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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 …
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 …
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 …