KAHIBARO
22.5. Performance Considerations
Table of Contents
CPU scaling
Multithreaded Geant4 is designed so that each worker thread runs independent events. In an ideal situation, doubling the number of threads would nearly halve the wall‑clock time for a fixed number of events. In practice, scaling is limited by how much of your simulation is truly parallel and how much time is spent in shared resources.
The part that scales well is event processing. If most of your time is spent transporting particles through geometry and processing physics, you can usually obtain close to linear speedup until you saturate your CPU cores. To benefit from this, keep anything that happens inside events local to the worker threads. Thread‑local objects and per‑thread analysis buffers help here, because they reduce the need for synchronization.
Synchronization points reduce scaling. Whenever all threads must wait for a shared action, such as writing to a single output file, updating a global histogram, or accessing a shared non thread‑safe detector description, performance can stall. If you notice poor scaling as you increase the number of threads, look for such global bottlenecks in your own code. Geant4 itself avoids global locks during tracking, so most scaling issues come from user code.
You should also pay attention to load balance. The Geant4 master thread distributes events to worker threads dynamically, so you do not have to manage explicit work distribution. However, if some events are extremely long and others very short, overall runtime can be dominated by a few long events that occupy only a subset of threads near the end of the run. In that case, increasing the total number of events or dividing very long events into several simulations can improve effective scaling.
The way you choose physics and geometry affects CPU scaling indirectly. Very detailed physics lists and complex geometries increase CPU time per event for all threads. This does not change how well the code scales with thread count, but it does change the absolute runtime. For large production runs, you often balance physics detail, number of events, and thread count to meet a time budget.
For good CPU scaling, keep event‑level work local to each thread and avoid frequent access to shared, non thread‑safe resources during tracking and event processing.
Memory usage
In multithreaded Geant4, each worker thread has its own copy of many data structures, including physics tables, geometry navigation caches, and user action objects. As a result, total memory usage increases roughly with the number of threads. When you choose a thread count, you must consider not only CPU time but also available RAM.
The memory per thread depends strongly on your physics list and geometry complexity. Detailed reference physics lists, many different materials, and large, finely segmented detector geometries all contribute to larger memory footprints. When such a configuration is replicated across many threads, the overall memory demand can become high. On systems with limited RAM, using too many threads may cause swapping to disk, which can make the simulation slower than a run with fewer threads.
You can reduce memory pressure in several ways. Simplifying geometry, limiting the number of distinct materials where possible, and avoiding unnecessary detector segmentation all reduce the size of per‑thread data structures. Choosing a physics list that is appropriate to your problem, instead of always using the most detailed option, can also save memory. If you use large user data structures, for example big lookup tables or cross section grids, ensure that they are shared read‑only where possible rather than duplicated in each thread.
Another aspect of memory usage is output. If you accumulate large analysis objects in memory and only write them at the end of the run, the required memory grows with the number of events processed in parallel. Instead, write or flush output periodically, or use per‑thread files that you merge later. This keeps per‑thread memory use more stable as you increase thread count.
When you increase the number of threads, check memory usage carefully. If total memory approaches or exceeds physical RAM, reduce thread count or simplify the simulation to avoid severe slowdowns due to swapping or memory exhaustion.
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