Parallel Computing

Several SpikeLab methods support parallel computation to speed up analyses on large recordings.

Thread-Pool Parallelism

Methods that compute pairwise metrics across all unit pairs accept an n_jobs parameter that controls thread-pool parallelism:

  • get_pairwise_ccg() — pairwise cross-correlograms

  • get_pairwise_fr_corr() — pairwise firing-rate correlations

Pass n_jobs=-1 (the default) to use all available cores, n_jobs=1 for serial execution, or a specific integer to limit the number of threads:

corr, lag = rd.get_pairwise_fr_corr(max_lag=10, n_jobs=-1)

Numba Acceleration

When the optional numba package is installed, SpikeLab automatically uses JIT-compiled parallel kernels for the most computationally expensive operations:

  • STTC — spike_time_tilings() uses numba.prange to parallelize across all unit pairs

  • Pairwise latencies — get_pairwise_latencies() uses parallel nearest-spike search

  • Spike-triggered population rate — compute_spike_trig_pop_rate() uses parallel per-unit computation

No code changes are needed — SpikeLab detects numba at import time and switches to the accelerated kernels automatically. Install with:

pip install spikelab[numba]