# Design Doc: Operation Graph Based Parameter Server

## Abstract

We propose an approach to implement the parameter server. In this approach, there is no fundamental difference between the trainer and the parameter server: they both run subgraphs, but subgraphs of different purposes.

## Background

The previous implementations of the parameter server does not run a subgraph. parameter initialization, optimizer computation, network communication and checkpointing are implemented twice on both the trainer and the parameter server.

It would be great if we can write code once and use them on both the trainer and the parameter server: reduces code duplication and improves extensibility. Given that after the current refactor, we are representing everything as a computing graph on the trainer. Representing everything as a computing graph on the parameter server becomes a natural extension.

## Design

### Graph Converter

The graph converter converts the user-defined operation (OP) graph into subgraphs to be scheduled on different nodes with the following steps:

1. OP placement: the OPs will be placed on different nodes according to heuristic that minimizes estimated total computation time. Currently we will use a simple heuristic that puts parameter varable on parameter server workers and everything else on trainer workers.
2. Add communication OPs to enable the communication between nodes.

We will need these OPs: Send, Recv, Enqueue, Dequeue.

Below is an example of converting the user defined graph to the subgraphs for the trainer and the parameter server:

After converting:

1. The parameter variable W and it’s optimizer subgraph are placed on the parameter server.
2. Operators are added to the subgraphs.
   - Send sends data to the connected Recv operator. The scheduler on the receive node will only schedule Recv operator to run when the Send operator has ran (the Send OP will mark the Recv OP runnable automatically).
   - Enueue enqueues the input variable, it can block until space become available in the queue.
   - Dequeue outputs configurable numbers of tensors from the queue. It will block until the queue have the required number of tensors.

### Benefits

- Model parallelism become easier to implement: it’s an extension to the trainer - parameter server approach. we already have the communication OPs, but need to extend the graph converter’s placement functionality.
- User-defined optimizer is easier to add - user can now express it as a subgraph.
- No more duplication logic inside the trainer and the parameter server mentioned in the background section.

### Challenges

- It might be hard for the graph converter to cut a general graph (without any hint for which subgraph is the optimizer). We may need to label which subgraph inside the OP graph is the optimizer.
- It’s important to balance the parameter shards of on multiple parameter server. If a single parameter is very big (some word-embedding, fully connected, softmax layer), we need to automatically partition the single parameter onto different parameter servers when possible (only element-wise optimizer depends on the parameter variable).

### Discussion

- In the “Aync SGD” figure, the “W” variable on the parameter server could be read and wrote concurrently, what is our locking strategy? E.g., each variable have a lock cpp method to be invoked by every OP, or, have a lock OP.
- Can the Enqueue OP be implemented under our current tensor design (puts the input tensor into the queue tensor)?
- Dequeue OP will have variable numbers of output (depends on the `min_count` attribute), does our current design support it? (similar question for the Add OP)

### References:

[1] TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems