A simulation framework for scheduling performance evaluation on CPU-GPU heterogeneous system

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7336 LNCS, Issue PART 4, 2012, Pages 457-469

  Vella, Flavio ^a   Neri, Igor ^a   Gervasi, Osvaldo ^a   Tasso, Sergio ^a  

^a UNIVERSITY OF PERUGIA   (Italy)

Author keywords

GPU; Heterogeneous Systems; OpenCL; Operating System; Scheduling; Simulation

Indexed keywords

COMPUTATIONAL POWER; GPU; HETEROGENEOUS SYSTEMS; HYBRID ARCHITECTURES; MODEL EVENTS; OPENCL; PRIORITY QUEUES; SCHEDULING DISCIPLINE; SCHEDULING PERFORMANCE; SCHEDULING POLICIES; SCHEDULING PROCESS; SIMULATION; SIMULATION FRAMEWORK;

COMPUTER OPERATING SYSTEMS; HYBRID SYSTEMS;

SCHEDULING;

EID: 84863945458     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-31128-4_34     Document Type: Conference Paper

References (18)

1. NVidia: NVIDIA CUDA - Compute Unified Device Architecture: Programming Guide (2011), http://developer.nvidia.com/nvidia-gpu-computing-documentation
2. Khronos OpenCL Working Group, The OpenCL Specification, version 1.0.29 (2008), http://khronos.org/registry/cl/specs/opencl-1.0.29.pdf
3. Vella, F., Cefalá, R., Costantini, A., Gervasi, O., Tanci, C.: Gpu computing in egi environment using a cloud approach. In: 2011 International Conference on Computational Science and Its Applications, pp. 150-155. IEEE (2011)
4. Pabla, C.: Completely fair scheduler. Linux Journal 2009(184), 4 (2009)
5. Lin, C., Lai, C.: A scheduling algorithm for gpu-attached multicore hybrid systems. In: 2011 5th International Conference on New Trends in, Information Science and Service Science (NISS), vol. 1, pp. 26-31. IEEE (2011)
6. Guevara, M., Gregg, C., Hazelwood, K., Skadron, K.: Enabling task parallelism in the cuda scheduler. Work (2009)
7. Jiménez, V.J., Vilanova, L., Gelado, I., Gil, M., Fursin, G., Navarro, N.: Predictive Runtime Code Scheduling for Heterogeneous Architectures. In: Seznec, A., Emer, J., O'Boyle, M., Martonosi, M., Ungerer, T. (eds.) HiPEAC 2009. LNCS, vol. 5409, pp. 19-33. Springer, Heidelberg (2009)
8. Phatanapherom, S., Uthayopas, P., Kachitvichyanukul, V.: Dynamic scheduling ii: fast simulation model for grid scheduling using hypersim. In: Proceedings of the 35th Conference on Winter Simulation: Driving Innovation. pp. 1494-1500. Winter Simulation Conference (2003)
9. Buyya, R., Murshed, M.: Gridsim: A toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. Concurrency and Computation: Practice and Experience 14(13-15), 1175-1220 (2002)
10. Casanova, H.: Simgrid: A toolkit for the simulation of application scheduling. In: Proceedings of First IEEE/ACM International Symposium on Cluster Computing and the Grid 2001, pp. 430-437. IEEE (2001)
11. Gupta, A., Tucker, A., Urushibara, S.: The impact of operating system scheduling policies and synchronization methods of performance of parallel applications. In: ACM SIGMETRICS Performance Evaluation Review, vol. 19, pp. 120-132. ACM (1991)
12. Law, A., Kelton, W.: Simulation modeling and analysis, vol. 3. McGraw-Hill, New York (2000)
13. Gere Jr., W.: Heuristics in job shop scheduling. Management Science, 167-190 (1966)
14. Japkowicz, N., Stephen, S.: The class imbalance problem: A systematic study. Intelligent Data Analysis 6(5), 429-449 (2002)
15. Brown, R.: Calendar queues: a fast 0 (1) priority queue implementation for the simulation event set problem. Communications of the ACM 31(10), 1220-1227 (1988)
16. Silberschatz, A., Galvin, P., Gagne, G.: Operating system concepts, vol. 4. Addison- Wesley (1998)
17. Tanenbaum, A., Tannenbaum, A.: Modern operating systems, vol. 2. Prentice Hall, New Jersey (1992)
18. Nickolls, J., Dally, W.: The gpu computing era. IEEE Micro 30(2), 56-69 (2010)