한국정보과학회논문지:시스템및이론 (Journal of KIISE:Computer Systems and Theory)

한국정보과학회 (Korean Institute of Information Scientists and Engineers)
권호 표지

계층 자료구조의 결합과 3차원 클러스터링을 이용하여 적응적으로 부하 균형된 GPU-클러스터 기반 병렬 볼륨 렌더링

Adaptive Load Balancing Scheme using a Combination of Hierarchical Data Structures and 3D Clustering for Parallel Volume Rendering on GPU Clusters

  • 이원종 (연세대학교 컴퓨터과학과) ;
  • 박우찬 (세종대학교 인터넷공학과) ;
  • 한탁돈 (연세대학교 컴퓨터과학과)
  • 발행 : 2006.02.01
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⟨ 이전 논문 다음 논문 ⟩

초록

대용량 볼륨 데이타를 가시화하는 효과적인 방법인 후-정열 병렬 렌더링은 부하균형에 의해 성능이 결정된다. 기존의 정적 데이타 분할 방법은 태스크 병렬성만의 관점에서는 자기균형을 쉽게 얻을 수 있었지만, 데이타 내부의 빈 공간을 고려하지 않았기 때문에 데이타 병렬성의 관점에서는 심각한 불균형을 초래할 수 있었다. 본 논문은 태스크 병렬성과 데이타 병렬성이 함께 고려된, 적응적이며 확장적인 부하 균형 기법을 제안한다. 우리는 계층적 자료 구조인 옥트리와 BSP-트리를 효과적으로 결합하여 볼륨 데이타의 실제 영역만을 추출하여 렌더링 노드들로 균등하게 분산시켰으며, 각 렌더링 노드들에서는 3차원 클러스터링 알고리즘을 적용하여 렌더링 순서를 효과적으로 결정하였다. 제안하는 방법은 기존의 정적 데이타 분산 기법에 비해 최대 22배의 병렬성을 높였고 동기화 비용을 낮추어 렌더링 성능을 크게 향상시켰음을 실험을 통해 알 수 있었다.

Sort-last parallel rendering using a cluster of GPUs has been widely used as an efficient method for visualizing large- scale volume datasets. The performance of this method is constrained by load balancing when data parallelism is included. In previous works static partitioning could lead to self-balance when only task level parallelism is included. In this paper, we present a load balancing scheme that adapts to the characteristic of volume dataset when data parallelism is also employed. We effectively combine the hierarchical data structures (octree and BSP tree) in order to skip empty regions and distribute workload to corresponding rendering nodes. Moreover, we also exploit a 3D clustering method to determine visibility order and save the AGP bandwidths on each rendering node. Experimental results show that our scheme can achieve significant performance gains compared with traditional static load distribution schemes.

키워드

참고문헌

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