Similar articles

Abstract: With the increasing energy consumption of computing systems and the growing advocacy for green computing, energy efficiency has become one of the critical challenges in high-performance heterogeneous computing systems. Energy consumption can be reduced by not only hardware design but also software design. In this paper, we propose an energy-aware scheduling algorithm with equalized frequency, called EASEF, for parallel applications on heterogeneous computing systems. The EASEF approach aims to minimize the finish time and overall energy consumption. First, EASEF extracts the set of paths from an application. Then, it reconstructs the application based on the extracted set of paths to achieve a reasonable schedule. Finally, it adopts a progressive way to equalize the frequency of tasks to reduce the total energy consumption of systems. Randomly generated applications and two real-world applications are examined in our experiments. Experimental results show that the EASEF algorithm outperforms two existing algorithms in terms of makespan and energy consumption.

This paper proposes a scheduling method for the heterogeneous computing system to reduce the energy consumption, as called Heterogeneous Energy-Aware Scheduling (HEAS) algorithm, which consists of three stages: 1) generating the critical paths, 2) reconstructing the directed acyclic graph (DAG) and calculating task priority, and 3) scheduling the task with energy-awareness. Overall, the paper is well written and clearly organized.

面向异构系统的节能调度算法

[1]Amador, E., Knopp, R., Pacalet, R., et al., 2012. Dynamic power management for the iterative decoding of turbo codes. IEEE Trans. VLSI Syst., 20(11):2133-2137.

[2]Bajaj, R., Agrawal, D.P., 2004. Improving scheduling of tasks in a heterogeneous environment. IEEE Trans. Parall. Distr. Syst., 15(2):107-118.

[3]Bansal, S., Kumar, P., Singh, K., 2003. An improved duplication strategy for scheduling precedence constrained graphs in multiprocessor systems. IEEE Trans. Parall. Distr. Syst., 14(6):533-544.

[4]Bansal, S., Kumar, P., Singh, K., 2005. Dealing with heterogeneity through limited duplication for scheduling precedence constrained task graphs. J. Parall. Distr. Comput., 65(4):479-491.

[5]Benini, L., Bogliolo, A., de Micheli, G., 2000. A survey of design techniques for system-level dynamic power management. IEEE Trans. VLSI Syst., 8(3):299-316.

[6]Boeres, C., Rebello, V.E.F., 2004. A cluster-based strategy for scheduling task on heterogeneous processors. 16th Symp. on Computer Architecture and High Performance Computing, p.214-221.

[7]Bozdag, D., Ozguner, F., Catalyurek, U.V., 2009. Compaction of schedules and a two-stage approach for duplication-based DAG scheduling. IEEE Trans. Parall. Distr. Syst., 20(6):857-871.

[8]Brown, R., 2008. Report to Congress on Server and Data Center Energy Efficiency: Public Law 109-431. Lawrence Berkeley National Laboratory.

[9]Cormen, T.H., Leiserson, C.E., Rivest, R.L., et al., 2009. Introduction to Algorithms. MIT Press, Cambridge.

[10]Freund, R.F., Siegel, H.J., 1993. Guest editor’s introduction: heterogeneous processing. Computer, 26(6):13-17.

[11]Fu, F.F., Bai, Y.X., Hu, X.A., et al., 2010. An objective-flexible clustering algorithm for task mapping and scheduling on cluster-based NoC. Academic Symposium on Optoelectronics and Microelectronics Technology and 10th Chinese-Russian Symp. on Laser Physics and Laser Technology Optoelectronics Technology, p.369-373.

[12]Hagras, T., Janeček, J., 2005. A high performance, low complexity algorithm for compile-time task scheduling in heterogeneous systems. Parall. Comput., 31(7):653-670.

[13]Huang, Q.J., Su, S., Li, J., et al., 2012. Enhanced energy-efficient scheduling for parallel applications in cloud. 12th IEEE/ACM Int. Symp. on Cluster, Cloud and Grid Computing, p.781-786.

[14]Ilyas, M.U., Khan, S.A., 2001. A clustering heuristic algorithm for scheduling periodic and deterministic tasks on a multiprocessor system. Proc. IEEE Int. Multi Topic Conf., Technology for the 21st Century, p.1-5.

