Managing large datasets

Understanding the Nature of Large Datasets in HPC

In high performance computing, “large” usually means datasets that are too big to fit comfortably in a single node’s memory, or even on a single storage device. Managing such datasets is not just about having enough disk space. It is about structuring data and workflows so that input, output, movement, and long term storage do not dominate the total time and cost of a project.

Large datasets in HPC often arise from three main sources. They can be produced by simulations, for example climate models, molecular dynamics, or fluid dynamics codes. They can be collected by instruments, for example satellites, telescopes, microscopes, and sensors. They can be generated by analysis workflows themselves, for example large ensembles of Monte Carlo runs, machine learning training data, or feature extraction from raw observations.

Managing these datasets effectively in HPC environments requires thinking about the whole lifecycle. This includes acquisition or generation, organization on storage, access during computation, intermediate and final outputs, sharing and collaboration, and long term preservation or deletion. The goal is to make data usable at scale without overwhelming file systems, network links, or human users.

Data Layout and Organization on Parallel Filesystems

Parallel filesystems such as Lustre and GPFS are designed to serve many clients concurrently. For large datasets, how you organize and lay out files on these systems directly affects performance and reliability.

A common issue in HPC is the “small files problem”. Many applications produce millions of tiny files, for example one file per timestep per MPI rank. Surprisingly, this can be much worse than producing fewer, larger files of the same total size. Metadata operations such as file creation, open, close, and stat are relatively expensive and can overload the metadata servers of a parallel filesystem. As a result, directory listings slow down, backups become difficult, and job runtimes increase even before any useful data is read or written.

A better strategy is to reduce the number of files and increase their size where possible. This is often achieved by having a subset of processes perform collective I/O, aggregating data from many ranks into shared files. This approach ties into parallel I/O libraries and formats, but from a dataset management perspective the key idea is to avoid file explosion. For example, instead of output_rank00001_step0001.dat for every rank and step, you might design output_step0001.h5 that contains structured datasets for all ranks.

Directory structures also matter at large scale. A single directory containing millions of files becomes problematic for both performance and usability. Many HPC centers recommend a hierarchical layout that groups files by date, simulation campaign, or other logical units. For instance, a path such as project/experiment1/run0005/step0001/ is easier to browse and archive than a flat directory with all runs mixed together. It also helps keep per directory file counts manageable.

Striping parameters on parallel filesystems are another important aspect. Some systems let you choose how many storage targets a file is striped across and what stripe size to use. Large sequential I/O to very big files often benefits from wider striping and larger stripe sizes. Conversely, many small concurrent I/O streams may overload a few targets if striping is misconfigured. In practice, HPC centers often provide default recommendations, but for very large datasets you should understand and intentionally set striping for key files, especially checkpoints and major outputs.

Naming, Versioning, and Metadata Practices

Large datasets are valuable only if you can interpret them later. Good naming, versioning, and metadata practices are essential to avoid confusion and data loss.

File naming should encode just enough information to identify the contents without becoming unreadable. Common elements include project name, run or experiment ID, date or simulation time, and a version or revision tag. For example, climate_RCP45_run12_timestep_010000_v2.nc conveys more context than output.dat, yet still follows a regular pattern. Regular patterns are important because many HPC tools, scripts, and workflows rely on simple string matching and globbing to locate and process groups of files.

Versioning is particularly critical for large datasets that are expensive to regenerate. Small code changes or parameter tweaks can create incompatible outputs. Instead of overwriting old files, it is often safer to keep explicit versions, either encoded in filenames or through a directory structure. For instance, you might have run12/v1/, run12/v2/ and so on. Coupled with a run log, this ensures that you can trace exactly which input settings produced which dataset.

Metadata sits alongside filenames and provides richer descriptions. For large datasets, relying on filenames alone is not enough. Many scientific file formats can store metadata within the files, such as variable names, units, grid descriptions, and provenance information. However, there is also value in project level metadata that lives outside any single file. Simple solutions include structured text files, such as README, runlog.txt, or manifest.json, located at the top of a dataset directory. These can record creation times, software versions, compiler flags, cluster names, and SLURM job IDs. More sophisticated approaches use databases or data catalogs, but even a well designed set of plain text metadata files can dramatically improve manageability.

