# Examples¶

## Matrix multiplication¶

Here is a naive implementation of matrix multiplication using a HSA kernel:

```@roc.jit
def matmul(A, B, C):
i = roc.get_global_id(0)
j = roc.get_global_id(1)

if i >= C.shape or j >= C.shape:
return

tmp = 0

for k in range(A.shape):
tmp += A[i, k] * B[k, j]

C[i, j] = tmp
```

This implementation is straightforward and intuitive but performs poorly, because the same matrix elements will be loaded multiple times from device memory, which is slow (some devices may have transparent data caches, but they may not be large enough to hold the entire inputs at once).

It will be faster if we use a blocked algorithm to reduce accesses to the device memory. HSA provides a fast shared memory for workitems in a group to cooperatively compute on a task. The following implements a faster version of the square matrix multiplication using shared memory:

```import numpy as np
from numba import roc
from numba import float32
from time import time as timer

blocksize = 16
gridsize = 16

@roc.jit('(float32[:,:], float32[:,:], float32[:,:])')
def matmulfast(A, B, C):
x = roc.get_global_id(0)
y = roc.get_global_id(1)

tx = roc.get_local_id(0)
ty = roc.get_local_id(1)

sA = roc.shared.array(shape=(blocksize, blocksize), dtype=float32)
sB = roc.shared.array(shape=(blocksize, blocksize), dtype=float32)

if x >= C.shape or y >= C.shape:
return

tmp = 0

for i in range(gridsize):
sA[tx, ty] = A[x, ty + i * blocksize]
sB[tx, ty] = B[tx + i * blocksize, y]
# wait for preload to end
roc.barrier(1)
# compute loop
for j in range(blocksize):
tmp += sA[tx, j] * sB[j, ty]
# wait for compute to end
roc.barrier(1)

C[x, y] = tmp

N = gridsize * blocksize
A = np.random.random((N, N)).astype(np.float32)
B = np.random.random((N, N)).astype(np.float32)
C = np.zeros_like(A)

griddim = gridsize, gridsize
blockdim = blocksize, blocksize

with roc.register(A, B, C):
ts = timer()
matmulfast[griddim, blockdim](A, B, C)
te = timer()
print("1st GPU time:", te - ts)

with roc.register(A, B, C):
ts = timer()
matmulfast[griddim, blockdim](A, B, C)
te = timer()
print("2nd GPU time:", te - ts)

ts = timer()
ans = np.dot(A, B)
te = timer()
print("CPU time:", te - ts)
np.testing.assert_allclose(ans, C, rtol=1e-5)
```

Because the shared memory is a limited resource, the code preloads a small block at a time from the input arrays. Then, it calls `barrier()` to wait until all threads have finished preloading before doing the computation on the shared memory. It synchronizes again after the computation to ensure all threads have finished with the data in shared memory before overwriting it in the next loop iteration.