Default Behaviours and Conventions

CPU and GPU Computation

All pysiglib functions support both CPU and GPU (CUDA) computation. The computation is performed on the same device as the input data: pass a CPU tensor for CPU computation, or a CUDA tensor for GPU computation.

Note that CUDA is not necessarily faster than CPU for all operations. Whether the GPU backend is beneficial depends on the specific function, the problem parameters (batch size, path length, dimension, truncation degree, etc.), and the hardware. We recommend benchmarking on your own setup to determine the best device for your workload.

CPU Parallelism

For CPU-based computations, the n_jobs parameter in pysiglib functions specifies the number of threads to run in parallel. If n_jobs = 1, the computation is run serially. This is the default behaviour. If set to -1, all available threads are used. For n_jobs below -1, (max_threads + 1 + n_jobs) threads are used. For example if n_jobs = -2, all threads but one are used.

Parallelising the computation by setting n_jobs != 1 is beneficial when the workload is large. However, if the workload is too small, it may be faster to set this to 1 and run the computation serially, due to parallelisation overhead.

Floating Point Precision

Data passed to pysiglib functions should be of type float or double. The calculation will be performed in the same type as the data, so that input data of type float will lead to faster but less accurate calculations, whilst inputs of type double will be slower but more accurate. For most machine learning applications, float-level accuracy should be sufficient.

Input types

All arrays passed to a given pysiglib function should be of the same type and be located on the same device. For example, for the call k = pysiglib.sig_kernel(X, Y, 1), if X is a torch tensor of type float located on a GPU, then Y should be the same. This will also be the format of the output tensor, k.

Non-Contiguous Arrays

Ideally, any array passed to pysiglib functions should be both contiguous and own its data. If this is not the case, pysiglib will internally create a contiguous copy, which may be inefficient in some cases. When this happens, pysiglib will issue a one-time warning.

Citation

If you found this library useful in your research, please consider citing the paper:

@article{shmelev2025pysiglib,
  title={pySigLib-Fast Signature-Based Computations on CPU and GPU},
  author={Shmelev, Daniil and Salvi, Cristopher},
  journal={arXiv preprint arXiv:2509.10613},
  year={2025}
}