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The sparse identification of nonlinear dynamics (SINDy) recovers interpretable governing equations from time-series data by sparse regression over a library of candidate functions. As data sets, candidate libraries and the number of repeated fits grow, the cost of SINDy becomes dominated by the evaluation of the library matrix and its least-squares problems. We present an open-source Python implementation of SINDy in which a single set of loop-based kernels is just-in-time compiled with Numba and executed on a shared-memory multicore node by four interchangeable back ends: Numba prange, OpenMP directives through PyOMP, Python threads that release the global interpreter lock, and multiprocessing with shared memory. The normal equations are accumulated in one cache-blocked pass over the data, with an automatic fallback to a tall-skinny QR reduction for ill-conditioned problems. Two decompositions are studied on the Lorenz and Rössler systems: an ensemble (task-parallel) workload of 960 independent identifications and a library (data-parallel) workload of one trajectory with $2\times10^7$ samples and a fifth-order library of 56 candidate terms. All back ends recover the exact sparsity pattern in every identification, with coefficient errors identical to PySINDy. On a dual-socket Intel Xeon E5-2650 v4 node of the University of Houston-Downtown HPC cluster (24 cores, 48 hardware threads), the compiled serial code is 4.3-5.6x faster than PySINDy, the parallel back ends reach speed-ups of 14.8-20.3 on 24 cores (parallel efficiency 0.61-0.84), and the overall time to solution is reduced by up to 117x with respect to PySINDy. We analyse the differences between the back ends, the effect of the two sockets and of hyper-threading, and give practical recommendations.