Typical HPC workflows

From Idea to Results on an HPC System

A typical HPC workflow is the sequence of steps that takes you from a scientific or engineering question to trustworthy, reproducible results produced on a cluster. While details vary by domain, most workflows share a common structure. Understanding this structure helps you plan your work, estimate time and resource needs, and avoid common bottlenecks.

This chapter focuses on how these steps connect into a practical, end to end workflow on a real HPC system, not on the low level details of each step, which are covered elsewhere.

Phase 1: Problem Definition and Planning

An effective HPC workflow begins before you log in to a cluster. You start by clarifying what you want to compute and what constraints you face.

The first task is to identify what outputs you need. For example, you might want time series of temperature fields from a simulation, a set of optimal design parameters, a trained model, or a high resolution visualization. The type and amount of output deeply influence the rest of the workflow, including I/O patterns, storage needs, and postprocessing tools.

Next, translate scientific or engineering requirements into computational ones. You choose numerical methods or software packages, estimate spatial and temporal resolution, and identify whether the workload is embarrassingly parallel, tightly coupled, or somewhere in between. This is where you roughly estimate the problem size, such as number of degrees of freedom, matrix sizes, or dataset sizes.

At the planning stage you also sketch how your workflow will map onto the HPC environment. You decide whether to use an existing application or write your own, which languages and libraries you will rely on, and what parallel model you will use. These choices determine what modules, containers, or software stacks you will need on the cluster.

A good plan includes time for incremental scaling. You expect to run small tests, medium scaling experiments, and only then full scale production. This phased mindset is central to real HPC workflows and prevents expensive failures late in the process.

Phase 2: Local Development and Prototyping

Although clusters are where large computations run, many workflow steps begin on a local machine.

You usually explore algorithms, data layouts, or analysis scripts on a laptop or workstation, where iteration is quick. During this phase you make decisions about input formats, configuration schemes, and diagnostic outputs. You aim to create a minimal working version of the workflow that operates on tiny test cases.

In a typical workflow, you develop and test:

Source code for the core computation or scripts that drive an existing application.

Configuration files that specify model parameters, numerical tolerances, and output controls.

Input generators that synthesize small test inputs or preprocess raw data into the formats required by the main code.

Postprocessing scripts that can already handle small result files and produce preliminary plots or metrics.

You also set up basic software environment descriptions, such as a list of required libraries, compiler versions, and Python packages. Even if you later switch to modules or containers, having this list helps with reproducibility and reduces friction when you move to the cluster.

Local prototyping focuses on correctness and clarity, not performance. The main goal is to ensure that the workflow is logically complete and that every piece from input to output is present and functioning for tiny problem sizes.

Phase 3: Porting the Workflow to the Cluster

Once you have a locally working prototype, you port it to the HPC environment. This stage converts an abstract workflow into something that can run on login and compute nodes managed by a scheduler.

The first part is setting up the environment on the cluster. You determine which installed compilers, libraries, and applications match your needs. You may use environment modules or containers to enable reproducible setups. This step often involves slight adjustments to paths, compiler names, or library links, relative to your local environment.

For codes you develop yourself, you adapt the build system to the cluster. You configure compilers and optimization flags that fit the architecture. At this stage you usually compile a debug variant and an optimized variant so that you can switch between them without repeatedly reconfiguring the build.

Next, you adapt your workflow scripts to the scheduler. You convert direct local executions into batch jobs. For example, where you previously ran ./mycode config.in, you now write a job script that loads modules, sets environment variables, calls mpirun or srun, and redirects output. Submission, monitoring, and cancellation strategies are integrated into your workflow scripts, so you do not manage jobs manually for each run.

Data placement is another crucial part of porting. You decide which directories to use for input, output, scratch data, and long term storage. You locate fast scratch filesystems for temporary intermediates and plan how results will be moved to project or archival storage when runs complete. Good workflows include scripted data movement so that manual copying is minimized.

Finally, you verify that the basic workflow runs end to end on the cluster using very small problem sizes. The emphasis is on making sure the environment and scheduling parts work properly, not on performance or scaling.

Phase 4: Small Scale Test Runs

After the workflow is functional on the cluster, you perform small test runs that mimic the full workflow but at reduced scale. These runs serve multiple purposes.

First, they confirm correctness in the new environment. You check that numerical results match your local tests within expected differences, that random seeds behave as intended, and that I/O formats are identical. Any environment dependent bugs, such as library mismatches or file path issues, are usually discovered here.

