Can hyperconverged architecture meet HPC demands in 2025?

L’architecture hyperconvergée face aux exigences du calcul intensif: can hyperconverged infrastructure really satisfy HPC workloads in 2025?

If you’re weighing hyperconverged infrastructure (HCI) for high‑performance computing (HPC), the short answer is: yes, in specific patterns—and no, for the most latency‑sensitive, tightly coupled jobs. This article clarifies where HCI fits, where it struggles, and how hybrid designs can align HPC needs with the operational simplicity of HCI. At Score Group, we help organizations navigate that decision through our Energy, Digital (Noor ITS), and New Tech (Noor Technology) pillars to balance performance, efficiency, and resilience.

At a glance

• HCI can serve “scale‑out” and data‑centric HPC (AI/ML, analytics, bioinformatics) but often falls short for tightly coupled MPI at scale.

• The winning pattern for 2025 is hybrid: HCI for control plane, data services, and general compute; dedicated HPC islands for ultra‑low‑latency jobs.

• Key enablers include RDMA fabrics, NVMe‑oF, GPU pools, and scheduler integration (e.g., Slurm + Kubernetes).

• Energy‑aware design—from facility cooling to workload placement—becomes a first‑class requirement for HPC clusters.

• Score Group aligns energy, digital infrastructure, and new tech to reduce risk and accelerate ROI without compromising scientific or engineering outcomes.

What HPC really demands in 2025

HPC needs have diversified. Alongside classic CFD/CAE and tightly coupled MPI codes, organizations now run AI training, inference at scale, and data analytics. This split matters because each class stresses hardware differently.

• Ultra‑low latency interconnects: Tightly coupled MPI jobs rely on microsecond‑level message passing and collective operations. Technologies like InfiniBand and RDMA are central to predictable performance at scale. See the InfiniBand Trade Association’s overview of the fabric’s characteristics for context (https://www.infinibandta.org/technology/).

• Accelerators and memory bandwidth: GPUs dominate AI/ML and many simulation workloads. Kubernetes includes GPU scheduling primitives (https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/), while HPC schedulers like Slurm remain standard for batch control (https://slurm.schedmd.com/overview.html).

• Throughput and I/O locality: Parallel filesystems and NVMe‑oF are used to feed data‑hungry tasks with minimal jitter. SNIA’s primer on NVMe‑over‑Fabrics is a useful reference (https://www.snia.org/educational-library/what-nvme-over-fabrics-2020).

• Energy efficiency by design: With exascale ambitions and rising power density, energy is a performance constraint. The U.S. DOE outlines why efficiency is foundational for modern supercomputers (https://www.energy.gov/science/ascr/articles/energy-efficiency-exascale-supercomputers).

• Scale and reliability: The TOP500 and Green500 lists—updated twice yearly—show accelerating use of accelerators and energy‑optimized design patterns (https://www.top500.org/lists/top500/, https://www.top500.org/green500/).

In HPC, it’s not just peak FLOPS that matter—it’s low‑variance latency, predictable I/O, and energy‑aware orchestration across the stack.

What hyperconverged infrastructure actually offers

HCI consolidates compute, storage, and virtualization/containers into unified nodes managed by software-defined control planes. Benefits include:

• Operational simplicity: One management plane for rolling upgrades, scaling, and lifecycle management.

• Elasticity and agility: Fast provisioning for dev/test, data services, and AI platforms; strong fit for containerized ecosystems.

• Software‑defined storage: Local NVMe with replication/erasure coding—good for resilience and general throughput.

• Modern offloads: Increasing use of DPUs/SmartNICs to offload storage/network virtualization from CPUs, improving determinism. See the Open Programmable Infrastructure initiative (https://opiproject.org/).

Limits in HPC contexts:

• Tightly coupled MPI: Virtualization overhead, storage abstraction, and east‑west node variability can introduce latency jitter.

• Fabric constraints: Many HCI stacks are optimized for Ethernet/IP; ultra‑low‑latency HPC may require specialized fabrics.

• Failure domain coupling: Converging compute+storage can hamper right‑sizing failure domains for large MPI jobs.

