Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Comparison of Voxel Map and Sphere Tree Structures for Proximity Computation of Protein Molecules

단백질 분자에 대한 proximity 연산을 위한 복셀 맵과 스피어 트리 구조 비교

  • 김병주 (경북대학교 전자공학과 대학원) ;
  • 이정은 (경북대학교 전자전기컴퓨터학부 대학원) ;
  • 김영준 (이화여자대학교 컴퓨터공학전공) ;
  • 김구진 (경북대학교 컴퓨터학부)
  • Received : 2012.01.11
  • Accepted : 2012.04.19
  • Published : 2012.06.30
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Abstract

For the geometric computations on the protein molecules, the proximity queries, such as computing the minimum distance from an arbitrary point to the molecule or detecting the collision between a point and the molecule, are essential. For the proximity queries, the efficiency of the computation time can be different according to the data structure used for the molecule. In this paper, we present the data structures and algorithms for applying proximity queries to a molecule with GPU acceleration. We present two data structures, a voxel map and a sphere tree, where the molecule is represented as a set of spheres, and corresponding algorithms. Moreover, we show that the performance of presented data structures are improved from 3 to 633 times compared to the previous data structure for the molecules containing 1,000~15,000 atoms.

단백질 분자에 대해 공간 상의 한 점으로부터의 최소 거리를 계산하거나, 임의의 점에 대한 충돌을 감지하는 등의 proximity query는 분자에 대한 기하학적 연산을 수행하기 위해 매우 중요한 기본 연산이다. Proximity query의 계산 시간 효율성은 분자가 어떤 자료구조로 표현되는가에 따라 크게 달라질 수 있다. 본 논문에서는 GPU 가속을 이용하여 효율적으로 proximity 연산을 수행하기 위한 기법을 제안하고자 한다. 분자에 대응하는 구의 집합에 대해 복셀 맵 (voxel map)과 스피어 트리 (sphere tree) 를 사용한 자료구조를 제안하며 각 자료구조에 대응되는 알고리즘을 제시한다. 또한, 1,000개~15,000개의 원자를 포함하는 분자에 대한 실험을 통해 두 자료구조의 성능이 기존 자료구조에 비해 최소 3배에서 최대 633배 향상되었음을 보인다.

Keywords

Acknowledgement

Supported by : 한국연구재단

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