KAHIBARO
43.8. Performance Problems
Table of Contents
Excessive memory use
Performance issues in GATE often start with memory. A simulation that slowly consumes more and more RAM can eventually crash or become extremely slow because the operating system starts swapping memory to disk.
In a typical GATE Python simulation, memory is used by three main components: the Geant4 geometry and physics state, the data you choose to record, and anything you accumulate in Python while the simulation runs. When you see excessive memory use, you should first identify which of these is the dominant contributor.
A frequent cause is recording too much detailed output. If you attach many actors that record hits, phase space data, and dose grids all at once, the event information must be buffered and written. Very fine voxel grids for dose or energy deposition can also fill RAM quickly. A grid of $512 \times 512 \times 512$ voxels already contains over 134 million elements. If you store multiple floating point values per voxel, memory usage grows rapidly. Whenever you configure an image actor, check the voxel numbers in each dimension and the data type. If your goal is to inspect a dose profile, you often do not need full clinical resolution. Reducing the number of voxels in any direction, or focusing on a smaller region of interest, can dramatically reduce memory.
It is important to remember that complex geometries also consume memory. Deeply nested volume hierarchies, large numbers of repeated volumes, or very fine voxelized phantoms all add overhead in Geant4. Medical CT based phantoms can already have several hundred slices, each with high in plane resolution. Before you add more detail, ask whether each structure is really needed for your question. You can often simplify parts of the geometry that are far away from the region of interest without affecting the quantity you want to measure, such as central dose or detector energy spectrum. Removing unused test volumes or old phantom components from the script also helps.
Recorded event data stored at the Python level is another silent memory consumer. When you run a simulation, try to avoid accumulating large arrays of results in memory. It is often better to write output directly to disk using ROOT or other formats, and read it later for analysis. In Python, make sure you do not keep references to large temporary arrays or images that you no longer need. Even a single NumPy array with tens of millions of elements can occupy hundreds of megabytes. After writing such arrays to disk and confirming you no longer need them, delete the variables or allow them to go out of scope so the garbage collector can reclaim memory.
Some performance problems described as memory issues are actually caused by small memory leaks or by keeping long lists of Python objects that grow with every event. If you generate diagnostic information in a loop, such as writing to a list or storing per event statistics, confirm that this list does not grow without bound. Instead of collecting everything in one run, consider saving intermediate results to files and starting a new run, or only recording aggregated statistics, for example, sums and means.
To check memory use, you can monitor system tools such as top or htop on Linux, the Task Manager on Windows, or Activity Monitor on macOS. Watch how memory evolves in time while the simulation progresses. A stable plateau suggests acceptable usage, while a steady upward trend suggests a leak or uncontrolled accumulation. If the growth appears during the initialization phase, geometry or material definitions might be responsible. If memory increases only when events are processed, output actors and Python analysis are more likely to be the cause.
Finally, be aware that multithreading can amplify memory needs. Each worker thread may have its own copies of certain data structures. If memory is already tight with a single thread, increasing the number of threads may lead to rapid exhaustion. In that situation, you should reduce output volume or geometry complexity before increasing thread counts. It is more efficient to run multiple lean simulations than a single very heavy one that fails due to memory.
To avoid excessive memory use, limit voxel resolution, record only the data you truly need, write data to disk instead of keeping it all in RAM, and avoid unnecessary Python collections that grow with the number of events.
Slow simulation
A slow GATE simulation can be caused by several independent factors. Identifying the dominant limitation is the first step toward improvement. Typical causes include very detailed geometry and materials, complex physics configurations, tight production cuts, extremely fine scoring grids, intensive output recording, and inefficient use of threads.
Geometry complexity directly affects tracking time. When a particle is transported, Geant4 must repeatedly determine which volume it is in and when it will hit material boundaries. If you have thousands or millions of small volumes, especially if they are nested or include many boolean operations, these navigation calculations become expensive. If you observe that event processing is slow from the start, ask whether your geometry is more detailed than necessary. Simplifying shapes, merging small components into larger logical units, or reducing the number of repeated volumes can all speed up navigation. Voxelized geometries from CT also fall into this category. Reducing image resolution, cropping to a smaller region, or using a coarser version for preliminary tests can save significant computation time.
Physics configuration is another critical factor. If you request many detailed processes or very precise models in regions where they are not needed, each particle step involves extra calculations. In medical imaging, for instance, you often do not need all hadronic physics or very low energy extensions. In therapeutic applications you might require more detailed electromagnetic physics in the patient region but not in surrounding support structures. Tailoring the physics list to your application and volume regions avoids unnecessary work. Very small production cuts, which control where and when secondary particles are created, can also slow the simulation considerably because they produce more secondary tracks and smaller transport steps.
Scoring and output can easily limit performance. Actors that record step by step information, detailed phase space data, or high resolution dose and energy maps introduce significant overhead per step or per hit. Writing many large ROOT branches or image slices during the run also consumes time. To improve speed, first remove any actor that is not essential for the result you currently need. Next, reduce scoring resolution. For example, use fewer dose voxels or record phase space only on a thin region instead of a large volume. If you only need summary statistics, such as total deposited energy or integrated dose, configure actors accordingly instead of storing the full spatial distribution.
Digitization for detectors can be expensive if configured in a very detailed way. Energy blurring, time blurring, position smearing, and complex coincidence logic all consume CPU time, especially if they are performed for very large numbers of hits or singles. When you develop or debug the geometry and source, it is often efficient to disable the digitizer entirely or keep only the simplest energy summation. You can then enable the full chain once the basic simulation is validated. Separating coarse preliminary runs from final high precision runs is a practical strategy to balance development speed and final accuracy.
Multithreading can, in principle, speed up simulations, but only if you choose a number of threads that matches your CPU resources and memory. If you request too many threads for the available cores, the operating system will constantly switch between them, and total runtime may increase rather than decrease. Monitor CPU usage while your simulation runs. If all cores are saturated and each event still progresses slowly, your process is CPU bound. In that case, fewer threads with better per thread performance might give similar throughput with less contention. Also check that your output system can keep up with multiple threads. Writing to a single disk file from many threads can create a bottleneck.
Sometimes the limitation is in Python logic that runs between or around simulation calls. Reading and writing large images repeatedly, applying slow analysis within the event loop, or performing expensive conversions on every run can outweigh the cost of the Monte Carlo transport itself. Try to keep the Python part of the simulation script as lean as possible during the actual event processing. Post processing can generally be done after the run has finished, using separate analysis scripts.
When you face a very slow simulation, measure its behavior. Look at how long initialization takes compared with event processing. Pay attention to how runtime scales when you increase the number of events, or when you enable or disable a specific actor or digitizer. If disabling a single actor significantly reduces total time, that actor is the bottleneck and you should reduce its workload. If runtime scales linearly with the number of events, but the time per event is high even with a very simple geometry and source, you should check the physics configuration and production cuts.
Finally, consider using incremental refinement. Start with a minimal geometry, simple source, reduced physics, and only one or two essential actors. Confirm that this runs fast. Then, step by step, add components, physics details, and output until you reach the level required for your scientific goal. At each step, observe how performance changes. This method not only speeds debugging but also teaches you which aspects of your simulation consume most of the computation time.
To reduce slow simulation, simplify geometry, choose only the physics processes you need, relax production cuts when appropriate, minimize scoring resolution and output volume, and use a suitable number of threads that matches your hardware.
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