Evaluation of GPU-based empirical mode decomposition for off-line analysis

Pulung Waskito, Shinobu Miwa, Yasue Mitsukura, Hironori Nakajo

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


In off-line analysis, the demand for high precision signal processing has introduced a new method called Empirical Mode Decomposition (EMD), which is used for analyzing a complex set of data. Unfortunately, EMD is highly compute-intensive. In this paper, we show parallel implementation of Empirical Mode Decomposition on a GPU. We propose the use of "partial+total" switching method to increase performance while keeping the precision. We also focused on reducing the computation complexity in the above method from O(N) on a single CPU to O(N/P log (N)) on a GPU. Evaluation results show our single GPU implementation using Tesla C2050 (Fermi architecture) achieves a 29.9x speedup partially, and a 11.8x speedup totally when compared to a single Intel dual core CPU.

Original languageEnglish
Pages (from-to)2328-2337
Number of pages10
JournalIEICE Transactions on Information and Systems
Issue number12
Publication statusPublished - 2011 Dec
Externally publishedYes


  • CUDA
  • Empirical mode decomposition (EMD)
  • GPU
  • Hilbert-huang transform (HHT)

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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