I'm developing a physics simulation that currently runs locally in Python and takes about four minutes per iteration. It relies heavily on SciPy, NumPy, Pandas, diskcache/memoization, and other libraries whose numerical routines are already implemented in optimized C, C++, or Fortran. The simulation may need to run roughly 1,000,000 times on an HPC cluster, potentially in batches of hundreds or thousands, with access to the cluster for about two months. My supervisor suggested considering a C or C++ implementation for better performance. Since Python is mostly coordinating calls to compiled numerical libraries, I'm unsure whether rewriting the whole program would provide a meaningful speedup, or whether the effort would be better spent on profiling, parallelization, batching, and efficient I/O. What would be the strongest case for moving to C or C++ in this situation?
4 Answers
At four minutes per run, the total workload is enormous: serial execution would be roughly 7.6 years. Even with substantial parallelism, this is large enough that optimization is worth taking seriously. However, the first priority should be determining whether the million runs are independent and can be distributed across nodes or jobs. Benchmark a representative batch, account for startup and storage overhead, and estimate the actual throughput before committing to a full rewrite.
SciPy and NumPy already perform their heavy numerical work in optimized native code, so rewriting Python glue code in C++ may not help much if the simulation is mostly passing arrays into those libraries. First profile one iteration to identify where the four minutes actually go. You may get a much larger improvement from parallelizing independent runs, reducing data movement and disk access, and making sure the cluster is being used efficiently. Only rewrite the genuinely Python-heavy hotspots if profiling shows they matter.
That makes sense. I’m going to benchmark the individual stages and see whether the time is going into the native routines, Python-level loops, caching, or I/O.
A full C++ rewrite is not automatically the best choice. Keep the existing implementation as a baseline, profile it, and then optimize the parts that dominate runtime. Python loops, object-heavy data handling, repeated conversions between arrays and tables, cache lookups, and excessive file access are possible targets. If one small algorithm is responsible for most of the time, moving just that routine to C++, Cython, Numba, or another compiled approach could provide most of the benefit without replacing the whole application.
Memoization can help when inputs repeat, but it can also become expensive if cached values are large or if many processes access the same disk-backed cache. Measure cache hit rates, serialization costs, and filesystem contention on the cluster. Also verify that each worker is using an appropriate number of threads; otherwise you can accidentally oversubscribe the nodes. These operational details may matter more than the language choice.
The plan is to use the cluster and run jobs in batches of around 100 to 1,000. We have access to the compute resources for roughly two months, so throughput and scheduling overhead will be important.