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As artificial intelligence continues to evolve, there has been a growing focus on GPUs as an essential component of AI infrastructures, but less on understanding the intricacies of networking within AI environments. However, to maximize the utilization of high-cost GPU resources,  AI requires a network capable of dynamically allocating computation resources. This necessity is steering us towards a renewed interest in composable infrastructure—a concept that, while not new, has resurfaced as a critical solution to meet the surging demand for computing capacity.

However, the journey to achieving such composable infrastructure is not without its hurdles. Traditional network technologies like ethernet and Infiniband, alongside newer direct connection technologies such as PCI-E, NVLink and CXL are being explored as potential pathways to this goal. That being said, each of these technologies presents advantages and limitations, adding to the complexity of the task. 

“Dynamic allocation of GPU resources in a high bandwidth, lossless AI cloud network is indeed a problem.  Energy and cooling are perhaps even larger gating factors for power hungry AI infrastructure.  Hedgehog customers like Dema Energy and VMAccel are tackling these problems with innovative energy management and fluid immersion cooling in extremely hot and demanding environments.  Hedgehog rounds out the solution with an AI network that puts GPUs and Field-Programmable Gate Arrays (FPGAs) to use for accelerated edge computing in unique locations,” shares Matthew Fields, CEO of VMAccel.

For instance, while PCI-E is ubiquitous, it isn’t naturally suited for the dynamic sharing envisioned in a composable infrastructure. Similarly, NVLink from Nvidia offers promise, but is limited by its proprietary nature. CXL, backed by Intel, emerges as another potential option in this landscape, promising a more open and flexible approach. However, there has been much debate on whether it will work.

Simultaneously, another problem arises in the journey towards an intra-rack networking future, and it’s a hot one. The surge in demand for computational resources, propelled by the proliferation of data-intensive applications and the continuous expansion of cloud services, has spotlighted the pivotal role of Computational Fluid Dynamics (CFD) in the planning and optimization of data centers. 

For some context, CFD has long been used in simulating and modeling fluid dynamics for applications as critical as jet engine development. However, its use has a strong foundation for its application in modern technological challenges. Today, as data centers evolve into the primary unit of compute resources, the ability of CFD to predict and manage heat transfer has become indispensable. 

In fact, CFD is even emerging as one of the more sought-after fields in engineering schools. Furthermore, we can see real-time examples of major companies starting to prioritize the growing need for CFD. Just within the past couple of months, Synopsys announced plans to acquire CFD software market leader Ansys, and Cadence announced a new GPU-powered CFD platform called “Millennium”.

CFD’s growing significance in data center planning is a direct response to heat becoming a critical gating factor in the scalability of these facilities. As data centers scale, the density of computing hardware increases, elevating the amount of heat generated within these environments. Overheating could lead to hardware failure, reduced performance and increased cooling costs without precise and effective heat management strategies, impacting operational efficiency and sustainability goals. 

This convergence of technologies and challenges illustrates how closely intertwined the future of computing infrastructure is with the evolution of thermal management strategies. As we move toward a world characterized by rack-scale or datacenter-scale composable infrastructure, innovations like shared memory and CXL enable even greater GPU density.

This, in turn, amplifies the generation of heat, thrusting advanced CFD into the spotlight as a critical tool for addressing these emerging issues. The path forward for scaling computing capabilities is innovating how we manage the resultant heat and power consumption. Thus, the journey toward the next frontier in computing will be guided as much by advances in thermal management as by the breakthroughs in computational technologies themselves.