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Executive Summary The effective management of memory in heterogeneous computing environments—encompassing Central Processing Units (CPUs) and accelerators such as Graphics Processing Units (GPUs)—represents one of the most critical challenges in Read More …
1. Introduction: The Memory Wall in Massively Parallel Computing In the domain of High-Performance Computing (HPC) and deep learning, the performance of Massively Parallel Processing (MPP) systems is governed less 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 …
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 …
Executive Summary This report provides a comprehensive architectural and economic analysis of the two dominant high-performance memory technologies, High Bandwidth Memory (HBM) and Graphics Double Data Rate (GDDR). It frames Read More …