Consistent timestamps and time zones are another subtle, but important, detail. Large projects often involve runs performed at different times and on different systems. When recording metadata about datasets, especially when recording when a dataset was produced or copied, be explicit about time zones or use UTC consistently. Mismatched timestamps can lead to confusion about which dataset is the latest or which one was used for a particular publication.

Hierarchical Storage Management and Data Tiers

HPC centers rarely store all data on a single type of storage. Instead, they organize storage into tiers with different performance, capacity, and cost characteristics. Large datasets usually must move across these tiers during their lifetime.

At the top tier is fast, relatively small storage, such as NVMe based scratch or a high performance parallel filesystem. This tier is optimized for high bandwidth and low latency I/O and is intended for active computations. Below that, there may be larger, slower project or home filesystems that are suitable for long term working data and source code. At the lowest tier, there may be archival storage systems, often based on tape or object storage, that offer huge capacity but slow access.

Managing large datasets means designing workflows that respect these tiers. For example, raw output from a big simulation might first be written to scratch storage. Once the simulation completes, a postprocessing job could convert and compress key results, then move them to project storage for analysis. If the raw outputs are needed only rarely, they can be archived to tape and removed from scratch to free space. This tiered approach prevents fast and expensive storage from being filled with inactive data.

Hierarchical storage management systems sometimes automate movement between tiers, but you should not rely on automation alone. You need to understand policies like scratch purge rules. Many centers automatically delete files from scratch after a fixed period of inactivity, such as 30 or 90 days. For large datasets, failing to plan for these policies can result in silent data loss. A common pattern is to include explicit archival steps in your job workflows so that important datasets are safely stored before purge deadlines.

Designing datasets for tiered storage can also influence file formats and chunking. For example, you might store raw, high resolution outputs in a format that is efficient for writing during simulation, and then create downsampled or derived datasets in a format that is more compact and portable for long term storage. High level data formats that support compression and chunked storage can make this dual role easier, since the same container can hold both full resolution and reduced products.

Data Reduction, Compression, and Subsetting Strategies

For very large datasets, storing every possible detail is rarely practical or necessary. Data reduction and compression are central tools for managing size while preserving scientific value.

Lossless compression aims to reduce storage size without changing data values. General purpose compressors, such as gzip, can be used, but many scientific formats include built in compression filters that work on chunks of arrays. These filters can exploit patterns and redundancy in numeric data more effectively. Layout choices, such as chunk size and shape, matter because they determine how well the compressor sees structure in the data and how efficiently future reads can access subsets.

Lossy compression trades exact reconstruction for higher compression ratios. In many scientific contexts, data already has some measurement or discretization error, so a controlled additional error may be acceptable. For example, wavelet based or floating point quantization based compressors can reduce dataset size by factors of ten or more while preserving key statistical properties. In HPC workflows that produce petabytes, such reduction can make the difference between a manageable and an impossible dataset. However, any lossy method must be carefully validated for its impact on downstream analysis. It is important to treat compression settings as part of the scientific protocol and record them in metadata.

Subsetting is another powerful reduction technique. Instead of storing every timestep, 3D field, or variable, you can decide which parts are actually needed for analysis. For instance, you might keep full spatial fields every 100 timesteps and coarser, spatially averaged diagnostics for each timestep. Similarly, not every intermediate variable in a simulation or analysis pipeline needs to be stored. Many can exist only in memory during computation and be discarded afterward. Evaluating what must be persisted and at what resolution is a key design decision in managing large datasets.

Derived products and summarization also help. For some users, statistics such as means, variances, and principal components may be sufficient, even if you retain full fields only for a subset of the domain or time period. In practice, a multi tier data strategy often emerges, with a small, highly curated dataset for broad distribution, a medium sized dataset for routine analysis, and a much larger raw dataset kept in deep archive or entirely discarded once its role is fulfilled.

Concurrency, Access Patterns, and I/O Contention

Large datasets in HPC environments are often accessed by many processes concurrently. Poorly planned access patterns can saturate storage systems, creating bottlenecks for all users and, in some cases, leading to job failures.