Decision matrix: HCI vs HPC needs (2025)

HPC requirement	Why it matters	HCI strengths	Gaps/mitigations
Tightly coupled MPI (CFD/CAE)	Microsecond latency, low jitter	Simple ops for control plane, small to mid-size jobs	Prefer dedicated HPC nodes with InfiniBand/RDMA; keep HCI for services/smaller jobs
AI/ML training	GPU density, data feeding	Rapid cluster bring‑up, container-first, GPU pools	Ensure RDMA/NVMe‑oF; integrate Slurm/K8s; consider DPU offload
High‑throughput computing (HTC)	Many independent tasks	Excellent fit; easy scaling and scheduling	Watch noisy neighbor effects; enforce QoS and isolation
Data pipelines/feature stores	I/O bandwidth, resilience	Strong SDS and snapshotting	Use RDMA/NVMe‑oF for hot paths; cache near GPUs
Energy efficiency	Cost, sustainability, power caps	Unified observability and automation	Facility-level optimization and energy-aware schedulers are essential

Where HCI fits—and where it doesn’t

Strong fit: AI/ML, HTC, and data-centric HPC

• AI/ML: Containerized training and inference platforms benefit from HCI’s rapid provisioning and lifecycle operations. Combine GPU scheduling in Kubernetes (https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/) with batch control via Slurm (https://slurm.schedmd.com/overview.html) for mixed workloads.

• HTC and parameter sweeps: Thousands of loosely coupled tasks map cleanly to HCI clusters with robust SDS, snapshots, and horizontal scaling.

• Data engineering: Streaming/ETL, feature stores, and MLOps backplanes often run well on HCI, especially when paired with NVMe‑oF (https://www.snia.org/educational-library/what-nvme-over-fabrics-2020).

Caution: Tightly coupled MPI at scale

• Latency and jitter: Virtualization layers, shared storage services, and east‑west traffic can introduce variability. InfiniBand or RoCE-based designs and careful NUMA/GPU topology control are mandatory in these cases (https://www.infinibandta.org/technology/).

• Failure isolation: Large MPI jobs prefer clean failure domains and deterministic fabrics. Use dedicated HPC islands for these workloads and connect them to HCI‑based data/control planes.

Architecture patterns that work in 2025

1) Hybrid control/data plane

• Run control services, logging, artifact registries, workflow engines, and general-purpose compute on HCI.

• Maintain a dedicated HPC partition for latency‑critical MPI jobs with InfiniBand or equivalent RDMA.

2) Storage designed for throughput and predictability

• Use NVMe‑oF for hot data paths to reduce CPU overhead and improve determinism (https://www.snia.org/educational-library/what-nvme-over-fabrics-2020).

• Keep snapshots and object storage on HCI; stage datasets to local NVMe close to GPUs/CPUs for active jobs.

3) Orchestration that spans Slurm and Kubernetes

• Batch jobs (Slurm) and services/pipelines (Kubernetes) can coexist. Integration patterns enable a single intake while honoring workload specifics (https://slurm.schedmd.com/overview.html, https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/).

4) Smart networking and offloads

• Adopt RDMA, QoS, and congestion control. SmartNICs/DPUs isolate storage/network functions, improving HPC job stability (https://opiproject.org/).

5) Emerging composability with CXL

• Compute Express Link enables memory and accelerator pooling across nodes. Expect maturing designs that blend HCI manageability with HPC‑class composability (https://www.computeexpresslink.org/).

6) Energy‑aware, facility‑to‑workload optimization

• Coordinate power/cooling envelopes with job placement and scheduling. ASHRAE provides data center thermal guidance that informs safe efficiency gains (https://www.ashrae.org/technical-resources/data-center-resources).

• The Green500 underscores why energy efficiency is now a core performance metric (https://www.top500.org/green500/).

Risks and anti‑patterns to avoid

• Over‑virtualizing MPI: Use bare metal or near‑bare‑metal for tight coupling; containerize thoughtfully with CPU pinning and topology awareness.

• Ignoring fabric design: Ethernet without RDMA/QoS can bottleneck collective ops; size east‑west bandwidth conservatively.

• One‑size‑fits‑all storage: Separate hot data paths from resilient backends; avoid forcing all I/O through SDS layers during peak jobs.

• Mixed failure domains: Keep large jobs away from nodes carrying heavy storage services to prevent noisy neighbor effects.

Governance, security, and continuity for HPC on HCI

HPC environments increasingly host sensitive IP, regulated datasets, and shared services. Robust governance is not optional.

• Security: Network micro‑segmentation, hardware roots of trust on accelerators, and supply‑chain validation for firmware.

• Continuity: Replicate metadata and critical services across HCI clusters; define clear RPO/RTO for research platforms and engineering toolchains.

• Observability: Full‑stack metrics—fabric, accelerators, power, and thermals—feed energy‑aware schedulers and capacity planning.

