reordering_depth的值要小于outstanding值

reordering_depth的值要小于outstanding值的原因是为了确保计算机能够按照正确的顺序执行指令和操作。

reordering_depth是指计算机处理指令时能够对其进行重新排序的深度。在处理器中，为了提高性能，计算机经常会对指令进行重新排序，以最大程度地充分利用处理器的资源。但是，重新排序也可能导致一些问题，例如，指令之间的依赖关系可能被打乱，导致错误的结果。

而outstanding值表示在指令处理过程中，尚未完成的指令的数量。这些指令通常是因为执行指令时需要等待某些资源或者条件满足才能继续执行。

如果reordering_depth的值大于outstanding值，计算机可能会发生指令顺序混乱的情况。这是因为reordering_depth的值越大，计算机对指令进行重新排序的范围也越大，这可能会导致依赖关系被打乱，进而产生错误的结果。因此，reordering_depth的值应该小于outstanding值，以确保计算机能够按照正确的顺序处理指令，保证计算的准确性和可靠性。

总而言之，reordering_depth的值要小于outstanding值，是为了保证计算机能够按照正确的顺序处理指令，以确保计算的准确性和可靠性。

相关问题

Reordering Buffer 设计

可以回答这个问题。Reordering Buffer (ROB) 是处理器的一个重要组成部分，用于重排指令的执行顺序，以提高程序的执行效率。ROB 设计的关键是要保证指令的顺序不会影响程序的正确性，同时尽可能地利用处理器资源。ROB 的实现方式有多种，其中比较常见的是基于循环队列和链表的设计。

class SVDRecommender: def init(self, k=50, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack'): self.k = k self.ncv = ncv self.tol = tol self.which = which self.v0 = v0 self.maxiter = maxiter self.return_singular_vectors = return_singular_vectors self.solver = solver def svds(self, A): if which == 'LM': largest = True elif which == 'SM': largest = False else: raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if k <= 0 or k >= min(n, m): raise ValueError("k must be between 1 and min(A.shape), k=%d" % k) if isinstance(A, LinearOperator): if n > m: X_dot = A.matvec X_matmat = A.matmat XH_dot = A.rmatvec XH_mat = A.rmatmat else: X_dot = A.rmatvec X_matmat = A.rmatmat XH_dot = A.matvec XH_mat = A.matmat dtype = getattr(A, 'dtype', None) if dtype is None: dtype = A.dot(np.zeros([m, 1])).dtype else: if n > m: X_dot = X_matmat = A.dot XH_dot = XH_mat = _herm(A).dot else: XH_dot = XH_mat = A.dot X_dot = X_matmat = _herm(A).dot def matvec_XH_X(x): return XH_dot(X_dot(x)) def matmat_XH_X(x): return XH_mat(X_matmat(x)) XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype, matmat=matmat_XH_X, shape=(min(A.shape), min(A.shape))) # Get a low rank approximation of the implicitly defined gramian matrix. eigvals, eigvec = eigsh(XH_X, k=k, tol=tol ** 2, maxiter=maxiter, ncv=ncv, which=which, v0=v0) # Gramian matrix has real non-negative eigenvalues. eigvals = np.maximum(eigvals.real, 0) # Use complex detection of small eigenvalues from pinvh. t = eigvec.dtype.char.lower() factor = {'f': 1E3, 'd': 1E6} cond = factor[t] * np.finfo(t).eps cutoff = cond * np.max(eigvals) # Get a mask indicating which eigenpairs are not degenerate tiny, # and create a reordering array for thresholded singular values. above_cutoff = (eigvals > cutoff) nlarge = above_cutoff.sum() nsmall = k - nlarge slarge = np.sqrt(eigvals[above_cutoff]) s = np.zeros_like(eigvals) s[:nlarge] = slarge if not return_singular_vectors: return np.sort(s) if n > m: vlarge = eigvec[:, above_cutoff] ularge = X_matmat(vlarge) / slarge if return_singular_vectors != 'vh' else None vhlarge = _herm(vlarge) else: ularge = eigvec[:, above_cutoff] vhlarge = _herm(X_matmat(ularge) / slarge) if return_singular_vectors != 'u' else None u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None indexes_sorted = np.argsort(s) s = s[indexes_sorted] if u is not None: u = u[:, indexes_sorted] if vh is not None: vh = vh[indexes_sorted] return u, s, vh将这段代码放入一个.py文件中，用Spyder查看，有报错，可能是缩进有问题，无法被调用，根据这个问题，给出解决办法，给出改正后的完整代码

