KAHIBARO
41.1. Why Use HPC?
Table of Contents
Large Monte Carlo simulations
High Performance Computing, often abbreviated as HPC, becomes important in GATE when a single simulation on a desktop or laptop would take too long or require too much memory. Monte Carlo methods in medical physics rely on tracking very large numbers of particles to ensure low statistical uncertainty. In practice, this means millions or even billions of primary particles, and even more secondary particles, must be transported through complex geometries and materials.
For a beginner, it is useful to think of Monte Carlo uncertainty as behaving roughly like $1 / \sqrt{N}$, where $N$ is the number of primary histories. To reduce the relative uncertainty by a factor of 10, you need about 100 times more histories. This growth makes simulations increasingly expensive as you seek more precise results, for instance when you need accurate dose distributions or realistic PET and SPECT performance metrics.
On a single workstation, very detailed simulations might run for days or even weeks, especially if they involve complex voxelized patient geometries, detailed detector models, or sophisticated physics lists. HPC systems, which typically provide many CPU cores, large memory, and fast storage, allow you to distribute the work across many processors. In GATE this can be done by using multithreading within one job or by launching many independent simulations that each handle a subset of events or a specific part of a problem.
Using HPC resources can make the difference between a project that is practically impossible and one that completes in a reasonable time. For example, a full 3D patient dose calculation with realistic statistics, or a complete PET scanner simulation that generates enough coincidences for image reconstruction, may require hundreds or thousands of CPU hours. On an HPC cluster you can spread those hours over many compute nodes and finish in hours or days instead of months.
Another reason to use HPC for large Monte Carlo simulations is memory. Voxelized geometries, high resolution dose grids, and detailed particle histories can consume large amounts of RAM and disk space. HPC nodes often have more memory per node and faster parallel file systems, which helps when you need to store big ROOT files, dose images, or phase space data. In addition, HPC centers usually provide tools for monitoring and managing long-running jobs, which improves reliability for simulations that must run unattended for a long time.
For large Monte Carlo simulations, the key motivation for using HPC is to achieve acceptable statistical uncertainty in a reasonable wall-clock time by distributing many events across multiple cores and nodes.
Parameter studies
HPC is also very useful when you need to run many related simulations with different input parameters. This is called a parameter study. Instead of running a single, very large GATE simulation, you might need hundreds of smaller simulations that systematically vary some aspect of your setup.
In medical physics, parameter studies appear in many forms. You may want to scan over detector design variables, such as crystal thickness, pixel size, or different scintillator materials, to see their effect on energy resolution, timing resolution, or sensitivity. You might explore different shielding thicknesses and materials in a radiation protection scenario, or study how changes in beam energy, field size, or collimator settings influence dose distributions in radiotherapy or proton therapy.
If you run all these variations on a single machine, you would need to execute one simulation after another, which can easily take weeks. On an HPC system, you can submit many jobs at once, each with a different configuration, and let the batch scheduler distribute them across available nodes. This approach is particularly natural in GATE because your simulation setup is defined by Python scripts and configuration files, so you can generate multiple input files programmatically, for example by looping over a list of parameter values.
Many HPC centers support job arrays, which are designed exactly for this type of task. Each job in the array runs the same script but with a different index that you can map to, for example, a different source energy, scanner geometry, or patient orientation. With this setup you can explore a high dimensional parameter space much more efficiently, and it becomes feasible to perform systematic optimization and sensitivity analysis.
Parameter studies also benefit from HPC when you study reproducibility and uncertainty. You might need to run the same configuration multiple times with different random seeds to estimate the spread of results. Running these independent replicas in parallel reduces the turnaround time and helps you quantify statistical variability.
For parameter studies, HPC is most useful when you have many independent GATE simulations with different settings that can run in parallel, such as design optimization, sensitivity analysis, or repeated runs with different random seeds.
Overall, HPC is not required to learn GATE or to run small test simulations, but it becomes essential when you move toward realistic, research-level problems that need either very high statistics or a large number of parameter variations.
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