Accelerating Foundational Model Training: A Systematic Review of Hardware, Algorithmic, and Distributed Computing Optimizations

Athul Ramkumar

doi:10.36948/ijfmr.2024.v06i06.32140

Accelerating Foundational Model Training: A Systematic Review of Hardware, Algorithmic, and Distributed Computing Optimizations

Author(s)	Athul Ramkumar
Country	United States
Abstract	The exponential growth in the size and complexity of foundation models has precipitated an urgent need for more efficient training methodologies. This article presents a comprehensive analysis of training acceleration strategies across three fundamental domains: hardware optimization, algorithmic improvements, and distributed computing frameworks. The investigation reveals that a synergistic approach combining specialized hardware accelerators (TPUs/GPUs) with advanced algorithmic techniques, including sparse modeling and adaptive optimization, can reduce training time by up to 67% compared to traditional methods. We demonstrate that implementing mixed-precision training alongside pipeline parallelism and optimal checkpointing strategies yields particularly promising results, achieving a 3.2x speedup while maintaining model accuracy within 0.5% of baseline performance. Through extensive experimentation with large-scale language models ranging from 1B to 175B parameters, The article identifies critical bottlenecks and proposes a novel framework for balancing the trade-offs between training speed, computational cost, and model quality. The findings indicate that careful orchestration of hardware-aware algorithms with distributed computing strategies can significantly improve training efficiency while preserving model performance. Additionally, The article presents a systematic evaluation of various acceleration techniques' scalability and cost-effectiveness, providing practical guidelines for researchers and practitioners in the field of artificial intelligence. This article contributes to the growing body of knowledge on efficient model training and offers valuable insights for the future development of large-scale AI systems.
Keywords	Keywords: Model Architecture Optimization, Deep Learning Infrastructure, Large-scale Machine Learning, Neural Network Training, High-Performance Computing.
Field	Computer
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-12-04
Cite This	Accelerating Foundational Model Training: A Systematic Review of Hardware, Algorithmic, and Distributed Computing Optimizations - Athul Ramkumar - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.32140
DOI	https://doi.org/10.36948/ijfmr.2024.v06i06.32140
Short DOI	https://doi.org/g8tv8w

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 1 Cover Page Vol 6 Isu 6 Cover Page Vol 6 Isu 5

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Accelerating Foundational Model Training: A Systematic Review of Hardware, Algorithmic, and Distributed Computing Optimizations

Share this