Profiling Apple Silicon Performance for ML Training Article Swipe

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Dawei Feng , Zhiming Xu , W. Walkowiak , Felix Xiaozhu Lin ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2501.14925

Apple Silicon has attracted much attention for its performance and role in machine learning (ML) training. Unlike NVIDIA GPUs, which have traditionally dominated ML training, Apple Silicon has a significant difference in memory architecture. It uses Unified Memory, which integrates CPU and GPU memory instead of separate CPU memory and GPU VRAM. However, it is difficult to tell whether Unified Memory means more performance benefits. This paper investigates the performance differences by training several large language model (LLM) workloads end-to-end under different memory scenarios. The results show a significant performance gap between Apple Silicon and NVIDIA GPUs. This paper attributes this gap to system-level factors such as page faults, power consumption, and kernel launch time. In addition, the performance difference of basic linear algebra subprograms (BLAS) on the NVIDIA GPUs and Apple Silicon chips is analyzed to further explain the observed gap.

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Profiling (computer programming) Silicon Computer science Materials science Optoelectronics Operating system

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2501.14925
PDF: https://arxiv.org/pdf/2501.14925
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4406879736

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DOI: https://doi.org/10.48550/arxiv.2501.14925

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Title: Profiling Apple Silicon Performance for ML Training

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-01-24

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Authors: Dawei Feng, Zhiming Xu, W. Walkowiak, Felix Xiaozhu Lin

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Landing page: https://arxiv.org/abs/2501.14925

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PDF URL: https://arxiv.org/pdf/2501.14925

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OA URL: https://arxiv.org/pdf/2501.14925

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Concepts: Profiling (computer programming), Silicon, Computer science, Materials science, Optoelectronics, Operating system

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Cited by: 0

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