Faster matmul for case sparse.coo * np.ndarray

I am using this module quite heavily in a recent project and found that sparse.matmul could work faster.
I have implemented a faster version for the case matmul(sparse.coo, np.ndarray) using numba for the case of 2D arrays (cut be generalized). I also implemented a parallellized Version. The single threaded version is roughly 3 times faster than sparse.matmul in this case and scales linearly with the number of entries in the sparse matrix. The multi-threaded Version has some overhead but scales well for very large matrices (10000000+ entries).

Here is the code:

@numba.njit()
def matmul_fast(dim1:np.ndarray,dim2:np.ndarray,V:np.ndarray,Arr: np.ndarray,out: np.ndarray):
    for i in range(V.shape[0]):
        out[dim1[i]] += V[i] * Arr[dim2[i]]
    return out

@numba.njit(parallel=True,fastmath=True,boundscheck=False)
def matmul_fast_parallel(dim1:np.ndarray,dim2:np.ndarray,V:np.ndarray,Arr: np.ndarray,out: np.ndarray,n: np.int64 = 100000):
    f = V.shape[0]
    s = out.shape
    out_loc = np.zeros(s,dtype=np.float64)
    g = 0
    i = 0
    for i_0 in numba.prange(0,f//n+1):
        i = i_0 * n
        out_loc = np.zeros(s,dtype=np.float64)
        for g in range(i,i+n):
            if g>=f:
                break
            out_loc[dim1[g]] += V[g] * Arr[dim2[g]]
        out += out_loc
    return out

These functions can be driven by a non numba function that unpacks the sparse array object into coordinates, data and shape.

def matmul_sparse(Arr_sparse, Arr):
    out = np.zeros((Arr_sparse.shape[0],Arr.shape[1]))
    return matmul_fast_parallel(Arr_sparse.coords[0],Arr_sparse.coords[1],Arr_sparse.data,Arr,out)

Are there any optimizations planned that improve the performance of sparse.matmul?
If not would there be interest in introducing specific solutions like this as an option in the current implementation?

Beginnen Sie damit, den aktuellen sparse.matmul-Einstiegspunkt und den Umgang mit der Multiplikation von sparse.coo mit einem NumPy ndarray zu überprüfen. Vergleichen Sie diesen Pfad mit den vorgeschlagenen Single-Threaded- und parallelen Implementierungen; die Arbeit ist erst abgeschlossen, wenn sich das Projekt auf den Umfang der Implementierung geeinigt und Leistungsverbesserungen für den angegebenen 2D-Fall verifiziert hat.