Hey all, As you know, the lastest Apache Hama provides distributed training of an Artificial Neural Network using its BSP computing engine. In general, the training data is stored in HDFS and is distributed in multiple machines. In Hama, two kinds of components are involved in the training procedure: the master task and the groom task. The master task is in charge of merging the model updating information and sending model updating information to all the groom tasks. The groom tasks is in charge of calculate the weight updates according to the training data.
The training procedure is iterative and each iteration consists of two phases: update weights and merge update. In the update weights phase, each groom task would first update the local model according to the received message from the master task. Then they would compute the weight updates locally with assigned data partitions (mini-batch SGD) and finally send the updated weights to the master task. In the merge update phase, the master task would update the model according to the messages received from the groom tasks. Then it would distribute the updated model to all groom tasks. The two phases will repeat alternatively until the termination condition is met (reach a specified number of iterations). The model is designed in a hierarchical way. The base class is more abstract than the derived class, so that the structure of the ANN model can be freely set by the user, as long as it is a layered model. Therefore, the Perceptron, Auto-encoder, Linear and Logistic regressor can all be uniformly represented by an ANN. However, as described in above, currently the data parallelism is only used. Each node will have a copy of the model. In each iteration, the computation is conducted on each node and a final aggregation is conducted in one node. Then the updated model will be synchronized to each node. So, the performance is one thing; the parameters should fit into the memory of a single machine. Here is a tentative near future plan I propose for applications needing large model with huge memory consumptions, moderate computational power for one mini-batch, and lots of training data. The main idea is use of Parameter Server to parallelize model creation and distribute training across machines. Apache Hama framework assigns each split of training data stored in HDFS to each BSP task. Then, the BSP task assigns each of the N threads a small portion of work, much smaller than 1/Nth of the total size of a mini-batch, and assigns new portions whenever they are free. With this approach, faster threads do more work than slower threads. Each thread asynchronously asks the Parameter Server who stores the parameters in distributed machines for an updated copy of its model, computes the gradients on the assigned data, and sends updated gradients back to the parameter server. This architecture is inspired by Google's DistBelief (Jeff Dean et al, 2012). Finally, I have no concrete idea regarding programming interface at the moment but I'll try to provide neuron-centric programming model like Google's Pregel if possible. -- Best Regards, Edward J. Yoon
