High-performance computing (HPC) and Artificial Intelligence (AI) supercomputers are often discussed together, given their shared foundation in advanced in-parallel processing computational capabilities. However, while they intersect, HPC stands apart and goes beyond AI supercomputers in several key ways. So, in this blog post, Let’s take a look at how HPC distinguishes itself and expands the frontiers beyond what AI supercomputers offer.

What is HPC?

High-performance computing, or HPC, is both a technology and practice. NVIDIA describes it as the art and science of using clusters of powerful processors working together to process data and perform complex calculations at high speeds across multiple servers in parallel. This is a significant leap from standard computing systems, which primarily use serial computing — solving problems by dividing the workload into a sequence of tasks and then running them one after the other on the same processor.

The efficiency of HPC comes from its ability to parallelize tasks, significantly enhancing computational performance. For instance, a typical desktop with a 3 GHz processor can handle about 3 billion calculations per second, but an HPC system can perform quadrillions of calculations per second, making it approximately 333,333.33 times faster than an average computer.

How Does HPC Work?

An HPC solution can be set up either on-premises, at the edge location, or in the cloud. An HPC cluster comprises hundreds or thousands of compute servers collaborating with each other. Each server, known as a node, contributes to the cluster’s processing power. These nodes operate in parallel, enhancing overall performance. A standard HPC setup typically consists of three key components: compute, network, and storage.

HPC can address a wide range of problems requiring complex calculations combined with parallel processing power. These include tasks like DNA sequencing, automating stock trading, and running AI algorithms for self-driving cars. HPC’s applications span various industries such as healthcare, energy, climate science, smart cities, and life sciences.

The Landscape of HPC

The HPC landscape is dominated by several key players and technologies that facilitate the efficient execution of complex tasks.

• NVIDIA: Known for its powerful GPUs. NVIDIA plays a significant role in HPC by providing the hardware necessary for high-speed computations.
• IBM: Offers the IBM Spectrum LSF, a workload management platform for distributed high-performance computing.
• Microsoft: Provides Azure Batch, which delivers Scheduler as a Service on Azure. And Azure CycleCloud, a traditional HPC cluster tool that supports autoscaling.
• Amazon: Through AWS, it offers the Elastic Fabric Adapter (EFA), which powers HPC applications with low-latency, low-jitter channels for inter-instance communications.
• Google: Alongside other emerging providers, Google Cloud offers scalable HPC solutions to meet diverse computational needs.

The Fundamental Differences

HPC and AI supercomputers have different roles:

• HPC uses aggregated computing power to solve large-scale computational problems across multiple fields, from climate modeling to complex weather predictions.
• AI supercomputers, however, are optimized specifically for AI workloads. It is focusing on training machine learning models and neural networks, and performing tasks related to data analytics and pattern recognition.

The following video gives an overview of how AI supercomputers train large GenAI models such as GPT3, GPT4, and GPT-4O:

Use case Versatility and Breadth of Applications

One of the most significant ways HPC surpasses AI supercomputers is in its versatility. HPC systems are built to handle a wide variety of computational tasks. Whether it’s simulating nuclear reactions, predicting weather patterns, or running large-scale financial models, HPC can do it all. This broad applicability is a major strength, simultaneously enabling advancements in multiple fields.

In scientific research, HPC has sequenced the human genome, modeled disease spread like COVID-19, and studied material properties. These tasks demand huge computational power and the flexibility to manage various data types and algorithms. AI supercomputers, though powerful, focus on specific tasks like image recognition, language processing, and autonomous decision-making.

Computational Power and Performance

When it comes to sheer computational power, HPC systems often lead the pack. Supercomputers like Summit and Fugaku, used for HPC tasks, boast performance levels in the exascale range (a billion billion calculations per second). This performance level is necessary for tasks requiring extreme precision and speed over extended periods.

AI supercomputers, while also incredibly powerful, are typically designed to optimize throughput and efficiency for specific AI workloads. They leverage specialized hardware like GPUs and TPUs (Tensor Processing Units), which excel in parallel processing required for training deep learning models. However, this specialization does not necessarily translate to superior performance in other types of computations that HPC systems excel at.

Beyond AI: The Future of HPC

The future of HPC involves not just maintaining its power, but also expanding its potential. One exciting area is quantum computing. HPC systems now integrate quantum capabilities to tackle problems unsolvable by classical computers.

For example, quantum HPC can revolutionize drug discovery by accurately simulating molecular structures at an unprecedented scale. It can also transform cryptography by creating and breaking encryption methods beyond current technologies.

Additionally, HPC is vital in big data analytics, processing vast datasets from various sources like social media and IoT sensors. This ability to extract insights from large, diverse datasets highlights HPC’s wide-ranging applications.

Synergy Between HPC and AI

While HPC and AI supercomputers have distinct domains, they exhibit significant synergy. HPC systems often accelerate the training of AI models, making the process faster and more efficient. Conversely, AI techniques optimize HPC workloads and enhance performance. This symbiotic relationship boosts the capabilities of both fields, fostering innovations that neither could achieve independently.

For example, in climate modeling, AI algorithms improve HPC simulations, resulting in more accurate weather forecasts and climate predictions. In healthcare, AI-driven data analysis with HPC systems enables breakthroughs in personalized medicine and early disease detection. Here is a book that would be suitable.

My personal take about HPC versus AI supercomputers

HPC differs from AI supercomputers and goes beyond them. This means in the versatility of use cases, computational power, and the breadth of applications. By continuing to evolve and integrate emerging technologies like quantum computing, HPC will remain at the forefront of computational advancements. And it will be pushing the boundaries of what we can achieve and understand.

The future promises a continued symbiosis between HPC and AI, enhancing our ability to solve complex problems and improve the world in myriad ways.

Looking forward

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