好的，根据你提供的代码，我发现其中存在一些缩进错误，导致代码无法运行。下面是修改后的完整代码，请注意缩进：

import numpy as np
from scipy.sparse.linalg import eigsh, LinearOperator
from scipy.sparse import isspmatrix, is_pydata_spmatrix


class SVDRecommender:
    def __init__(self, k=50, ncv=None, tol=0, which='LM', v0=None,
                 maxiter=None, return_singular_vectors=True, solver='arpack'):
        self.k = k
        self.ncv = ncv
        self.tol = tol
        self.which = which
        self.v0 = v0
        self.maxiter = maxiter
        self.return_singular_vectors = return_singular_vectors
        self.solver = solver

    def svds(self, A):
        if self.which == 'LM':
            largest = True
        elif self.which == 'SM':
            largest = False
        else:
            raise ValueError("which must be either 'LM' or 'SM'.")

        if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)):
            A = np.asarray(A)
            n, m = A.shape

        if self.k <= 0 or self.k >= min(n, m):
            raise ValueError("k must be between 1 and min(A.shape), k=%d" % self.k)

        if isinstance(A, LinearOperator):
            if n > m:
                X_dot = A.matvec
                X_matmat = A.matmat
                XH_dot = A.rmatvec
                XH_mat = A.rmatmat
            else:
                X_dot = A.rmatvec
                X_matmat = A.rmatmat
                XH_dot = A.matvec
                XH_mat = A.matmat

            dtype = getattr(A, 'dtype', None)

            if dtype is None:
                dtype = A.dot(np.zeros([m, 1])).dtype

        else:
            if n > m:
                X_dot = X_matmat = A.dot
                XH_dot = XH_mat = _herm(A).dot
            else:
                XH_dot = XH_mat = A.dot
                X_dot = X_matmat = _herm(A).dot

        def matvec_XH_X(x):
            return XH_dot(X_dot(x))

        def matmat_XH_X(x):
            return XH_mat(X_matmat(x))

        XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype, matmat=matmat_XH_X, shape=(min(A.shape), min(A.shape)))

        # Get a low rank approximation of the implicitly defined gramian matrix.
        eigvals, eigvec = eigsh(XH_X, k=self.k, tol=self.tol ** 2, maxiter=self.maxiter, ncv=self.ncv,
                                which=self.which, v0=self.v0)

        # Gramian matrix has real non-negative eigenvalues.
        eigvals = np.maximum(eigvals.real, 0)

        # Use complex detection of small eigenvalues from pinvh.
        t = eigvec.dtype.char.lower()
        factor = {'f': 1E3, 'd': 1E6}
        cond = factor[t] * np.finfo(t).eps
        cutoff = cond * np.max(eigvals)

        # Get a mask indicating which eigenpairs are not degenerate tiny,
        # and create a reordering array for thresholded singular values.
        above_cutoff = (eigvals > cutoff)
        nlarge = above_cutoff.sum()
        nsmall = self.k - nlarge
        slarge = np.sqrt(eigvals[above_cutoff])
        s = np.zeros_like(eigvals)
        s[:nlarge] = slarge

        if not self.return_singular_vectors:
            return np.sort(s)

        if n > m:
            vlarge = eigvec[:, above_cutoff]
            ularge = X_matmat(vlarge) / slarge if self.return_singular_vectors != 'vh' else None
            vhlarge = _herm(vlarge)
        else:
            ularge = eigvec[:, above_cutoff]
            vhlarge = _herm(X_matmat(ularge) / slarge) if self.return_singular_vectors != 'u' else None

        u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None
        vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None

        indexes_sorted = np.argsort(s)
        s = s[indexes_sorted]

        if u is not None:
            u = u[:, indexes_sorted]

        if vh is not None:
            vh = vh[indexes_sorted]

        return u, s, vh


def _augmented_orthonormal_cols(U, n):
    if U.shape[0] <= n:
        return U

    Q, R = np.linalg.qr(U)
    return Q[:, :n]


def _augmented_orthonormal_rows(V, n):
    if V.shape[1] <= n:
        return V

    Q, R = np.linalg.qr(V.T)
    return Q[:, :n].T


def _herm(x):
    return np.conjugate(x.T)