How Score Group helps (Energy, Digital, New Tech)

At Score Group, our role is to integrate—where efficiency embraces innovation—and to align architecture with outcomes:

• Noor ITS (Digital): Designs and integrates the hybrid stack—HCI clusters, HPC islands, fabrics, storage tiers, and scheduler integrations—while reinforcing cybersecurity and resilience.

• Noor Energy (Energy): Optimizes power and cooling, implements energy monitoring, and aligns facility SLAs with workload power envelopes.

• Noor Technology (New Tech): Industrializes AI/ML and data platforms, enabling reproducible pipelines and accelerator utilization.

• As an integrator, we tailor the mix rather than forcing a single paradigm. Explore our approach on our homepage (https://score-grp.com).

Implementation roadmap

1. Workload profiling: Classify jobs by coupling, latency sensitivity, I/O intensity, and accelerator needs.

2. Fabric and storage blueprint: Decide where RDMA/InfiniBand is mandatory; define NVMe‑oF tiers and data staging flows.

3. Orchestration model: Integrate Slurm with Kubernetes, set admission controls, and enforce placement policies.

4. Energy model: Correlate power/thermals with job classes; set thresholds and automation hooks.

5. Pilot and validate: Benchmark micro‑latency, I/O variance, and end‑to‑end job time-to-solution on representative datasets.

6. Operate and optimize: Continuous observability, cost/energy reports, and iterative right‑sizing of HCI vs HPC partitions.

FAQ

Is HCI fast enough for tightly coupled MPI workloads?

Sometimes, but not consistently at large scale. Tightly coupled MPI requires ultra‑low latency and low jitter across nodes. HCI adds abstraction layers (virtualization, software‑defined storage) that can inject variability. You can mitigate with RDMA fabrics, CPU pinning, and offloading storage/network functions to DPUs, yet most organizations still keep a dedicated HPC island for critical MPI jobs and use HCI for services, data staging, and less latency‑sensitive compute.

Can I run Slurm and Kubernetes together for HPC and AI?

Yes. Many teams run Slurm for batch scheduling of simulations while using Kubernetes for services, MLOps pipelines, and interactive notebooks. Integration patterns allow Slurm to launch containers or for Kubernetes to hand off batch jobs to Slurm. The key is clear workload taxonomy, GPU and NUMA‑aware placement, and shared observability. Official docs for Slurm and Kubernetes GPU scheduling outline baseline practices (https://slurm.schedmd.com/overview.html, https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/).

How do I make HCI storage “HPC‑friendly”?

Separate resilient backends from hot data paths. Use local NVMe for scratch and stage active datasets with NVMe‑over‑Fabrics to minimize CPU overhead and jitter. Keep snapshots, catalogs, and object stores on HCI SDS, while feeding GPUs/CPUs via RDMA‑enabled paths for critical jobs. SNIA’s NVMe‑oF resources provide architectural guidance (https://www.snia.org/educational-library/what-nvme-over-fabrics-2020).

What about emerging technologies like CXL and DPUs?

They are promising for bridging HCI and HPC needs. CXL enables memory/accelerator pooling and future composability across nodes, while DPUs/SmartNICs offload storage/network functions to improve determinism. Adoption is growing, but plan phased integration with careful benchmarking. See the CXL Consortium for roadmaps (https://www.computeexpresslink.org/) and the OPI Project for open offload ecosystems (https://opiproject.org/).

How important is energy optimization for HPC on HCI?

Critical. Energy is now a primary design constraint, not a secondary concern. Align facility capabilities (power and cooling) with workload scheduling, and apply energy‑aware policies that consider thermals, time‑of‑day tariffs, and performance targets. The Green500 shows efficiency leadership in HPC, while ASHRAE offers practical data center guidance (https://www.top500.org/green500/, https://www.ashrae.org/technical-resources/data-center-resources).

Key takeaways

• HCI fits AI/ML, HTC, and data‑centric HPC; tightly coupled MPI often benefits from dedicated HPC islands.

• Hybrid designs—HCI for services/elastic compute and separate HPC partitions for ultra‑low latency—are the 2025 sweet spot.

• Success depends on RDMA fabrics, NVMe‑oF, scheduler integration (Slurm+K8s), and energy‑aware operations.

• DPUs/SmartNICs and emerging CXL composability help reconcile HCI manageability with HPC determinism.

• Facility‑to‑workload energy optimization is now a core performance lever, not an afterthought.

• Ready to design your hybrid HPC/HCI strategy? Talk to Score Group—integrating Energy, Digital, and New Tech for resilient performance (https://score-grp.com).