Function gpu_aamp

stumpy/gpu_aamp.py:443–722

Compute the non-normalized (i.e., without z-normalization) matrix profile with one or more GPU devices This is a convenience wrapper around the Numba `cuda.jit` `_gpu_aamp` function which computes the non-normalized (top-k) matrix profile according to modified version GPU-STOMP

(T_A, m, T_B=None, ignore_trivial=True, device_id=0, p=2.0, k=1)

def gpu_aamp(T_A, m, T_B=None, ignore_trivial=True, device_id=0, p=2.0, k=1):
    """
    Compute the non-normalized (i.e., without z-normalization) matrix profile with
    one or more GPU devices

    This is a convenience wrapper around the Numba `cuda.jit` `_gpu_aamp` function
    which computes the non-normalized (top-k) matrix profile according to modified
    version GPU-STOMP. The default number of threads-per-block is set to `512` and
    may be changed by setting the global parameter `config.STUMPY_THREADS_PER_BLOCK`
    to an appropriate number based on your GPU hardware.

    Parameters
    ----------
    T_A : numpy.ndarray
        The time series or sequence for which to compute the matrix profile

    m : int
        Window size

    T_B : numpy.ndarray, default None
        The time series or sequence that contain your query subsequences
        of interest. Default is `None` which corresponds to a self-join.

    ignore_trivial : bool, default True
        Set to `True` if this is a self-join. Otherwise, for AB-join, set this
        to `False`. Default is `True`.

    device_id : int or list, default 0
        The (GPU) device number to use. The default value is `0`. A list of
        valid device ids (int) may also be provided for parallel GPU-STUMP
        computation. A list of all valid device ids can be obtained by
        executing `[device.id for device in numba.cuda.list_devices()]`.

    p : float, default 2.0
        The p-norm to apply for computing the Minkowski distance. Minkowski distance is
        typically used with `p` being 1 or 2, which correspond to the Manhattan distance
        and the Euclidean distance, respectively.

    k : int, default 1
        The number of top `k` smallest distances used to construct the matrix profile.
        Note that this will increase the total computational time and memory usage
        when k > 1.

    Returns
    -------
    out : numpy.ndarray
        When k = 1 (default), the first column consists of the matrix profile,
        the second column consists of the matrix profile indices, the third column
        consists of the left matrix profile indices, and the fourth column consists
        of the right matrix profile indices. However, when k > 1, the output array
        will contain exactly 2 * k + 2 columns. The first k columns (i.e., out[:, :k])
        consists of the top-k matrix profile, the next set of k columns
        (i.e., out[:, k:2k]) consists of the corresponding top-k matrix profile
        indices, and the last two columns (i.e., out[:, 2k] and out[:, 2k+1] or,
        equivalently, out[:, -2] and out[:, -1]) correspond to the top-1 left
        matrix profile indices and the top-1 right matrix profile indices, respectively.

        For convenience, the matrix profile (distances) and matrix profile indices can