industry, hardware tuned for deep learning can accelerate medical imaging, genomics pipelines in precision medicine and predictive models.
• DevSecOps For HPC: Continuous integration, automated testing and embedded security can streamline the deployment of HPC-enabled healthcare apps.
• Containerization And Microservices: HPC workloads can be modularized for integration with low-code platforms.
What Organizations Must Do Next
With all of this in mind, I believe healthcare leaders should:
• Audit their AI-HPC strategy. Evaluate needs, define clear goals and craft a governance framework for continuous improvement, balancing costs and performance.
• Invest in skills and partnerships. Collaborate with HPC vendors, cloud providers and universities to bridge talent gaps.
• Design for hybrid sustainability. Combine on-premises HPC for sensitive data with cloud HPC for burst capacity, utilizing parallel I/O frameworks to balance data throughput.
• Prioritize interoperability standards. Build connectors, APIs and orchestration layers that bridge HPC outputs with low-code tools, using API gateways to abstract hardware complexity and ensure seamless interoperability.
• Compliance and security by design. Embed security-by-design, automated compliance validation using DevSecOps and zero-trust architectures in HPC pipelines.
Toward Human-Centric Supercomputing
The convergence of HPC and low code in healthcare is not just about speed; it’s about empowering people with actionable insights that drive informed decision making. The future lies in human-centric supercomputing, systems that deliver immense computational power through intuitive, secure and interoperable interfaces.
By embracing this fusion of HPC, low-code platforms and security-first cloud-native architectures, healthcare can accelerate innovation, improve outcomes and make advanced care more accessible to a broader population.
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