add benchmark

python/paddle_fl/mpc/README.md

@@ -157,7 +157,15 @@ To make the MPC training run, we need to deploy the training processes on multip
 1. Start a Redis service, and keep the service address:
 
 ```sh
-redis-server --port ${port}
+# we provide a stable redis package for you to download 
+
+wget https://paddlefl.bj.bcebos.com/redis-stable.tar --no-check-certificate
+tar -xf redis-stable.tar
+cd redis-stable && make
+
+# start service
+cd src
+./redis-server --port ${port}
 ```
 
 2. Deploy the above `train.py` on three machines, and run with different role settings (from 0 to 2):
@@ -176,7 +184,34 @@ Then the training process will start and the underlying MPC-based operators will
 
 ## Benchmark Task
 
-put result here as a table? | DataSet/Task | training methods | Result | | --- | --- | --- |
+#### Convergence of paddle_fl.mpc vs paddle 
+
+- Dataset: Boston house price dataset
+- Number of Epoch: 20
+- Batch Size: 10
+- Loss of Step 0 in each Epoch
+
+| Epoch/Step | paddle_fl.mpc | Paddle |
+| Epoch=0, Step=0  | 738.39491 | 738.46204 |
+| Epoch=1, Step=0  | 630.68834 | 629.9071 |
+| Epoch=2, Step=0  | 539.54683 | 538.1757 |
+| Epoch=3, Step=0  | 462.41159 | 460.64722 |
+| Epoch=4, Step=0  | 397.11516 | 395.11017 |
+| Epoch=5, Step=0  | 341.83102 | 339.69815 |
+| Epoch=6, Step=0  | 295.01114 | 292.83597 |
+| Epoch=7, Step=0  | 255.35141 | 253.19429 |
+| Epoch=8, Step=0  | 221.74739 | 219.65132 |
+| Epoch=9, Step=0  | 193.26459 | 191.25981 |
+| Epoch=10, Step=0  | 169.11423 | 167.2204 |
+| Epoch=11, Step=0  | 148.63138 | 146.85835 |
+| Epoch=12, Step=0  | 131.25081 | 129.60391 |
+| Epoch=13, Step=0  | 116.49708 | 114.97599 |
+| Epoch=14, Step=0  | 103.96669 | 102.56854 |
+| Epoch=15, Step=0  | 93.31706 | 92.03858 |
+| Epoch=16, Step=0  | 84.26219 | 83.09653 |
+| Epoch=17, Step=0  | 76.55664 | 75.49785 |
+| Epoch=18, Step=0  | 69.99673 | 69.03561 |
+| Epoch=19, Step=0  | 64.40562 | 63.53539 |
 
 ## On Going and Future Work