docs: add bytescheduler reference in readme (#94)

* add bytescheduler reference in byteps readme

* add bytescheduler reference to publications section

* add paper reference

README.md

@@ -70,11 +70,15 @@ If your tasks only rely on Horovod's allreduce and broadcast, you should be able
 Many of our examples were copied from Horovod and modified in this way. For instance, compare the MNIST example for [BytePS](https://github.com/bytedance/byteps/blob/master/example/tensorflow/tensorflow_mnist.py) and [Horovod](https://github.com/horovod/horovod/blob/master/examples/tensorflow_mnist.py).
 
 ## Limitations and Future Plans
-
 BytePS does not support pure CPU training for now. One reason is that the [cheap PS assumption](docs/rationale.md) of BytePS do not hold for CPU training. Consequently, you need CUDA and NCCL to build and run BytePS.
 
 We would like to have below features, and it is not hard to implement them in BytePS architecture. However, they are not implemented yet:
 * Sparse model training
 * Asynchronous training
 * Fault-tolerance
 * Straggler-mitigation
+
+## Publications
+BytePS adopts similar ideas in [ByteScheduler](https://github.com/bytedance/byteps/tree/bytescheduler/bytescheduler), e.g., tensor partitioning and credit-based preemptive scheduling, but with a different system design as it works as a communication library under the framework engine layer. To access ByteScheduler's source code, check the bytescheduler folder in bytescheduler branch of this repo [here](https://github.com/bytedance/byteps/tree/bytescheduler/bytescheduler). You can also find more details about ByteScheduler in the following [paper](https://i.cs.hku.hk/~cwu/papers/yhpeng-sosp19.pdf):
+
+Yanghua Peng, Yibo Zhu, Yangrui Chen, Yixin Bao, Bairen Yi, Chang Lan, Chuan Wu, Chuanxiong Guo. "A Generic Communication Scheduler for Distributed DNN Training Acceleration," in ACM SOSP, Huntsville, Ontario, Canada, October 27-30, 2019.