Second, you validate the job configuration. You learn whether your job scripts correctly request resources, whether output and error logs are captured as expected, and whether the code starts and finishes without hangups or premature termination. This step includes verifying that environment variables that control threading, MPI behavior, or GPU usage are properly set.

Third, you begin to gather crude performance information. You inspect wall clock times from the scheduler, memory usage reports, and disk usage after the job. Even small runs can reveal whether your memory footprint scales as you expect or whether certain operations dominate runtime. Based on this, you can refine input parameters and plan future allocations more accurately.

Small test runs are an opportunity to refine diagnostic outputs. You may add lightweight logging, sanity checks on intermediate results, or integrity checks on output files. These features can later help detect silent failures in large production runs.

Phase 5: Scaling Studies and Resource Sizing

Before launching large scale production jobs, most HPC workflows include a deliberate scaling and sizing phase. The primary goal is to map problem size to resource usage in a way that is both efficient and compatible with allocation limits and queue policies.

In practice you perform a series of runs where you vary the number of cores, nodes, or GPUs while keeping the problem size fixed for strong scaling tests. You measure wall time and parallel efficiency. This reveals diminishing returns and helps you choose a sweet spot where additional resources still provide enough speedup to justify their use.

You may also perform weak scaling style tests where you grow the problem size with the resources. This gives insight into how memory usage, I/O volume, and time per degree of freedom change as the problem grows. These results inform decisions about maximum feasible problem sizes within the cluster limits and your project allocation.

During scaling studies you pay attention to resource related metrics such as maximum memory per process, I/O throughput, and initialization overheads. For example, you might discover that reading input data dominates runtime at low core counts but becomes less critical when parallel work grows. Conversely, you might discover that output writing time grows faster than expected.

The outcome of scaling and sizing studies is a set of concrete rules that tie input parameters to resource requests. For instance, you might deduce that a mesh with $N$ cells needs at least $M$ gigabytes of memory per node, or that you obtain acceptable efficiency up to $P$ processes. These empirical relationships become part of the workflow documentation and are reused for future projects.

Phase 6: Production Runs and Job Campaigns

With scaling results in hand, you move to production. At this stage the workflow typically shifts from individual job submissions to structured job campaigns.

A production campaign is a collection of jobs that cover the parameter space of interest. For example, you might vary several physical parameters, run multiple realizations for uncertainty quantification, or sweep over design variables. You design a plan that specifies which combinations to run, how many repeats are needed, and whether jobs are independent or depend on each other.

To manage these campaigns you often develop driver scripts or use simple workflow managers. These tools generate job scripts from templates, substitute parameter values, submit batches of jobs, and track their status. They help avoid manual mistakes like duplicated runs, missing combinations, or inconsistent configuration files.

During production you account for scheduler policies and queue behavior. You may separate long jobs and short jobs into different queues, adjust job sizes to improve throughput, or stagger submissions so that storage and I/O systems are not overloaded. Efficient workflows sometimes group many small tasks into a single larger job so that resources are used continuously and queue overhead is reduced.

Production runs also integrate fault tolerance strategies. You design your workflow so that failed jobs can be resubmitted easily, possibly starting from checkpoints. You monitor failure patterns to detect systematic issues such as insufficient wall time, underestimated memory requirements, or occasional node problems, and then adjust job templates accordingly.

Throughout the campaign you keep careful metadata. This includes not only physical parameters but also code revision identifiers, environment descriptions, resource requests, and job IDs. Attaching this metadata to outputs, often in a structured file or directory naming convention, is a key aspect of practical HPC workflows.

Phase 7: Data Management During and After Computation

As the production campaign progresses, data volumes grow. Managing this data stream is a central element of real HPC workflows.

In running workflows you must decide when to write outputs. Frequent output provides rich diagnostics and flexibility for later analysis, but increases I/O time and storage demands. Infrequent output reduces overhead but limits what you can inspect. In practice, workflows often use multiple output streams: lightweight diagnostic logs with high frequency and heavy data dumps less often.

You also manage temporary and permanent storage. Temporary intermediates, such as scratch files, are placed on fast but volatile filesystems and are cleaned up automatically or by your scripts. Final results and important checkpoints are copied to project directories or archival storage with longer retention. Clear policies on what is kept and what is discarded are essential to avoid filling filesystems or losing critical data.

