How IBM Storage Scale Delivers Performance at the Intersection of AI and HPC

Often organizations assume that AI and High Performance Computing (HPC) require separate infrastructures to support them. However, modern storage architecture must be capable of scaling over time to support both technologies.

To meet the demands of AI and HPC, storage infrastructure needs to handle multiple dimensions of performance. IBM Storage Scale provides an architecture designed to handle mixed AI and HPC workloads without forcing infrastructure tradeoffs.

Why AI and HPC Workloads Are Converging

AI and HPC systems handle workloads that exhibit different behavior patterns. However, these workloads share fundamental storage requirements, leading the two technologies to converge. AI model training and inference workloads have the same storage demands as scientific simulation, analytics, and research workloads in HPC environments. Many HPC workloads are becoming AI-driven.

Understanding the Different I/O Patterns Behind AI and HPC

HPC and AI workloads show different Input/Output (I/O) patterns. While HPC workloads are phase-driven, AI workloads tend to be more pipeline-driven.

Traditional HPC workloads exhibit long compute cycles, periodic checkpoint write bursts, and high-bandwidth restart reads and analysis. AI workloads involve continuous read-heavy data ingestion, small files and metadata operations, and sensitivity to latency and pipeline stalls.

Five Storage Performance Dimensions for AI and HPC

Despite these differences in I/O patterns, HPC and AI workload requirements share dimensions. The five mutual performance dimensions include:

• Bandwidth, the maximum amount of data that can be transmitted over a network or internet connection in a given amount of time
• I/O Operations Per Second (IOPS), the metric for measuring speed and performance of storage
• Metadata Performance, how efficiently a system discovers, reads, catalogs, and indexes data about your data
• Latency, the time delay between a stimulus or request and the corresponding response in a system
• Concurrency, an application’s ability to execute multiple tasks or processes seemingly at the same time

Because HPC and AI aren’t defined by a single access pattern, storage systems must perform across all these dimensions to succeed. Optimizing storage architecture to meet one benchmark seldom produces optimal real-world enterprise performance for AI and HPC workloads.

Why Concurrency Is the Real Architectural Advantage

IBM Storage Scale is designed to support the idea that concurrency is the primary architectural characteristic of storage instead of a side effect of throughput. By embracing the significance of concurrency, IBM Storage Scale performs well across HPC and AI systems.

Concurrency supports parallelism across clients, CPU cores, metadata operations, I/O queues, network paths, and storage devices. Instead of optimizing a single metric, Storage Scale enables many operations to be conducted at the same time without creating bottlenecks.

How IBM Storage Scale Supports AI and HPC on a Single Platform

Traditionally, organizations have deployed separate storage systems for AI and HPC workloads. While one system was optimized for HPC throughput, the other was tuned for AI pipelines. This approach created challenges, such as data duplication, operational complexity, and increased costs.

IBM Storage Scale provides a single platform that supports both AI and HPC. Within one architecture, users take advantage of high bandwidth and IOPS, scalable metadata, and consistent latency under load. High aggregate bandwidth supports HPC checkpoint bursts, while high metadata rates and IOPS meet the demands of AI training pipelines. Low latency, highly concurrent access meets the performance requirements of inference workloads.

With Storage Scale, organizations eliminate storage silos while improving utilization and simplifying infrastructure management.

Blue Vela: A Real-World Example of Mixed AI and HPC Workloads

IO500 testing of IBM’s Blue Vela deployment demonstrates how Storage Scale performs under real production conditions. Blue Vela is a production environment used for training Large Language Models (LLMs) and AI foundation models, as well as running HPC and research workloads.

IO500 is a meaningful benchmark for HPC storage because it captures both bandwidth-heavy operations and metadata and small I/O workloads. Strong results indicate that a system can perform well across dimensions.

While the IO500 benchmark test was run on approximately 2.6% of the cluster, the remaining 97% or so was running production workloads at the same time. The success of Blue Vela demonstrates that Storage Scale performance can be sustained under real multi-tenant, mixed-workload conditions required by enterprises and research institutions.

Key Architectural Features That Maintain Performance Under Load

The Blue Vela configuration has architectural components that show how Storage Scale enables consistent performance for both AI and HPC workloads.

Parallel Shared Namespace

AI and HPC workloads operate on a unified namespace, eliminating data silos and allowing sharing between pipelines.

Distributed Metadata

Storage Scale is designed to scale metadata-intensive workloads through parallel client access, distributed placement, and metadata services that don’t rely on a single controller, preventing bottlenecks.

NVMe + Balanced System Design

The system combines NVMe-based storage with balanced networking and compute to enable high bandwidth for HPC bursts and high IOPS for AI pipelines.

No Burst Buffer Dependency

Results of the IO500 test noted that Storage Scale not only produced a high benchmark result but that the primary file system absorbed the workload.

Built-In Resilience

The system maintains performance even under failure conditions through erasure coding and redundancy.

Business Benefits of Consolidating AI and HPC Storage

As IT leaders evaluate future AI infrastructure investments, they need to consider which modern storage platforms provide the flexibility needed as AI, analytics, and HPC workloads continue to evolve and converge. Deciding on a unified storage architecture for AI and HPC workloads contributes to organizational efficiency, scalability, and long-term ROI.

Consolidating AI and HPC storage using IBM Spectrum Scale translates into measurable business outcomes, including lower infrastructure costs, reduced operational complexity, simplified data management, improved resource utilization, and accelerated AI development.

How to Build Modern IBM Storage Scale Environments

AI and HPC are increasingly becoming different expressions of the same storage infrastructure challenge. Companies need to evaluate whether their current storage architecture is prepared for the performance demands of both AI and HPC workloads.  

Re-Store specializes in developing IBM-powered HPC environments. Using our understanding of trends toward AI-driven HPC workloads, we can help organizations design, deploy, and optimize IBM Storage Scale solutions to support AI and HPC.  

Explore how to meet the performance demands of both AI and HPC workloads. Ask for a consultation(opens in new tab) with a Re-Store expert.