Table of Contents
Working Locally in an HPC-Oriented Way
Developing code locally is usually the most productive way to write, test, and debug your programs before you ever touch a cluster. The key idea is not just to write code on your laptop, but to develop in a way that anticipates the constraints and environment of an HPC system. This chapter focuses on that practical mindset.
You will learn how to set up a local environment that resembles a cluster, how to structure and test code so it is “HPC ready,” and how to avoid common traps that only show up when you finally run at scale.
Matching Your Local Environment to the Cluster
The more your local environment resembles the cluster, the fewer surprises you will face when you submit a job. Since cluster environments are described elsewhere, the goal here is to mimic some of their key aspects.
Start by using the same or similar compiler family locally. If your cluster uses GCC, install a recent GCC locally. If the cluster provides Intel or LLVM-based compilers, use their local equivalents where possible, or at least check that you compile with similar language standards and warning levels. Use consistent language versions, for example -std=c11 for C or -std=c++17 for C++, so that language features behave in the same way.
Try to match library availability. If you rely on MPI, BLAS, or other libraries on the cluster, install MPI and a BLAS implementation on your local machine too. It is generally better to develop against widely available interfaces, so that linking becomes straightforward when you move to the cluster.
Reproduce similar runtime constraints. Cluster jobs usually run from non-interactive scripts, have limited environment variables, and no direct access to your desktop’s graphical tools. When you run locally, you can simulate this by invoking your program from a simple script, avoiding GUI dependencies, and keeping input and output files in a structured layout. This makes the eventual translation to job scripts much easier.
If your operating system differs from the cluster, for instance you use macOS or Windows while the cluster runs Linux, pay special attention to portability. Avoid nonstandard extensions and platform-specific system calls. Rely on POSIX-compatible features and standard libraries where possible, because those translate well to HPC systems.
Setting Up Local Tools for HPC-Style Development
HPC codes are usually compiled and driven by command line tools. Even on your laptop, practice this workflow rather than relying solely on integrated development environments.
Use a version control system such as Git from the beginning. This helps you track changes, maintain multiple branches, and synchronize work between your local machine and the cluster. A simple pattern is to push your local repository to a remote service and then clone it on the cluster.
Develop simple, reproducible build commands. Even for small programs, write a Makefile or CMake configuration so you can rebuild consistently with a single command. This will mirror how you build on the cluster and also make it easier to introduce optimization flags, debugging flags, and variations in compilers.
Edit and test from the command line. Learn to compile with warning flags locally, for example -Wall -Wextra in GCC, and treat warnings as valuable early indicators of problems. Combine this with basic shell scripting to automate repetitive compile and run cycles, similar to how batch scripts control your jobs later.
Install lightweight profiling and debugging tools that run locally, such as simple CPU profilers or memory checkers, and develop the habit of using them while your code is still small and easy to modify. This will make performance analysis on the cluster less daunting, because you will already be familiar with the basic concepts.
Writing Code That Scales from Local to Cluster
Although you begin on a single machine, you should write code with larger problem sizes and parallel execution in mind. This does not require sophisticated parallel programming from the start, but it does require discipline in how you structure your program.
Separate computation from input and output. Keep your core numerical or logical kernels in functions that do not depend on user interaction or file I/O. This makes it easier to plug those kernels into MPI, OpenMP, or GPU frameworks later. Also, it makes local testing much faster and more reproducible.
Avoid unnecessary global state. Code that relies heavily on global variables or static mutable data is harder to parallelize and harder to reason about when running many processes on a cluster. Use well-defined data structures and pass them explicitly to functions instead.
Plan for large data sizes. When you write algorithms locally, do not assume that arrays are tiny. Be mindful of memory usage and data layout. Use dynamic allocation and check return values instead of assuming all allocations succeed. This prepares your code for the larger memory footprints encountered on clusters.
Limit platform-specific features. For local development, it might be tempting to use convenience libraries that will not exist on the cluster. Keep core logic free of such dependencies, or confine them to small, replaceable portions of the code. This makes porting and scaling less painful.
Important rule: Design your code so that the same source can be compiled and run both locally and on the cluster with only minimal configuration changes, such as compiler choice or library paths.
