Korean Journal of Cognitive Science (인지과학)

The Korean Society for Cognitive Science (한국인지과학회)
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Analysis tool for the diffusion model using GPU: SNUDM-G

GPU를 이용한 확산모형 분석 도구: SNUDM-G

  • Lee, Dajung (Department of Psychology, Seoul National University) ;
  • Lee, Hyosun (Cognitive Program, Seoul National University) ;
  • Koh, Sungryong (Department of Psychology, Seoul National University)
  • 이다정 (서울대학교 심리학과) ;
  • 이효선 (서울대학교 인지과학협동과정) ;
  • 고성룡 (서울대학교 심리학과)
  • Received : 2022.09.08
  • Accepted : 2022.09.16
  • Published : 2022.09.30
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Abstract

In this paper, we introduce the SNUDM-G, a diffusion model analysis tool with improved computational speed. Although the diffusion model has been applied to explain various cognitive tasks, its use was limited due to computational difficulties. In particular, SNUDM(Koh et al., 2020), one of the diffusion model analysis tools, has a disadvantage in terms of processing speed because it sequentially generates 20,000 data when approximating the diffusion process. To overcome this limitation, we propose to use graphic processing units(GPU) in the process of approximating the diffusion process with a random walk process. Since 20,000 data can be generated in parallel using the graphic processing units, the estimation speed can be increased compared to generating data through sequential processing. As a result of analyzing the data of Experiment 1 by Ratcliff et al. (2004) and recovering the parameters with SNUDM-G using GPU and SNUDM using CPU, SNUDM-G estimated slightly higher values for certain parameters than SNUDM. However, in term of computational speed, SNUDM-G estimated the parameters much faster than SNUDM. This result shows that a more efficient diffusion model analysis for various cognitive tasks is possible using this tool and further suggests that the processing speed of various cognitive models can be improved by using graphic processing units in the future.

이 논문에서는 계산 속도를 개선한 확산모형 분석 도구 SNUDM-G를 소개한다. 확산모형은 다양한 인지과제를 설명하는 데에 적용되어 왔음에도 불구하고 계산적 어려움으로 인해 사용에 제한이 있었다. 특히 확산모형 분석 도구 중 하나인 SNUDM(고성룡 등, 2020)은 확산과정을 근사할 때 2만 개의 자료를 순차적으로 생성하기 때문에 처리 속도 면에서 단점이 있다. 이러한 한계를 극복하기 위해 확산과정을 무작위걷기 방법으로 근사하는 과정에서 그래픽처리장치(GPU)를 사용할 것을 제안한다. 그래픽처리장치를 사용하면 2만 개의 자료를 병렬로 생성할 수 있기 때문에 순차처리로 자료를 생성하는 것에 비해 분석의 속도를 높일 수 있다. GPU를 사용한 SNUDM-G와 CPU를 사용한 SNUDM으로 Ratcliff 등 (2004)의 실험 1 자료를 분석하고 매개변수 복구를 한 결과 SNUDM-G가 SNUDM보다 특정 매개변수에서 다소 높은 값을 추정하였으나, 계산 속도 면에서는 큰 차이로 SNUDM-G가 SNUDM보다 더 빠르게 매개변수를 추정하였다. 이 결과는 이 도구를 이용하여 다양한 인지 과제에 대해 보다 효율적인 확산모형 분석이 가능할 것임을 보여주며, 더 나아가 앞으로 그래픽처리장치를 이용하여 다양한 인지 모형의 처리 속도를 개선할 수 있음을 시사한다.

Keywords

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