Poster (Other)

FZJ-2024-06308

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png CARAML: Systematic Evaluation of AI Workloads on Accelerators

John, C. M.FZJ* ; Nassyr, S. (Corresponding author)FZJ* ; Herten, A. (Corresponding author)FZJ* ; Penke, C. (Corresponding author)FZJ*

2024

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Please use a persistent id in citations: doi:10.34734/FZJ-2024-06308

Abstract: The rapid advancement of machine learning (ML) technologies has driven the development of specialized hardware accelerators designed to facilitate more efficient model training. This paper introduces the CARAML benchmark suite, which is employed to assess performance and energy consumption during the training of transformer-based large language models and computer vision models on a range of hardware accelerators, including systems from NVIDIA, AMD, and Graphcore. CARAML provides a compact, automated, extensible, and reproducible framework for assessing the performance and energy of ML workloads across various novel hardware architectures. The design and implementation of CARAML, along with a custom power measurement tool called jpwr, are discussed in detail.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
2. 5122 - Future Computing & Big Data Systems (POF4-512) (POF4-512)
3. OpenGPT-X - Aufbau eines Gaia-X Knotens für große KI-Sprachmodelle und innovative Sprachapplikations-Services; Teilvorhaben: Optimierung und Skalierung auf großen HPC-Systemen (68GX21007F) (68GX21007F)
4. MAELSTROM - MAchinE Learning for Scalable meTeoROlogy and cliMate (955513) (955513)
5. Verbundprojekt: MAELSTROM - Skalierbarkeit von Anwendungen des Maschinellen Lernens in den Bereichen Wetter und Klimawissenschaften für das zukünftige Supercomputing (16HPC029) (16HPC029)
6. ATML-X-DEV - ATML Accelerating Devices (ATML-X-DEV) (ATML-X-DEV)

Appears in the scientific report 2024

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Database coverage:

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