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The Tyranny of Quadratic Complexity: Deconstructing the Transformer Inference Bottleneck The Transformer architecture has become the de facto standard for state-of-the-art models across numerous domains, from natural language processing to Read More …
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 …
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 …
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 …
Executive Summary Gradient accumulation is a pivotal technique in modern deep learning, designed to enable the training of models with large effective batch sizes on hardware constrained by limited memory.1 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 …
Executive Summary The selection of hardware for training deep learning models has evolved into a critical strategic decision, with Graphics Processing Unit (GPU) and Tensor Processing Unit (TPU) representing two Read More …
The Anatomy of LLM Inference and Its Intrinsic Bottlenecks The deployment of Large Language Models (LLM) in production environments has shifted the focus of the machine learning community from training-centric Read More …
Introduction: The Shifting Landscape of Sequence Modeling The field of sequence modeling was fundamentally reshaped in 2017 with the introduction of the Transformer architecture. Its core innovation, the self-attention mechanism, Read More …