ANYTIME MINIBATCH: EXPLOITING STRAGGLERS IN ONLINE DISTRIBUTED OPTIMIZATION
Distributed optimization is vital in solving large-scale machine learning problems. A widely-shared feature of distributed optimization techniques is the requirement that all nodes complete their assigned tasks in each computational epoch before the system can proceed to the next epoch. In such settings, slow nodes, called stragglers, can greatly slow progress. To mitigate the impact of stragglers, we propose an online distributed optimization method called Anytime Minibatch. In this approach, all nodes are given a fixed time to compute the gradients of as many data samples as possible. The result is a variable per-node minibatch size. Workers then get a fixed communication time to average their minibatch gradients via several rounds of consensus, which are then used to update primal variables via dual averaging. Anytime Minibatch prevents stragglers from holding up the system without wasting the work that stragglers can complete. We present a convergence analysis and analyze the wall time performance. Our numerical results show that our approach is up to 1.5 times faster in Amazon EC2 and it is up to five times faster when there is greater variability in compute node performance.
 Overall, this paper is well written with clearly presentation. However, the contribution is not good enough to research the ICLR requirement. Although the authors propose some method to balance the computation between distributed workers, which should be important for distributed optimization algorithm design, but not enough numerical experiments are proposed to prove the efficiency. Even though some convergence analysis is given. The main concern of this paper is to significantly increase the algorithm efficiency, but both theoretical and numerical results are lack of strength.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
ANYTIME MINIBATCH: EXPLOITING STRAGGLERS IN ONLINE DISTRIBUTED OPTIMIZATION
Distributed optimization is vital in solving large-scale machine learning problems. A widely-shared feature of distributed optimization techniques is the requirement that all nodes complete their assigned tasks in each computational epoch before the system can proceed to the next epoch. In such settings, slow nodes, called stragglers, can greatly slow progress. To mitigate the impact of stragglers, we propose an online distributed optimization method called Anytime Minibatch. In this approach, all nodes are given a fixed time to compute the gradients of as many data samples as possible. The result is a variable per-node minibatch size. Workers then get a fixed communication time to average their minibatch gradients via several rounds of consensus, which are then used to update primal variables via dual averaging. Anytime Minibatch prevents stragglers from holding up the system without wasting the work that stragglers can complete. We present a convergence analysis and analyze the wall time performance. Our numerical results show that our approach is up to 1.5 times faster in Amazon EC2 and it is up to five times faster when there is greater variability in compute node performance.
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 with clearly presentation. However, the contribution is not good enough to research the ICLR requirement. Although the authors propose some method to balance the computation between distributed workers, which should be important for distributed optimization algorithm design, but not enough numerical experiments are proposed to prove the efficiency. Even though some convergence analysis is given. The main concern of this paper is to significantly increase the algorithm efficiency, but both theoretical and numerical results are lack of strength.
assistant
 Overall, this paper is well written with clearly presentation. However, the contribution is not good enough to research the ICLR requirement. Although the authors propose some method to balance the computation between distributed workers, which should be important for distributed optimization algorithm design, but not enough numerical experiments are proposed to prove the efficiency. Even though some convergence analysis is given. The main concern of this paper is to significantly increase the algorithm efficiency, but both theoretical and numerical results are lack of strength.