Default Behaviours and Conventions

Default Behaviours and Conventions#

CPU and GPU Computation#

For functions which support both CPU and GPU computation, the computation will be performed on the same device as the input data. This applies to all functions involving signature kernels.

Functions involving signature calculations or similar tensor operations to Chen’s relation only have CPU-based implementations in pysiglib. This is because these operations are memory-bound rather than computation-bound, and so are not well-suited to GPUs. Whilst other packages may implement GPU algorithms, we find that pysiglib CPU algorithms often do as well or better on most chips.

When GPU data is passed to a CPU-only function, the data will be copied to the CPU where the algorithm will run, and the result will be moved back to the GPU. This behaviour applies to the following functions:

  • sig,

  • sig_combine,

  • sig_to_log_sig,

  • log_sig,

  • and corresponding backpropagation functions.

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}
}
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