Scientific workflow scheduling problem is a combinatorial optimization problem. In the real application, the scientific workflow generally has thousands of task nodes. Scheduling large-scale workflow has huge computational overhead. In this paper, a parallel algorithm for scientific workflow scheduling is proposed so that the computing speed can be improved greatly. Our method used ant colony optimization approaches on the GPU. Thousands of GPU threads can parallel construct solutions. The parallel ant colony algorithm for workflow scheduling was implemented with CUDA C language. Scheduling problem instances with different scales were tested both in our parallel algorithm and CPU sequential algorithm. The experimental results on NVIDIA Tesla M2070 GPU show that our implementation for 1000 task nodes runs in 5 seconds, while a conventional sequential algorithm implementation runs in 104 seconds on Intel Xeon X5650 CPU. Thus, our GPU-based parallel algorithm implementation attains a speed-up factor of 20.7.
목차
Abstract 1. Introduction 2. Problem Description 3. The Design and GPU Implementation of Parallel Ant Colony Algorithm 3.1. Algorithm for Scientific Workflow Scheduling 3.2. Node Selection Rules 3.3. Pheromone Update Rule 3.4. GPU-based Algorithm Implementation 4. Performance Evaluation 4.1. Experimental Settings 4.2. Experimental Results 5. Conclusions Acknowledgements References
보안공학연구지원센터(IJGDC) [Science & Engineering Research Support Center, Republic of Korea(IJGDC)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Grid and Distributed Computing
간기
격월간
pISSN
2005-4262
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Grid and Distributed Computing Vol.8 No.4