Nested Dithered Quantization for Communication Reduction in Distributed Training
In distributed training, the communication cost due to the transmission of gradients or the parameters of the deep model is a major bottleneck in scaling up the number of processing nodes. To address this issue, we propose dithered quantization for the transmission of the stochastic gradients and show that training with Dithered Quantized Stochastic Gradients (DQSG) is similar to the training with unquantized SGs perturbed by an independent bounded uniform noise, in contrast to the other quantization methods where the perturbation depends on the gradients and hence, complicating the convergence analysis. We study the convergence of training algorithms using DQSG and the trade off between the number of quantization levels and the training time. Next, we observe that there is a correlation among the SGs computed by workers that can be utilized to further reduce the communication overhead without any performance loss. Hence, we develop a simple yet effective quantization scheme, nested dithered quantized SG (NDQSG), that can reduce the communication significantly without requiring the workers communicating extra information to each other. We prove that although NDQSG requires significantly less bits, it can achieve the same quantization variance bound as DQSG. Our simulation results confirm the effectiveness of training using DQSG and NDQSG in reducing the communication bits or the convergence time compared to the existing methods without sacrificing the accuracy of the trained model.
 Overall, this paper is well written and clearly present their contribution. Although the idea seems to be interesting and novel, but not enough evidence to prove the efficiency, from both theoretical and numerical perspective, even though many numerical experiments are proposed. In general, this paper is high level in the articles assigned to me.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Nested Dithered Quantization for Communication Reduction in Distributed Training
In distributed training, the communication cost due to the transmission of gradients or the parameters of the deep model is a major bottleneck in scaling up the number of processing nodes. To address this issue, we propose dithered quantization for the transmission of the stochastic gradients and show that training with Dithered Quantized Stochastic Gradients (DQSG) is similar to the training with unquantized SGs perturbed by an independent bounded uniform noise, in contrast to the other quantization methods where the perturbation depends on the gradients and hence, complicating the convergence analysis. We study the convergence of training algorithms using DQSG and the trade off between the number of quantization levels and the training time. Next, we observe that there is a correlation among the SGs computed by workers that can be utilized to further reduce the communication overhead without any performance loss. Hence, we develop a simple yet effective quantization scheme, nested dithered quantized SG (NDQSG), that can reduce the communication significantly without requiring the workers communicating extra information to each other. We prove that although NDQSG requires significantly less bits, it can achieve the same quantization variance bound as DQSG. Our simulation results confirm the effectiveness of training using DQSG and NDQSG in reducing the communication bits or the convergence time compared to the existing methods without sacrificing the accuracy of the trained model.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 Overall, this paper is well written and clearly present their contribution. Although the idea seems to be interesting and novel, but not enough evidence to prove the efficiency, from both theoretical and numerical perspective, even though many numerical experiments are proposed. In general, this paper is high level in the articles assigned to me.
assistant
 Overall, this paper is well written and clearly present their contribution. Although the idea seems to be interesting and novel, but not enough evidence to prove the efficiency, from both theoretical and numerical perspective, even though many numerical experiments are proposed. In general, this paper is high level in the articles assigned to me.