Partnerships
AMD and Cornelis Expand HPC Partnership as GWDG Deploys EMMY P4
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AMD and Cornelis are expanding their high-performance computing partnership, pairing AMD processors and accelerators with Cornelis networking to help research organizations bring larger scientific workloads into production. A deployment at Germany’s GWDG provides a concrete example: the EMMY P4 supercomputer combines approximately 300 dual-socket AMD EPYC 9745 nodes with Cornelis CN5000 Omni-Path networking.
The collaboration extends beyond selling compatible components. The companies are establishing a joint go-to-market program covering system design, application benchmarking, validation, acceptance testing, and production deployment through existing manufacturers and system integrators. Customers can also test their own applications at AMD’s HPC Center of Excellence in Munich before committing to a configuration.
That approach addresses a persistent supercomputing problem: buying more processors does not guarantee proportionally faster research. Applications have to move data between those processors quickly enough to keep them working.
Why the Network Matters as Much as the Compute
Scientific simulations often divide a large problem across many servers. Each server calculates part of the result, then exchanges information with the others. Computational fluid dynamics, for example, requires neighboring regions of a simulated flow to share updated conditions as the calculation progresses.
Those exchanges make network latency, message-handling capacity, and congestion consequential. A processor waiting for another node’s data contributes little useful computation, however fast it performs arithmetic. Message Passing Interface, or MPI, is a widely used software interface for coordinating this distributed work.
Cornelis supplies the interconnect fabric connecting the nodes, while AMD supplies the compute. The expanded program aims to validate those pieces together against applications rather than treating network bandwidth and processor specifications as sufficient evidence of system performance.
Lisa Spelman, CEO of Cornelis, said each new EPYC generation increases the number of cores needing access to the network. Her point is that adding compute capacity can leave expensive resources waiting if communication cannot keep pace, making workload-specific validation essential.
What GWDG’s EMMY P4 Adds
GWDG, the scientific computing organization in Göttingen, supports NHR@Göttingen, one of Germany’s nine national high-performance computing centers. Its users run applications spanning life sciences, physics, quantum research, AI, and the humanities. Cornelis says the broader environment handles approximately 3.5 million HPC jobs annually.
AMD’s EPYC 9745 specifications list 128 physical cores per processor, putting 256 cores in each dual-socket node. Keeping that many cores productive across hundreds of nodes makes communication efficiency a system-level concern.
GWDG’s installation documentation describes an initial group of 252 nodes with 768 GiB of DDR5 memory and 3.8 TiB of local NVMe storage per node, connected through CN5000 adapters with 200G links. The announcement describes the larger EMMY P4 deployment as approximately 300 nodes.
The distinction between this CPU installation and the partnership’s GPU work matters. EMMY P4 demonstrates EPYC and Cornelis networking in a scientific computing environment; it does not mean the announced CPU nodes contain AMD Instinct accelerators.
Application Testing Moves Beyond Compatibility
Cornelis says CN5000 is qualified for production with AMD EPYC 9005 processors across more than 25 validated platform configurations in North America and Europe. The companies have also extended CN5000 qualification to AMD Instinct MI350X Series accelerators.
For buyers, application testing can reveal whether an architecture fits the work that actually runs in their organization. A large simulation with frequent synchronization can behave differently from an AI workload that spends more time inside accelerator kernels. Memory capacity, software libraries, node configuration, and network behavior all influence the result.
Cornelis reports that a selected set of 132 CONVERGE computational fluid dynamics workloads achieved 20% higher average throughput on four nodes than a comparison using NVIDIA NDR InfiniBand. It also reports nearly ninefold scaling from one to eight nodes for a STAR-CCM+ workload running on EPYC 9755 processors.
These are vendor-reported application results from particular test configurations. The supporting Cornelis technical white papers are the relevant reference for test conditions; the figures do not establish the same advantage for every code or cluster. The practical value of the Munich testing program is that customers can assess their own workloads against that backdrop.
A Roadmap for Larger CPU and GPU Systems
The companies are also preparing for another hardware generation. Cornelis positions its upcoming CN6000 around an 800G adapter and a PCIe 6.0 x16 interface, with OEM systems combining CN6000 and AMD EPYC 9006 processors expected in the first quarter of 2027. Those systems are a roadmap item, separate from the deployed CN5000-based EMMY P4 infrastructure.
AMD’s accelerator strategy also includes the ROCm software stack, which provides tools and libraries for GPU computing. Supporting scientific AI requires that software layer as well as the processors and network: researchers need applications, mathematical libraries, and communication software that work together.
Traditional scientific computing also places substantial emphasis on double-precision, or FP64, arithmetic. That differs from the lower-precision calculations used in many AI workloads, so a platform’s suitability depends on more than its headline AI performance.
The expanded AMD–Cornelis partnership is ultimately a bid to make complete research systems easier to evaluate and deploy. GWDG supplies a production example of the CPU-and-network combination, while joint application testing and future accelerator support broaden the potential use cases. For research centers, the consequential measure is how quickly a validated system completes useful work—not how many processors it contains.
