A Survey on Memory-Efficient Large-Scale Model Training in AI for Science

• URL: http://arxiv.org/abs/2501.11847v1
• Date: Tue, 21 Jan 2025 03:06:30 GMT
• Title: A Survey on Memory-Efficient Large-Scale Model Training in AI for Science
• Authors: Kaiyuan Tian, Linbo Qiao, Baihui Liu, Gongqingjian Jiang, Dongsheng Li,
• Abstract summary: This survey reviews applications across scientific fields such as biology, medicine, chemistry, and meteorology.<n>We review memory-efficient training techniques for large language models (LLMs) based on the transformer architecture.<n>We demonstrate how tailored memory optimization methods can reduce storage needs while preserving prediction accuracy.
• Score: 20.31466892935848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scientific research faces high costs and inefficiencies with traditional methods, but the rise of deep learning and large language models (LLMs) offers innovative solutions. This survey reviews LLM applications across scientific fields such as biology, medicine, chemistry, and meteorology, underscoring their role in advancing research. However, the continuous expansion of model size has led to significant memory demands, hindering further development and application of LLMs for science. To address this, we review memory-efficient training techniques for LLMs based on the transformer architecture, including distributed training, mixed precision training, and gradient checkpointing. Using AlphaFold 2 as an example, we demonstrate how tailored memory optimization methods can reduce storage needs while preserving prediction accuracy. We also discuss the challenges of memory optimization in practice and potential future directions, hoping to provide valuable insights for researchers and engineers.

Related papers

• Reasoning Under 1 Billion: Memory-Augmented Reinforcement Learning for Large Language Models [53.4530106173067]
  Large language models (LLMs) with reinforcement learning (RL) have shown promising improvements in complex reasoning tasks. RL remains challenging for tiny LLMs with 1 billion parameters or fewer because they lack the necessary pretraining strength to explore effectively. This work introduces a novel intrinsic motivation approach that leverages episodic memory to address this challenge.
  arXiv  Detail & Related papers  (2025-04-03T04:46:17Z)
• LLM Post-Training: A Deep Dive into Reasoning Large Language Models [131.10969986056]
  Large Language Models (LLMs) have transformed the natural language processing landscape and brought to life diverse applications. Post-training methods enable LLMs to refine their knowledge, improve reasoning, enhance factual accuracy, and align more effectively with user intents and ethical considerations.
  arXiv  Detail & Related papers  (2025-02-28T18:59:54Z)
• Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey [51.87875066383221]
  This paper introduces fundamental concepts, traditional methods, and benchmark datasets, then examine the various roles Machine Learning plays in improving CFD. We highlight real-world applications of ML for CFD in critical scientific and engineering disciplines, including aerodynamics, combustion, atmosphere & ocean science, biology fluid, plasma, symbolic regression, and reduced order modeling. We draw the conclusion that ML is poised to significantly transform CFD research by enhancing simulation accuracy, reducing computational time, and enabling more complex analyses of fluid dynamics.
  arXiv  Detail & Related papers  (2024-08-22T07:33:11Z)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark [166.40879020706151]
  This paper proposes a shift towards BP-free, zeroth-order (ZO) optimization as a solution for reducing memory costs during fine-tuning. Unlike traditional ZO-SGD methods, our work expands the exploration to a wider array of ZO optimization techniques. Our study unveils previously overlooked optimization principles, highlighting the importance of task alignment, the role of the forward gradient method, and the balance between algorithm complexity and fine-tuning performance.
  arXiv  Detail & Related papers  (2024-02-18T14:08:48Z)
• Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems [14.355768064425598]
  generative large language models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However, the computational intensity and memory consumption of deploying these models present substantial challenges in terms of serving efficiency. This survey addresses the imperative need for efficient LLM serving methodologies from a machine learning system (MLSys) research perspective.
  arXiv  Detail & Related papers  (2023-12-23T11:57:53Z)
• The Efficiency Spectrum of Large Language Models: An Algorithmic Survey [54.19942426544731]
  The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains. This paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs.
  arXiv  Detail & Related papers  (2023-12-01T16:00:25Z)
• A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture [0.0]
  This study presents a method for implementing generative AI services by utilizing the Large Language Models (LLM) application architecture. The research delves into strategies for mitigating the issue of inadequate data, offering tailored solutions. A significant contribution of this work is the development of a Retrieval-Augmented Generation (RAG) model.
  arXiv  Detail & Related papers  (2023-09-03T07:03:17Z)
• A Survey on Model Compression for Large Language Models [21.768293256849113]
  Large Language Models (LLMs) have transformed natural language processing tasks successfully. Yet, their large size and high computational needs pose challenges for practical use. Model compression has emerged as a key research area to address these challenges.
  arXiv  Detail & Related papers  (2023-08-15T08:31:05Z)

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.