Abstract
The paper proposes an approach for parallelization of computations across a collection of clusters with heterogeneous nodes with both GPUs and CPUs. The proposed system partitions input data into chunks and assigns to particular devices for processing using OpenCL kernels defined by the user. The system is able to minimize the execution time of the application while maintaining the power consumption of the utilized GPUs and CPUs below a given threshold. We present real measurements regarding performance and power consumption of various GPUs and CPUs used in a modern parallel system. Furthermore we show, for a parallel application for breaking MD5 passwords, how the execution time of the real application changes with various upper bounds on the power consumption.
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References
Buyya, R. (ed.): High Performance Cluster Computing, Programming and Applications. Prentice Hall (1999)
Kirk, D.B., Hwu, W.-M.W.: Programming Massively Parallel Processors: A Hands-on Approach 2nd edn. Morgan Kaufmann (2012) ISBN-13: 978-0124159921
Sanders, J., Kandrot, E.: CUDA by Example: An Introduction to General-Purpose GPU Programming. Addison-Wesley Professional (2010) ISBN-13: 978-0131387683
Geist, A., Beguelin, A., Dongarra, J., Jiang, W., Mancheck, R., Sunderam, V.: PVM Parallel Virtual Machine. In: A Users Guide and Tutorial for Networked Parallel Computing. MIT Press, Cambridge (1994), http://www.epm.ornl.gov/pvm/
Wilkinson, B., Allen, M.: Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers. Prentice Hall (1999)
Czarnul, P., Grzeda, K.: Parallel simulations of electrophysiological phenomena in myocardium on large 32 and 64-bit linux clusters. In: Kranzlmüller, D., Kacsuk, P., Dongarra, J. (eds.) EuroPVM/MPI 2004. LNCS, vol. 3241, pp. 234–241. Springer, Heidelberg (2004)
Balicki, J., Krawczyk, H., Nawarecki, E. (eds.): Grid and Volunteer Computing. Gdansk University of Technology, Faculty of Electronics Telecommunication and Informatics Press, Gdansk (2012) ISBN: 978-83-60779-17-0
Karonis, N.T., Toonen, B., Foster, I.: Mpich-g2: A grid-enabled implementation of the message passing interface. Journal of Parallel and Distributed Computing 63, 551–563 (2003); Special Issue on Computational Grids
Keller, R., Müller, M.: The Grid-Computing library PACX-MPI: Extending MPI for Computational Grids, http://www.hlrs.de/organization/amt/projects/pacx-mpi/
Czarnul, P.: BC-MPI: Running an MPI application on multiple clusters with beesyCluster connectivity. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds.) PPAM 2007. LNCS, vol. 4967, pp. 271–280. Springer, Heidelberg (2008)
Sotomayor, B.: The globus toolkit 4 programmer’s tutorial (2005), http://www.casa-sotomayor.net/gt4-tutorial/
Garg, S.K., Buyya, R., Siegel, H.J.: Time and cost trade-off management for scheduling parallel applications on utility grids. Future Gen. Comp. Systems 26, 1344–1355 (2010)
Chin, S.H., Suh, T., Yu, H.C.: Adaptive service scheduling for workflow applications in service-oriented grid. J. Supercomput. 52, 253–283 (2010)
Yu, J., Buyya, R.: A taxonomy of workflow management systems for grid computing. Journal of Grid Computing 3, 171–200 (2005)
Anderson, D.P.: Boinc: A system for public-resource computing and storage. In: Proceedings of 5th IEEE/ACM International Workshop on Grid Computing, Pittsburgh, USA (2004)
Barak, A., Ben-nun, T., Levy, E., Shiloh, A.: A package for opencl based heterogeneous computing on clusters with many gpu devices. In: Proc. of Int. Conf. on Cluster Computing, pp. 1–7 (2011)
He, C., Du, P.: Cuda performance study on hadoop mapreduce clusters. Univ. of Nebraska-Lincoln (2010), http://cse.unl.edu/~che/slides/cuda.pdf
Stan, M.R., Skadron, K.: Guest editors’ introduction: Power-aware computing. IEEE Computer 36, 35–38 (2003)
Cameron, K.W., Ge, R., Feng, X.: High-performance, power-aware distributed computing for scientific applications. Computer 38, 40–47 (2005)
Li, D., De Supinski, B., Schulz, M., Cameron, K., Nikolopoulos, D.: Hybrid mpi/openmp power-aware computing. In: 2010 IEEE International Symposium on Parallel Distributed Processing (IPDPS), pp. 1–12 (2010)
Kasichayanula, K., Terpstra, D., Luszczek, P., Tomov, S., Moore, S., Peterson, G.D.: Power aware computing on gpus. In: Symposium on Application Accelerators in High-Performance Computing, pp. 64–73 (2012)
Lawson, B., Smirni, E.: Power-aware resource allocation in high-end systems via online simulation. In: Arvind, Rudolph, L. (ed.) ICS, pp. 229–238. ACM (2005)
Garg, S., Buyya, R.: Exploiting heterogeneity in grid computing for energy-efficient resource allocation. In: Proceedings of the 17th International Conference on Advanced Computing and Communications (ADCOM 2009), Bengaluru, India (2009)
Cormen, T.H., Leiserson, C.E., Rivest, R.L.: Introduction to Algorithms. The Massachusetts Institute of Technology (1994)
Czarnul, P.: Integration of compute-intensive tasks into scientific workflows in beesyCluster. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3993, pp. 944–947. Springer, Heidelberg (2006)
Czarnul, P.: A model, design, and implementation of an efficient multithreaded workflow execution engine with data streaming, caching, and storage constraints. Journal of Supercomputing 63, 919–945 (2013)
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Czarnul, P., Rościszewski, P. (2014). Optimization of Execution Time under Power Consumption Constraints in a Heterogeneous Parallel System with GPUs and CPUs. In: Chatterjee, M., Cao, Jn., Kothapalli, K., Rajsbaum, S. (eds) Distributed Computing and Networking. ICDCN 2014. Lecture Notes in Computer Science, vol 8314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45249-9_5
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DOI: https://doi.org/10.1007/978-3-642-45249-9_5
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