Testing and Debugging Locally Before Scaling Up
Clusters are expensive resources and queue wait times can be long. Extensive testing and debugging on your own machine reduces wasted cluster time and failed jobs.
Use small, representative test cases. Construct inputs that exercise each part of your code, but are small enough to run quickly and repeatedly. You will eventually create larger test cases for the cluster, but local tests should favor speed and clarity of behavior.
Check correctness over performance at first. Confirm that your results are mathematically and logically sound on small examples. Only after correctness is solid should you focus on speed. It is much cheaper to discover a logic error locally than after a long-running cluster job fails.
Learn to use a debugger on your local system. Practice stepping through code, inspecting variables, and understanding call stacks. The basic skills transfer to cluster debuggers, but are easier to learn where you have full control of the environment.
Practice handling errors gracefully. If your program encounters invalid input, missing files, or out-of-range parameters, it should produce clear error messages and exit with a nonzero status. Locally you can simulate these situations easily and check that your error handling works. On the cluster this kind of robustness prevents wasted runs and confusing failure modes.
Simulating Parallel Behavior on a Single Machine
Even if you only have your laptop, you can still begin to think in terms of parallel execution. Many shared memory and message passing libraries have local implementations that run on a single node.
If you use OpenMP, compile with thread support locally and experiment with different thread counts. For example you can run with one, two, and several threads and check that results are identical. This helps uncover race conditions or hidden assumptions before you run on many cores of a cluster node.
If you use MPI, install a local MPI implementation and run simple multi-process tests on your own machine. For instance, you can use mpirun -np 4 to start four local processes. Even though all processes run on the same node, you can verify that your communication pattern is correct and that the program behaves as expected when multiple ranks execute concurrently.
Use environment variables to control thread and process counts. Get used to setting these variables locally so that when you move to job scripts, changing scale feels natural. Running with a small number of local processes or threads is often enough to reveal logic errors in your parallelization strategy.
Key practice: Always validate that your parallel program produces the same correct results for multiple local thread or process counts before scaling to many nodes on the cluster.
Rehearsing Cluster Workflows on Your Laptop
Cluster jobs are usually driven by scripts and noninteractive commands. You can rehearse most of this workflow locally so that moving to the cluster is mainly a matter of changing paths and resource requests.
Create simple shell scripts that compile your code, set environment variables, and run your program with various options. Treat these scripts like miniature job scripts. They will serve as templates when you write actual job submissions on the cluster.
Organize your project directory with clear subdirectories for source code, build artifacts, input data, and output results. This structure maps naturally onto the more constrained filesystem layout on clusters and makes it easier to synchronize selective parts of your project.
Practice capturing output and logs to files rather than relying only on terminal output. Redirect standard output and standard error into log files and inspect those files afterward. This behavior mirrors how batch jobs produce logs in cluster environments.
Finally, measure basic performance locally. Although absolute timings will differ on the cluster, relative behavior, such as which parts of the code are hot spots, often carries over. Simple timing tests on your laptop prepare you to design more systematic performance runs when you access many cores or nodes.
Moving Code from Local Machine to Cluster
The last step of local development is preparing your code and environment so they transfer smoothly to the cluster. While data transfer, modules, and job scheduling are covered elsewhere, your local practices can make this step almost mechanical.
Keep configuration flexible. Use simple configuration files or environment variables for paths to input data, output directories, and optional features. Avoid hard coding absolute paths that only exist on your local machine.
Ensure that your project is easy to rebuild from scratch. A clean build process that can recompile everything with a single command reduces confusion when you build on a new system. Test this locally by deleting build artifacts and rebuilding occasionally.
Use your version control system to synchronize the project instead of copying random files manually. Commit only relevant source, configuration, and documentation. Avoid committing large generated outputs, and create a clear .gitignore list or equivalent to separate source from build products.
By the time you are ready to log in to the cluster and submit your first job, your code should already compile reliably, pass its local tests, and be structured in a way that anticipates parallel execution and batch workflows. Developing code locally with this mindset will make your early HPC experiences far more efficient and less frustrating.