Concurrency means that multiple processes, nodes, or jobs read and write data at the same time. For a single dataset, concurrency can be internal, such as many MPI ranks writing different parts of a global array, or external, such as many independent jobs reading the same input files. In either case, the storage system must schedule a high volume of operations.

Access patterns have a major influence on performance. Large, contiguous reads and writes are more efficient than many small, scattered operations. Random access to tiny chunks of large files generates overhead that often dominates the actual data transfer time. When designing data structures, consider grouping data that is accessed together into contiguous blocks. Chunked storage layouts in high level formats allow you to tune chunk shapes to match typical slicing operations in analysis.

From a dataset management perspective, one of the most disruptive patterns is many jobs launching simultaneously and all reading the same large input files from a shared filesystem. This can cause a “thundering herd” effect, where metadata and I/O servers are overwhelmed. Techniques to mitigate this include staging commonly used datasets to local storage on compute nodes, using broadcast or caching mechanisms, or staggering job start times. Some centers provide shared read only data collections that are optimized and cached for heavy concurrent access.

I/O contention is not just a performance issue. When too many processes hammer the filesystem, individual operations may fail or time out. For example, checkpoints might be incomplete, or applications might crash on I/O errors. Managing large datasets therefore includes designing I/O phases in applications and workflows so they are as short, efficient, and coordinated as possible. In many cases, it is beneficial to separate heavy I/O into dedicated jobs, for example postprocessing or aggregation tasks, that run after a computation and transform many small outputs into a more manageable form.

Local Staging, Caching, and Data Movement

When datasets are large, moving them becomes a major cost. Each transfer across the network consumes time and bandwidth, and repeated transfers multiply this cost. Effective management minimizes unnecessary data movement and exploits locality whenever possible.

Local staging refers to copying a subset of data from a shared filesystem to local storage on a compute node or a small group of nodes before running a job. This reduces repeated remote reads and can drastically lower contention on the main filesystem. For example, if many tasks in a workflow reuse the same static reference data, it can be efficient to copy this data once per node and then have local tasks read it from a fast node-local directory.

Caching strategies build on the same idea. Instead of naively copying files each time, scripts can check whether a valid cached copy already exists and reuse it. Simple checksum or timestamp checks can ensure consistency. More sophisticated systems might use distributed caches or data management software, but even a small amount of scripting can make a large difference for frequently accessed datasets.

Data movement across sites is a special challenge. Large datasets may need to move between an instrument facility and an HPC center, between two HPC centers, or from an HPC center to institutional storage. Standard tools like scp are rarely sufficient at multi terabyte scales. Instead, you might use parallel transfer tools and data transfer nodes that are optimized for high throughput. Although the detailed tools vary by center, the dataset management principle is the same: treat transfers as part of the workflow, schedule them explicitly, monitor their progress, and verify integrity using checksums or similar mechanisms.

Within a single site, you should plan when and how data is moved between storage tiers. Scripts to automate data promotion and demotion are valuable. For example, at the end of each simulation campaign, a script might identify important result directories, create tar archives, compute checksums, transfer the archives to tape, verify checksums at the destination, and finally remove the original directories from scratch storage. Automating these steps reduces human error and ensures that policies are applied consistently.

Data Integrity, Validation, and Backup Strategies

Large datasets are often the product of expensive compute campaigns or unique experimental opportunities. Data loss or corruption can therefore be extremely costly. Managing large datasets includes explicit measures for data integrity and backup.

Data integrity has two aspects: detecting corruption and avoiding silent errors. Storage systems do fail, and bit flips or misbehaving hardware can damage files. For large datasets, you should not assume that “no error reported” means “data is correct.” Instead, use checksums or hashes to create compact fingerprints of files or archives. Common tools compute MD5 or SHA series hashes. Storing these checksums alongside the data allows you to verify files after transfer, after backup, or periodically during long term storage.

