matrixmultiplication_in_gpu.py

# -*- coding: utf-8 -*-
"""MatrixMultiplication_in_GPU.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1jUoopc2V_9ZpdpZ2az_7u1yHAPizj7Gz
"""

import numpy as np
from numba import cuda, types, float32

@cuda.jit
def fast_matmul(A, B, C):
    # Define an array in the shared memory
    # The size and type of the arrays must be known at compile time
    TPB = N

    sA = cuda.shared.array(shape=(TPB, TPB), dtype=float32)
    sB = cuda.shared.array(shape=(TPB, TPB), dtype=float32)

    x, y = cuda.grid(2)

    tx = cuda.threadIdx.x
    ty = cuda.threadIdx.y
    bpg = cuda.gridDim.x    # blocks per grid

    if x >= C.shape[0] and y >= C.shape[1]:
        # Quit if (x, y) is outside of valid C boundary
        return

    # Each thread computes one element in the result matrix.
    # The dot product is chunked into dot products of TPB-long vectors.
    tmp = 0.
    for i in range(bpg):
        # Preload data into shared memory
        sA[tx, ty] = A[x, ty + i * TPB]
        sB[tx, ty] = B[tx + i * TPB, y]

        # Wait until all threads finish preloading
        cuda.syncthreads()

        # Computes partial product on the shared memory
        for j in range(TPB):
            tmp += sA[tx, j] * sB[j, ty]

        # Wait until all threads finish computing
        cuda.syncthreads()

    C[x, y] = tmp

# This part is for initializing everything
M = 128
N = 32

a = np.ones(M*N).reshape(M,N).astype(np.float32)
b = np.ones(M*N).reshape(N,M).astype(np.float32)
c = np.zeros((M, M)).astype(np.float32)

d_a = cuda.to_device(a)
d_b = cuda.to_device(b)
d_c = cuda.to_device(c)

block_size = (N,N)
grid_size = (int(M/N),int(M/N))

fast_matmul[grid_size,block_size](d_a, d_b, d_c)

c = d_c.copy_to_host()
print(c)