Communication-Efficient, 2D Parallel Stochastic Gradient Descent for Distributed-Memory Optimization

• URL: http://arxiv.org/abs/2501.07526v1
• Date: Mon, 13 Jan 2025 17:56:39 GMT
• Title: Communication-Efficient, 2D Parallel Stochastic Gradient Descent for Distributed-Memory Optimization
• Authors: Aditya Devarakonda, Ramakrishnan Kannan,
• Abstract summary: This work generalizes work on 1D $s$-step SGD and 1D Federated SGD with Averaging (FedAvg) to yield a 2D parallel SGD method (HybridSGD) We implement all algorithms in C++ and MPI and evaluate their performance on a Cray EX supercomputing system.
• Score: 2.2596489829928452
• License:
• Abstract: Distributed-memory implementations of numerical optimization algorithm, such as stochastic gradient descent (SGD), require interprocessor communication at every iteration of the algorithm. On modern distributed-memory clusters where communication is more expensive than computation, the scalability and performance of these algorithms are limited by communication cost. This work generalizes prior work on 1D $s$-step SGD and 1D Federated SGD with Averaging (FedAvg) to yield a 2D parallel SGD method (HybridSGD) which attains a continuous performance trade off between the two baseline algorithms. We present theoretical analysis which show the convergence, computation, communication, and memory trade offs between $s$-step SGD, FedAvg, 2D parallel SGD, and other parallel SGD variants. We implement all algorithms in C++ and MPI and evaluate their performance on a Cray EX supercomputing system. Our empirical results show that HybridSGD achieves better convergence than FedAvg at similar processor scales while attaining speedups of $5.3\times$ over $s$-step SGD and speedups up to $121\times$ over FedAvg when used to solve binary classification tasks using the convex, logistic regression model on datasets obtained from the LIBSVM repository.

Related papers

• Near-Optimal Online Learning for Multi-Agent Submodular Coordination: Tight Approximation and Communication Efficiency [52.60557300927007]
  We present a $textbfMA-OSMA$ algorithm to transfer the discrete submodular problem into a continuous optimization. We also introduce a projection-free $textbfMA-OSEA$ algorithm, which effectively utilizes the KL divergence by mixing a uniform distribution. Our algorithms significantly improve the $(frac11+c)$-approximation provided by the state-of-the-art OSG algorithm.
  arXiv  Detail & Related papers  (2025-02-07T15:57:56Z)
• AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms [45.90015262911875]
  We analyze asynchronous-type algorithms for distributed SGD in the heterogeneous setting. As a by-product of our analysis, we also demonstrate guarantees for gradient-type algorithms such as SGD with random tightness.
  arXiv  Detail & Related papers  (2023-10-31T13:44:53Z)
• Convergence Analysis of Decentralized ASGD [1.8710230264817358]
  We present a novel convergence-rate analysis for decentralized asynchronous SGD (DASGD) which does not require partial synchronization among nodes nor restrictive network topologies. Our convergence proof holds for a fixed stepsize and any nonsmooth, homogeneous, L-shaped objective function.
  arXiv  Detail & Related papers  (2023-09-07T14:50:31Z)
• On Convergence of Incremental Gradient for Non-Convex Smooth Functions [63.51187646914962]
  In machine learning and network optimization, algorithms like shuffle SGD are popular due to minimizing the number of misses and good cache. This paper delves into the convergence properties SGD algorithms with arbitrary data ordering.
  arXiv  Detail & Related papers  (2023-05-30T17:47:27Z)
• Communication-Efficient Adam-Type Algorithms for Distributed Data Mining [93.50424502011626]
  We propose a class of novel distributed Adam-type algorithms (emphi.e., SketchedAMSGrad) utilizing sketching. Our new algorithm achieves a fast convergence rate of $O(frac1sqrtnT + frac1(k/d)2 T)$ with the communication cost of $O(k log(d))$ at each iteration.
  arXiv  Detail & Related papers  (2022-10-14T01:42:05Z)
• Scaling up Stochastic Gradient Descent for Non-convex Optimisation [5.908471365011942]
  We propose a novel approach to the problem of shared parallel computation. By combining two strategies into a unified framework, DPSGD is a better trade computation framework. The potential gains can be achieved by DPSGD on a deep learning (DRL) problem (Latent Diletrichal inference) and on a deep learning (DRL) problem (advantage actor - A2C)
  arXiv  Detail & Related papers  (2022-10-06T13:06:08Z)
• Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning [77.22019100456595]
  We show a training algorithm for distributed computation workers with varying communication frequency. In this work, we obtain a tighter convergence rate of $mathcalO!!!(sigma2-2_avg!! . We also show that the heterogeneity term in rate is affected by the average delay within each worker.
  arXiv  Detail & Related papers  (2022-06-16T17:10:57Z)
• Gradient Coding with Dynamic Clustering for Straggler-Tolerant Distributed Learning [55.052517095437]
  gradient descent (GD) is widely employed to parallelize the learning task by distributing the dataset across multiple workers. A significant performance bottleneck for the per-iteration completion time in distributed synchronous GD is $straggling$ workers. Coded distributed techniques have been introduced recently to mitigate stragglers and to speed up GD iterations by assigning redundant computations to workers. We propose a novel dynamic GC scheme, which assigns redundant data to workers to acquire the flexibility to choose from among a set of possible codes depending on the past straggling behavior.
  arXiv  Detail & Related papers  (2021-03-01T18:51:29Z)
• Avoiding Communication in Logistic Regression [1.7780157772002312]
  gradient descent (SGD) is one of the most widely used optimization methods for solving various machine learning problems. In a parallel setting, SGD requires interprocess communication at every iteration. We introduce a new communication-avoiding technique for solving the logistic regression problem using SGD.
  arXiv  Detail & Related papers  (2020-11-16T21:14:39Z)
• Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling [56.394284787780364]
  This paper provides the first theoretical convergence analysis for two fundamental RL algorithms of policy gradient (PG) and temporal difference (TD) learning. Under general nonlinear function approximation, PG-AMSGrad with a constant stepsize converges to a neighborhood of a stationary point at the rate of $mathcalO(log T/sqrtT)$. Under linear function approximation, TD-AMSGrad with a constant stepsize converges to a neighborhood of the global optimum at the rate of $mathcalO(log T/sqrtT
  arXiv  Detail & Related papers  (2020-02-15T00:26:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.