Validation goes beyond bit-level integrity and checks that data contents make sense. For example, you might implement simple sanity checks, such as verifying that physical quantities stay within plausible ranges, that the number of timesteps matches expectations, or that keys and attributes are present in metadata. These checks can be embedded into postprocessing scripts or quality control jobs that run automatically after data is produced. For very large datasets, it is often impractical to perform full validation for every element, but even sampling-based checks can catch gross problems early.

Backup strategies must take into account both volume and access patterns. Mirroring entire large datasets across multiple systems may be untenable. Instead, you may choose to back up only irreplaceable raw data and key processed products, while derived, easily reproducible outputs are regenerated on demand. Careful classification of datasets by their importance and reproducibility helps prioritize backup resources.

In HPC environments, tape archives often serve as the last line of defense. Tapes are suitable for long term, infrequently accessed data. However, restore times can be long, and archives may be write once or append only. For this reason, it is helpful to treat archival datasets as immutable snapshots, with clear versioning and descriptive metadata. When data needs to be updated, it is usually better to create a new snapshot than to modify the old one in place.

Project-Level Organization and Collaboration

Large datasets rarely belong to a single person. They are usually shared across teams, projects, and sometimes entire communities. Effective dataset management must therefore consider access control, documentation, and collaboration practices.

On shared HPC systems, project directories typically have group permissions that allow multiple members to read and write. For large datasets, access control must balance safety with usability. Granting write permissions to everyone can introduce the risk of accidental deletion or modification. A common pattern is to set directories that contain final, published, or archived datasets as read only for most users, while keeping separate working areas for active development. Role-based permissions, when available, help formalize this separation.

Documentation is arguably the most important collaborative tool. For each major dataset, maintain clear, human-readable documentation in the same directory tree. This documentation should answer questions like what the dataset represents, how it was generated, which software and versions were used, what each subdirectory contains, and how to interpret naming conventions. Even simple README files can save enormous time for collaborators who are not familiar with the project’s internal conventions.

For very large projects, it is often helpful to build a lightweight internal catalog of datasets. This might be a spreadsheet, a small database, or a web-based interface that lists datasets, their locations, sizes, creation dates, versions, and responsible contacts. The catalog does not need to store the data itself. It serves as a searchable index that points to where data lives on the filesystem or in archives. As datasets grow in size and number, such a catalog becomes essential for avoiding duplication and for tracking dependencies between simulations and analyses.

Finally, consider how datasets will be shared beyond a single HPC system. Journals and funding agencies increasingly require data availability for reproducibility. Large HPC datasets are often too big to attach directly to publications. In such cases, you may publish a curated subset to a community repository and maintain the full dataset at an HPC center or institutional archive. In all cases, plan for how future users will discover, access, and understand your data, even if you are no longer available to explain it.

Planning Data Lifecycles in HPC Workflows

Managing large datasets effectively is easiest when considered from the start of a project, not after the first petabyte has been written. A data lifecycle plan describes how data will be created, transformed, stored, moved, and eventually retired.

A basic lifecycle for an HPC simulation project might look like this. First, input data and configuration files are prepared and stored in a project directory. Next, simulation runs produce raw output to scratch storage. Immediately after or in scheduled phases, postprocessing jobs reduce, compress, and transform outputs into analysis-ready formats, storing them in a more persistent project space. At defined milestones, key datasets are archived to long term storage with associated checksums and documentation. Derived analysis products, such as plots or statistical summaries, may be stored separately, with clear links back to the source data and configurations. After a certain period, and once archival copies are verified, very large raw or intermediate datasets may be deleted from active storage.

Throughout this process, you should estimate data volumes and I/O needs. Rough calculations can prevent surprises. For example, if each timestep produces 10 GB of data and a run has 10,000 timesteps, the raw output will be roughly 100 TB. If you plan to keep ten such runs, the scale is 1 PB just for this class of data. These estimates inform decisions about reduction strategies, archival policies, and whether additional storage allocations are required.

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Before running large campaigns, always estimate expected data volumes, design a concrete plan for where data will live at each stage, and ensure that storage and I/O policies at your HPC center support that plan.
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By treating data as a first-class citizen in your workflows and making conscious decisions about layout, reduction, movement, integrity, and collaboration, you can manage large datasets in HPC efficiently and sustainably, while preserving the scientific value that motivates their creation.