SINGA-487 Fusing gradients to reduce network latency

[chrishkchris]

This PR reduces the network latency by fusing gradients in the same memory buffer before sending out with NCCL.
This can reduce much of the TCP/IP latency by reducing the number of NCCL API call.

Together with the result of PR #555, here is a simple test to make sure the training is correct:

ubuntu@ip-172-31-26-214:~/singa/examples/autograd$ python3 mnist_multiprocess.py
Starting Epoch 0:
Training loss = 831.072205, training accuracy = 0.700454
Evaluation accuracy = 0.927015, Elapsed Time = 0.676089s
Starting Epoch 1:
Training loss = 248.684601, training accuracy = 0.916183
Evaluation accuracy = 0.958265, Elapsed Time = 0.545179s
Starting Epoch 2:
Training loss = 172.330597, training accuracy = 0.943042
Evaluation accuracy = 0.967928, Elapsed Time = 0.543617s
Starting Epoch 3:
Training loss = 139.254807, training accuracy = 0.953425
Evaluation accuracy = 0.973067, Elapsed Time = 0.530805s
Starting Epoch 4:
Training loss = 115.329491, training accuracy = 0.960737
Evaluation accuracy = 0.976049, Elapsed Time = 0.530590s
Starting Epoch 5:
Training loss = 101.911728, training accuracy = 0.966179
Evaluation accuracy = 0.974095, Elapsed Time = 0.529574s
Starting Epoch 6:
Training loss = 90.820244, training accuracy = 0.969969
Evaluation accuracy = 0.980983, Elapsed Time = 0.530502s
Starting Epoch 7:
Training loss = 86.718071, training accuracy = 0.971037
Evaluation accuracy = 0.977590, Elapsed Time = 0.531085s
Starting Epoch 8:
Training loss = 79.507553, training accuracy = 0.973675
Evaluation accuracy = 0.976562, Elapsed Time = 0.529935s
Starting Epoch 9:
Training loss = 78.784409, training accuracy = 0.974025
Evaluation accuracy = 0.980469, Elapsed Time = 0.530919s