sxjscience commented on issue #9833: [Metric] Accelerate the calculation of F1 URL: https://github.com/apache/incubator-mxnet/pull/9833#issuecomment-367144647 After some investigation, I find using GPU is not faster because the batch_size tested is rather small, i.e, 16, 64, 256, 1024 and NDArray + GPU is not as fast as numpy in these cases. I've used this script to test the speed: ```python import mxnet as mx import mxnet.ndarray as nd import numpy as np import time # Warm up the GPU for _ in range(10): a = nd.ones((100, 100), ctx=mx.gpu()) b = a * 2 b.asnumpy() N = 100 # Test the speed for data_shape in [(16,), (64,), (256,), (1024,)]: dat_npy = np.random.uniform(0, 1, data_shape) dat_nd_gpu = nd.array(dat_npy, ctx=mx.gpu()) dat_nd_cpu = nd.array(dat_npy, ctx=mx.cpu()) nd.waitall() start = time.time() for _ in range(N): np_ret = np.sum(dat_npy) end = time.time() np_time = end - start start = time.time() for _ in range(N): nd_ret = nd.sum(dat_nd_gpu).asscalar() end = time.time() nd_gpu_time = end - start start = time.time() for _ in range(N): nd_ret = nd.sum(dat_nd_cpu).asscalar() end = time.time() nd_cpu_time = end - start print('sum, data_shape=%s, numpy time=%g, mxnet gpu time=%g, mxnet cpu time=%g' %(str(data_shape), np_time, nd_gpu_time, nd_cpu_time)) ``` Result ``` sum, data_shape=(16,), numpy time=0.00067687, mxnet gpu time=0.0206566, mxnet cpu time=0.193971 sum, data_shape=(64,), numpy time=0.000299454, mxnet gpu time=0.0147879, mxnet cpu time=0.00626922 sum, data_shape=(256,), numpy time=0.000304699, mxnet gpu time=0.0141888, mxnet cpu time=0.00622177 sum, data_shape=(1024,), numpy time=0.000349522, mxnet gpu time=0.015424, mxnet cpu time=0.00976443 ```
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