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Solving a Memory Bottleneck with Parallelism
A research team is training a model on a server with four GPUs, each with 24 GB of memory. They consistently encounter an 'out-of-memory' error. Profiling tools indicate that the error occurs during a single matrix multiplication involving a weight matrix that requires 26 GB of memory. Explain the specific parallelization technique designed to solve this problem and describe how it would distribute the computation across the four available GPUs.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Application in Bloom's Taxonomy
Cognitive Psychology
Psychology
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Empirical Science
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Two-Level Tile-Based Approach in Tensor Parallelism
A machine learning engineer is training a model with an exceptionally large layer. The weight matrix for this single layer is so large that it cannot fit into the memory of one GPU, causing an 'out-of-memory' error during the matrix multiplication step. Which of the following strategies directly addresses this specific memory bottleneck by parallelizing the problematic matrix multiplication itself across multiple devices?
Solving a Memory Bottleneck with Parallelism
Analyzing Distributed Matrix Multiplication Strategies
Example of Tensor Parallelism in an FFN Sub-layer