Data verification is another aspect. Many workflows include post run checks that verify that all expected files are present, that file sizes are within reasonable ranges, and that simple integrity checks pass. These checks can be automated as part of the job epilogue or as a separate step in your campaign scripts.

Since storage is finite, workflows usually incorporate data reduction. You may compress outputs, store derived quantities instead of raw fields, subsample in time or space, or aggregate multiple runs into statistical summaries. Decisions about reduction are best made early in the planning phase and implemented consistently during production.

Finally, ongoing synchronization with institutional storage or external repositories is often part of the workflow. Automated tools or periodic scripts move completed datasets to long term storage, freeing up space on the cluster and ensuring that results remain accessible beyond the active project lifetime.

Phase 8: Postprocessing, Analysis, and Visualization

Once production runs complete, attention shifts from computation to analysis. Postprocessing is where raw outputs are transformed into interpretable quantities and visual artifacts.

Typical HPC workflows separate heavy postprocessing that needs many cores from lighter analysis that can be done on local machines. For example, you might run parallel postprocessing codes on the cluster to compute derived fields, perform large aggregations, or render high resolution images or movies. The results are then transferred locally for interactive exploration and final figure production.

Analysis workflows often involve scripting languages. You build chains of scripts or notebooks that read raw or reduced outputs, apply filters and transformations, and compute statistics or error measures. These scripts are version controlled and parameterized so that you can rerun them on new data or with updated methods.

Visualization is a special part of analysis workflows. For very large datasets you may use in situ or in transit visualization, where rendering or reduction occurs during the main simulation run. More commonly, you run visualization tools on dedicated visualization nodes that have access to the parallel filesystem and graphical capabilities.

The analysis and visualization phase is usually iterative. Insights from early analyses may reveal that additional diagnostics are needed, that certain outputs are unnecessary, or that parameter ranges should be revised. In a practical HPC workflow, this feedback eventually leads back to new planning and possibly another limited production campaign.

Phase 9: Documentation, Provenance, and Reproducibility

Throughout a typical HPC workflow, but especially once results are obtained, documentation and provenance tracking become critical.

You document the full computational experiment: code versions, input configurations, resource usage, and environment details. You may store this information in text files next to results, in structured metadata within output files, or in external lab notebooks. The goal is that you, or someone else, can recreate the entire workflow at a later time.

Provenance tracking also applies to analysis steps. For every figure and table you aim to know which raw or intermediate files were used and which scripts and parameters were applied. This is often achieved through automated pipelines that record their own steps, or through careful manual logging that links outputs to scripts and versions.

A mature workflow also includes validation and sanity checks that connect computation to reality or to known benchmarks. These checks and their outcomes are written down, not just performed once and forgotten. They become part of the evidence that your workflow is trustworthy.

Finally, reproducibility considerations often motivate the use of environment capture tools such as modules, containers, or environment managers. In practice you may save module lists, container recipes, or environment definition files with the project. Combined with stored job scripts and input files, these artifacts define the complete computational protocol.

Phase 10: Iteration and Workflow Refinement

Real HPC projects rarely follow the workflow linearly only once. Instead, the process is iterative. Results and performance observations lead you to refine earlier stages.

After initial analyses, you may revise the scientific question, adjust model complexity, or change the parameter space. From a workflow perspective, this means returning to planning and scaling phases, then re launching focused production runs. Iteration may also be triggered by performance improvements in your code or by changes in the cluster hardware or software environment.

With each iteration, you often automate more of the process. Steps that were manual in a first campaign become scripted in the second. Over time, what began as a loose collection of commands and scripts matures into a reproducible workflow that can be reused and adapted across projects.

A common pattern in HPC practice is that you build reusable building blocks: templates for job scripts, generic submission drivers, common analysis modules, and documentation structures. These components can be combined into new workflows for different applications with relatively minor changes.

By viewing your work as an evolving workflow rather than isolated runs, you prepare yourself for larger projects, collaborative efforts, and long lived codebases where consistency and reproducibility are just as important as raw performance.

Putting It All Together

Typical HPC workflows follow a recognizable lifecycle: plan, prototype locally, port to the cluster, test on small cases, perform scaling studies, run production campaigns, manage data, analyze outputs, document everything, and iterate. The details differ in meteorology, materials science, finance, or machine learning, but the structural pattern is similar.

As you gain experience, you learn to design workflows with these phases in mind from the outset. This mindset enables efficient use of shared resources, reduces wasted allocations, and makes your computational work transparent and reproducible.