Rok 2024

• Investigation of Performance and Energy Consumption of Tokenization Algorithms on Multi-core CPUs Under Power Capping

  Publikacja

  • O. Diakun
  • J. Dobrosolski
  • P. Czarnul

  - Rok 2024

  In this paper we investigate performance-energy optimization of tokenizer algorithm training using power capping. We focus on parallel, multi-threaded implementations of Byte Pair Encoding (BPE), Unigram, WordPiece, and WordLevel run on two systems with different multi-core CPUs: Intel Xeon 6130 and desktop Intel i7-13700K. We analyze execution times and energy consumption for various numbers of threads and various power caps and...

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• Neural network agents trained by declarative programming tutors

  Publikacja

  • J. Dobrosolski
  • J. Szymański
  • H. Mora
  • K. Draszawka

  - Rok 2024

  This paper presents an experimental study on the development of a neural network-based agent, trained using data generated using declarative programming. The focus of the study is the application of various agents to solve the classic logic task – The Wumpus World. The paper evaluates the effectiveness of neural-based agents across different map configurations, offering a comparative analysis to underline the strengths and limitations...

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• Performance and Energy Aware Training of a Deep Neural Network in a Multi-GPU Environment with Power Capping

  Publikacja

  • G. Koszczał
  • J. Dobrosolski
  • M. Matuszek
  • P. Czarnul

  - Rok 2024

  In this paper we demonstrate that it is possible to obtain considerable improvement of performance and energy aware metrics for training of deep neural networks using a modern parallel multi-GPU system, by enforcing selected, non-default power caps on the GPUs. We measure the power and energy consumption of the whole node using a professional, certified hardware power meter. For a high performance workstation with 8 GPUs, we were...

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