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Executive Summary: The proliferation of large-scale models and massive datasets has made distributed training a fundamental requirement for modern machine learning. Navigating the ecosystem of tools designed to facilitate this Read More …
Section 1: Executive Summary The Zero Redundancy Optimizer (ZeRO) represents a paradigm-shifting technology from Microsoft Research, engineered to dismantle the memory bottlenecks that have historically constrained large-scale distributed training of Read More …
Executive Summary The exponential growth in the size of deep learning models has precipitated a significant challenge in high-performance computing: the “memory wall.” Traditional distributed training methods, particularly Distributed Data Read More …
Section 1: The Challenge of Scale and the Parallelism Paradigms 1.1 The Memory and Compute Wall in Modern Deep Learning The field of deep learning, particularly in natural language processing Read More …
The Paradigm of Data Parallelism in Deep Learning Data parallelism is a foundational strategy in Data-Parallel computing that has become the most prevalent method for accelerating the training of deep Read More …
I. Foundational Principles of Model Parallelism 1.1. The Imperative for Scaling: The Memory Wall The field of deep learning is characterized by a relentless pursuit of scale. State-of-the-art models, particularly Read More …
Introduction: The Imperative for Parallelism in Modern Deep Learning The landscape of artificial intelligence is defined by a relentless pursuit of scale. The performance and capabilities of deep learning models Read More …
Section 1: Introduction – The Evolution of Parallel Graphics Processing 1.1 The Foundational Premise of Multi-GPU Scaling The principle of multi-GPU (Graphics Processing Unit) scaling is rooted in the fundamental Read More …