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Running some benchmark of BLAS level 3 and LAPACK on Apple M1
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import numpy as np | |
try: | |
import tensorflow as tf | |
except ImportError: | |
tf = None | |
from time import perf_counter | |
def timeit(func, *args, **kwargs): | |
durations = [] | |
for i in range(10): | |
tic = perf_counter() | |
func(*args, **kwargs) | |
toc = perf_counter() | |
durations.append(toc - tic) | |
durations.sort() | |
return np.mean(durations[:-5]) # trim stragglers | |
for dtype in (np.float32, np.float64): | |
dtype_name = dtype.__name__ | |
rng = np.random.default_rng(42) | |
A = rng.normal(size=(1024, 1024)).astype(dtype) | |
B = rng.normal(size=(1024, 1024)).astype(dtype) | |
C = np.empty_like(A @ B) | |
d = timeit(np.dot, A, B, out=C) | |
GFLOP = A.shape[0] * B.shape[1] * (2 * A.shape[1] + 2) / 1e9 | |
print(f"[{dtype_name}] np.dot: {d * 1e3:.3f} ms, {GFLOP / d:.1f} GFLOP/s") | |
d = timeit(np.linalg.svd, A) | |
print(f"[{dtype_name}] np.linalg.svd: {d * 1e3:.3f} ms") | |
if tf is not None: | |
A = tf.constant(A) | |
B = tf.constant(B) | |
d = timeit(np.matmul, A, B) | |
GFLOP = A.shape[0] * B.shape[1] * (2 * A.shape[1] + 2) / 1e9 | |
print(f"[{dtype_name}] tf.matmul: {d * 1e3:.3f} ms, {GFLOP / d:.1f} GFLOP/s") | |
d = timeit(tf.linalg.svd, A) | |
print(f"[{dtype_name}] tf.linalg.svd: {d * 1e3:.3f} ms") |
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