[15]Iverson, M.A., Özgüner, F., Follen, G.J., 1995. Parallelizing existing applications in a distributed heterogeneous environment. 4th Heterogeneous Computing Workshop, p.93-100.

[16]Khan, M.A., 2012. Scheduling for heterogeneous systems using constrained critical paths. Parall. Comput., 38(4-5):175-193.

[17]Kim, S.J., Browne, J.C., 1988. A general approach to mapping of parallel computation upon multiprocessor architectures. Int. Conf. on Parallel Processing, 3:1-8.

[18]Kwok, Y.K., Ahmad, I., 1996. Dynamic critical-path scheduling: an effective technique for allocating task graphs to multiprocessors. IEEE Trans. Parall. Distr. Syst., 7(5):506-521.

[19]Kwok, Y.K., Ahmad, I., 1999. Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput. Surv., 31(4):406-471.

[20]Lee, C.H., Shin, K.G., 2004. On-line dynamic voltage scaling for hard real-time systems using the EDF algorithm. 25th IEEE Int. Real-Time Systems Symp., p.319-335.

[21]Lee, Y.C., Zomaya, A.Y., 2011. Energy conscious scheduling for distributed computing systems under different operating conditions. IEEE Trans. Parall. Distr. Syst., 22(8):1374-1381.

[22]Li, K.Q., 2012. Scheduling precedence constrained tasks with reduced processor energy on multiprocessor computers. IEEE Trans. Comput., 61(12):1668-1681.

[23]Mehta, N., Amrutur, B., 2012. Dynamic supply and threshold voltage scaling for CMOS digital circuits using in-situ power monitor. IEEE Trans. VLSI Syst., 20(5):892-901.

[24]Mei, J., Li, K.L., 2012. Energy-aware scheduling algorithm with duplication on heterogeneous computing systems. ACM/IEEE 13th Int. Conf. on Grid Computing, p.122-129.

[25]Mishra, R., Rastogi, N., Zhu, D.K., et al., 2003. Energy aware scheduling for distributed real-time systems. Proc. Int. Parallel and Distributed Processing Symp., p.1-9.

[26]Mittal, S., 2014. A survey of techniques for improving energy efficiency in embedded computing systems. Int. J. Comput. Aided Eng. Technol., 6(4):440-459.

[27]Piyatamrong, B., Ohara, S., Kantakajorn, S., 2000. GTCS: a greedy task clustering and scheduling algorithm for distributed memory processor architecture. Proc. 4th Int. Conf./Exhibition on High Performance Computing in the Asia-Pacific Region, p.310-314.

[28]Sih, G.C., Lee, E.A., 1993. A compile-time scheduling heuristic for interconnection-constrained heterogeneous processor architectures. IEEE Trans. Parall. Distr. Syst., 4(2):175-187.

[29]Tang, X.Y., Li, K.L., Liao, G.P., et al., 2010. List scheduling with duplication for heterogeneous computing systems. J. Parall. Distr. Comput., 70(4):323-329.

[30]Terzopoulos, G., Karatza, H.D., 2013. Dynamic voltage scaling scheduling on power-aware clusters under power constraints. IEEE/ACM 17th Int. Symp. on Distributed Simulation and Real Time Applications, p.72-78.

[31]Topcuoglu, H., Hariri, S., Wu, M.Y., 2002. Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parall. Distr. Syst., 13(3):260-274.

[32]Ullman, J.D., 1975. NP-complete scheduling problems. J. Comput. Syst. Sci., 10(3):384-393.

[33]Wang, L.Z., Khan, S.U., Chen, D., et al., 2013. Energy-aware parallel task scheduling in a cluster. Fut. Gener. Comput. Syst., 29(7):1661-1670.

[34]Yang, T., Gerasoulis, A., 1994. DSC: scheduling parallel tasks on an unbounded number of processors. IEEE Trans. Parall. Distr. Syst., 5(9):951-967.

[35]Zhu, X.M., He, C., Li, K.L., et al., 2012. Adaptive energy-efficient scheduling for real-time tasks on DVS-enabled heterogeneous clusters. J. Parall. Distr. Comput., 72(6):751-763.