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April 8, 2024 11:44
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how matmul is tiled in cuda
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import numpy as np | |
from threading import Barrier, Thread | |
from collections import namedtuple | |
dim3 = namedtuple("dim3", ["x", "y", "z"], defaults=(1, 1)) | |
TILE = 16 | |
def cdiv(a, b): | |
return (a + b - 1) // b | |
def matmul_kernel( | |
blockIdx: dim3, | |
threadIdx: dim3, | |
blockDim: dim3, | |
sync: Barrier, | |
shared, | |
a, | |
b, | |
out, | |
): | |
h, k = a.shape | |
_, w = b.shape | |
tr = threadIdx.y | |
tc = threadIdx.x | |
r = blockIdx.y * blockDim.y + tr | |
c = blockIdx.x * blockDim.x + tc | |
ta = shared[0].reshape(TILE, TILE) | |
tb = shared[1].reshape(TILE, TILE) | |
res = 0.0 | |
for i in range(cdiv(k, TILE)): | |
ta[tr][tc] = a[r][i * TILE + tc] if r < h and (i * TILE + tc) < k else 0.0 | |
tb[tr][tc] = b[i * TILE + tr][c] if c < w and (i * TILE + tr) < k else 0.0 | |
sync.wait() | |
for j in range(TILE): | |
res += ta[tr][j] * tb[j][tc] | |
sync.wait() | |
if r < h and c < w: | |
out[r][c] = res | |
def launch_kernel(f, blocks: dim3, tpb: dim3, *args): | |
for iby in range(blocks.y): | |
for ibx in range(blocks.x): | |
sync = Barrier(tpb.y * tpb.x) | |
shared = np.empty((2, TILE, TILE), dtype=np.float32) | |
threads = [ | |
Thread( | |
target=f, | |
args=(dim3(ibx, iby), dim3(itx, ity), tpb, sync, shared, *args), | |
) | |
for ity in range(tpb.y) | |
for itx in range(tpb.x) | |
] | |
for t in threads: | |
t.start() | |
for t in threads: | |
t.join() | |
def matmul(a, b): | |
h, _ = a.shape | |
_, w = b.shape | |
out = np.empty((h, w), dtype=a.dtype) | |
blocks = dim3(cdiv(h, TILE), cdiv(w, TILE)) | |
tpb = dim3(TILE, TILE) | |
launch_kernel(matmul_kernel, blocks, tpb, a, b, out) | |
return out | |
a = np.random.rand(20, 30) | |
b = np.random.rand(30, 20) | |
print(np.allclose(a @ b, matmul(a, b))) |
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