High-Performance Computing (HPC) has become a critical tool in scientific research, engineering, financial modeling, AI training, and more. While compute power continues to grow, many organizations find themselves limited not by the processors they deploy, but by how efficiently they can move, access, and manage data.
Data is the lifeblood of modern HPC, but it’s also one of its biggest bottlenecks. As systems scale, workflows become more complex, and datasets grow to petabyte levels and beyond, the need for high-throughput, low-latency, and intelligently orchestrated data infrastructure becomes impossible to ignore.
Here are some of the most impactful performance challenges affecting data workflows in HPC and how rethinking your infrastructure can help overcome them.
#1 Compute Starvation from Slow Data Feeds
Today’s HPC systems are increasingly built around powerful compute resources—especially GPUs—capable of processing massive volumes of data in parallel. But these systems are only as effective as the pipelines feeding them.
In many environments, storage simply cannot keep up with demand. Bandwidth limitations, high latency, or constrained I/O paths mean that GPUs sit idle waiting for input data to arrive. This is especially damaging in AI and simulation workflows, where compute is expected to work continuously and iteratively on large-scale datasets.
The result? Wasted compute capacity, slower time-to-results, and an overall reduction in ROI from expensive hardware investments. Alleviating this requires a storage layer specifically optimized to deliver sustained throughput with low-latency responsiveness—especially under concurrent access.
#2 Poor I/O Scaling Under Concurrency
One of the defining characteristics of HPC workloads is their scale. Jobs routinely span hundreds or thousands of compute nodes, all needing concurrent access to shared data. Without a storage backend built for true parallelism, these environments encounter serious contention.
Standard enterprise file systems often crumble under the pressure of massive parallel I/O. As the number of clients grows, I/O performance degrades leading to slower job execution, missed SLA windows, and underutilized compute resources. The impact is particularly noticeable in tightly-coupled MPI applications and distributed deep learning, where I/O bottlenecks can impact coordination between processes.
The solution lies in deploying storage systems that can scale I/O performance linearly with client load, ensuring predictable, sustained throughput regardless of cluster size.
#3 Siloed Storage Across Projects and Sites
In many HPC organizations, data ends up fragmented across multiple storage systems—scratch spaces, home directories, departmental NAS shares, legacy archives, or even geographically distant sites. Each one is often managed independently, with its own authentication, access controls, and interface.
This fragmentation leads to data duplication, inconsistency, and confusion. It also impairs collaborative research, as users struggle to locate or share relevant datasets, and developers waste time writing custom access logic. In worst-case scenarios, valuable data is simply “lost” in the system—not deleted, but practically unreachable.
A unified storage environment, ideally with a global namespace and centralized data cataloging, eliminates these barriers. It enables data reuse, reduces management overhead, and improves the efficiency of every research or simulation workflow.
#4 Manual and Rigid Data Workflows
HPC workflows are often built on years of homegrown tools, shell scripts, and legacy batch jobs. While functional, these methods are brittle, difficult to scale, and highly dependent on tribal knowledge.
A common example: datasets are manually copied to scratch space for compute jobs, then moved back (or archived) manually after processing. This approach introduces human error, delays, and inefficiencies — particularly when jobs fail, restart, or need to dynamically adjust data placement.
Modern HPC environments require orchestration platforms that automate data movement intelligently. Ideally, data should move seamlessly and transparently between ingest, processing, and archive stages, guided by job schedulers or access policies, not ad hoc scripting.
#5 Inefficient Tier-0 Utilization
High-performance NVMe storage tiers are vital to feed compute but they’re also expensive and finite. Yet in many environments, Tier-0 storage becomes cluttered with stale or inactive data because there’s no automated mechanism to move it elsewhere.
This leads to either: 1) paying for unnecessary expansion of high-cost storage, or 2) asking users to manually manage their own data lifecycle. Both poor outcomes.
Tier-0 should be reserved for active, high-priority data. Everything else—cold datasets, completed jobs, intermediate files—should automatically move to lower-cost, lower-performance tiers (e.g., HDD or object storage). The trick is doing this transparently, without breaking access paths or introducing friction.
#6 No Unified Namespace Across Data Tiers
When data moves between scratch, home, archive, and cloud, it often changes paths, protocols, or access methods. Users then need to know where the data lives, and how to get to it, adding unnecessary complexity to every workflow.
The lack of a unified namespace also impacts automation and scripting. Every change in storage tier might require changes to job scripts or data paths, which slows down teams and introduces fragility.
A single, global namespace across all tiers allows data to move freely while remaining consistently addressable. This simplifies application development, reduces user confusion, and enables truly seamless data orchestration behind the scenes.
#7 Archived Data is Practically Inaccessible
Data archiving is essential in HPC—both for cost control and long-term preservation. But traditional archive systems often turn into data graveyards: cold, slow, and difficult to search or retrieve from.
The problem is not just speed; it’s integration. Archived data is typically removed from the main namespace and stored separately. Reusing it requires special tools, IT intervention, or data duplication. In AI and research workflows, this is a major limitation. Past training runs, simulation results, and reference datasets must be quickly retrievable, especially when tuning models or repeating experiments.
A modern approach treats archive as a dynamic extension of the active data environment—instantly accessible when needed, and entirely transparent to the user or application.
#8 Data Lock-In Limits Agility and Collaboration
As HPC environments evolve, so do data usage patterns—cross-institution collaboration, hybrid cloud bursts, and AI workflows that span on-prem and cloud. But too often, storage systems create data lock-in through proprietary formats, closed protocols, or cloud-specific tools.
This limits your ability to adapt, scale, or share data freely. Moving data between platforms becomes complex, costly, or even infeasible. Lock-in not only stifles innovation but also increases long-term TCO and risk.
HPC platforms should prioritize open standards, portable data formats, and cloud-neutral orchestration. Data should be free to move—to wherever it’s needed—without rewriting code, losing metadata, or paying punitive egress fees.
How DataCore Helps You Break Through HPC Data Bottlenecks
Tackling the data challenges that limit HPC performance requires more than just faster hardware or incremental fixes—it takes a unified data platform designed to move at the pace of compute. DataCore Nexus delivers exactly that.
Built by combining the proven capabilities of Pixstor for high-performance file services with Ngenea for intelligent data orchestration, Nexus provides a complete data infrastructure optimized for demanding HPC workflows. It ensures that data is always where it needs to be—delivered with the throughput, concurrency, and flexibility needed to keep your compute resources fully utilized.
Did you know?
DataCore Nexus can deliver up to 180 GB/s read throughput and high IOPS—all in a compact 2U form factor designed for space-efficient, high-performance HPC environments.
Nexus streamlines operations by automating data movement across tiers, eliminating the need for manual staging, scripting, or cleanup. It simplifies collaboration and data reuse with a single, consistent namespace that spans across projects, teams, and even geographically distributed sites. And with support for open standards and multi-site deployments, it gives you the freedom to scale without lock-in—whether on-premises, in the cloud, or both.
For environments that need to retain large volumes of historical HPC data, DataCore Swarm complements Nexus with cost-effective, scalable archive storage that keeps older datasets accessible for recall, analysis, or re-use—without slowing down your active workflows.
Together, DataCore Nexus and Swarm provide a powerful, integrated solution to modern HPC data challenges—delivering the performance, agility, and simplicity needed to accelerate insight and maximize your infrastructure investments.
Contact DataCore to learn how Nexus can power your HPC workflows with the speed, scale, and efficiency they demand.
