Three significant drivers are reshaping how organizations approach their computing infrastructure and bringing HPC towards the center of IT strategies: (1) the proliferation of technology choices, (2) the need for a computing “fabric” that spans from device to datacenter, and (3) the emergence of artificial intelligence (AI) as a new ecosystem.
These forces are compelling a shift toward custom, co-designed hardware — a collaborative approach to building systems that reduces cost and complexity. Co-design unites customers with the right coalition of vendors to architect and build an optimal, integrated solution tailored to specific requirements.
The Complexity of Choice
One of the most challenging aspects of modern HPC and AI is the overwhelming number of technology options available. CPUs from Intel, AMD, Ampere, and others, alongside GPUs and accelerators from Nvidia, AMD, Intel, Cerebras, Untether, Groq, SambaNova, and others. This is further complicated by interconnect and networking choices and a variety of storage options, all supporting a growing roster of software. Selecting and integrating the right component now and into the future is a difficult task.
These advances present opportunities, challenges, and tradeoffs. For example, while low-precision hardware allows for faster computations, not all applications are suited to it. Many HPC workloads, such as weather modeling or molecular simulations, may still require 64-bit precision, while AI-driven applications can often operate effectively with significantly lower-precision arithmetic. But HPC applications can come out ahead if practitioners can reformulate the algorithms to harness the strengths of newer technologies without compromising the precision or accuracy the results.
This is an example of where co-design comes into play: the ability to canvas a wide range of hardware and software options to make the best use of available technology. In such a case, it’s no longer a question of selecting a pre-built platform; it’s about customizing and fine-tuning the platform to serve specific needs. Co-design becomes even more essential when considering the platform’s evolution over time — ensuring it adapts seamlessly as new technologies emerge while maintaining the same functionality.
Building a Computing Fabric: From Device to Datacenter
In a world where everything is an AI-enabled computer, all IT solutions blend into a “fabric” – a unified web of flexible infrastructure that spans from the edge (where much of the data is generated and eventually consumed) to datacenters of varying size. This distributed computing model aligns with the modern workflow, where managing data flow is as important as computational power.
Consider the landscape of a modern HPC workflow: data streams in from sensors, devices, scientific instruments, or other edge environments, and must flow seamlessly through various computing layers, from edge nodes to datacenter supercomputers and cloud resources. The ability to manage this flow, this meta system, is critical. The co-design model ensures that the computing stack is optimized both in parts and in whole, and that it interacts and integrates with the rest of its extended ecosystem.
This is especially true when speed is of the essence. In applications where real-time data analysis is critical — such as autonomous devices or fleets, or digital twins — the ability to process data close to where it is generated while also having access to centralized, powerful computing resources can be a game-changer.
The AI Revolution and the Demand for More Power
Finally, AI is creating a clean slate, removing and replacing the boundaries of what is possible, and driving the need for even more powerful machines. AI workloads differ from traditional HPC applications in that they can readily take advantage of low-precision data types and arithmetic, and they benefit from a favorable funding environment that enables massively scaled infrastructure. This shift has profound implications for the design of HPC systems because wherever there is speed, HPC is looking to exploit it.
Co-design allows HPC systems to meet these demands head-on. By aligning hardware innovation with the specific requirements of AI workloads, organizations can build systems that are not only powerful but also efficient and scalable.
The Time for Co-Design Is Now
As HPC enters this new era of technological complexity, distributed computing, and AI-driven workloads, the need for a co-design approach has never been greater. The proliferation of technology choices, the demand for a seamless computing fabric from device to datacenter, and the new AI ecosystem make it clear that only a broad, vendor-neutral, engineering-led, mission-focused approach can meet all needs. Co-design enables organizations to build HPC systems that are greater than the sum of their parts, allowing them to harness the full potential of the modern computational landscape.
Additionally, power consumption and cooling are becoming more critical as systems grow increasingly powerful. A thoughtful co-design approach can help optimize energy efficiency, ensuring that new HPC infrastructures remain sustainable while meeting performance goals.
How SourceCode Can Help with Your Co-Design Needs
SourceCode’s history and business model are rooted in the co-design approach. Partnering with SourceCode for your HPC and AI needs offers distinct advantages:
- Accelerates time to market via proven co-design methodology that brings the right expertise at the right time
- Reduces technical risk via access to best-in-class components and building blocks
- Balances design constraints: cost, performance, power, cooling
- Maximizes the potential of rapidly evolving technologies, complementing in-house expertise
SourceCode offers expertise across a broad spectrum of leading-edge technologies that span from device to datacenter, including exascale-class systems from Eviden.
As a vendor-agnostic partner, SourceCode provides the flexibility to select the best components for optimal performance. Our streamlined co-design process enhances efficiency, saving both time and cost.
Additionally, SourceCode’s in-house, U.S.-based environmental testing lab ensures that your hardware meets top-quality standards and achieves the necessary certifications, providing unmatched reliability for your most critical systems.
Learn more at www.sourcecode.com or reach out to us at info@sourcecode.com.
