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View all- Huber DSchreiber MSchulz M(2023)A Case Study on PMIx-Usage for Dynamic Resource ManagementHigh Performance Computing10.1007/978-3-031-40843-4_4(42-55)Online publication date: 21-May-2023
Invasive Compute Balancing for Applications with Shared and Hybrid Parallelization
Authors: 
Martin Schreiber, Christoph Riesinger, Tobias Neckel, Hans-Joachim Bungartz, Alexander BreuerAuthors Info & Claims
Martin Schreiber, Christoph Riesinger, Tobias Neckel, Hans-Joachim Bungartz, Alexander BreuerAuthors Info & ClaimsPages 1004 - 1027
Abstract
Achieving high scalability with dynamically adaptive algorithms in high-performance computing (HPC) is a non-trivial task. The invasive paradigm using compute migration represents an efficient alternative to classical data migration approaches for such algorithms in HPC. We present a core-distribution scheduler which realizes the migration of computational power by distributing the cores depending on the requirements specified by one or more parallel program instances. We validate our approach with different benchmark suites for simulations with artificial workload as well as applications based on dynamically adaptive shallow water simulations, and investigate concurrently executed adaptivity parameter studies on realistic Tsunami simulations. The invasive approach results in significantly faster overall execution times and higher hardware utilization than alternative approaches. A dynamic resource management is therefore mandatory for a more efficient execution of scenarios similar to our simulations, e.g. several Tsunami simulations in urgent computing, to overcome strong scalability challenges in the area of HPC. The optimizations obtained by invasive migration of cores can be generalized to similar classes of algorithms with dynamic resource requirements.
References
[1]
Aizinger, V.: A discontinuous Galerkin method for two-dimensional flow and transport in shallow water. Adv. Water Resour. 25, 67---84 (2002)
[2]
Al Faruque, M.A., Krist, R., Henkel, J.: ADAM: run-time agent-based distributed application mapping for on-chip communication. In: Proceedings of the 45th Annual Design Automation Conference, ACM, New York, NY, USA, DAC '08, pp. 760---765 (2008)
[3]
Bader, M., Breuer, A., Schreiber, M.: Parallel fully adaptive tsunami simulations. In: Facing the Multicore-Challenge III, Institut für Informatik, Technische Universität München, Springer, Heidelberg, Germany. Lecture Notes in Computer Science, vol. 7686 (2012a)
[4]
Bader, M., Bungartz, H.J., Schreiber, M.: Invasive computing on high performance shared memory systems. In: Facing the Multicore-Challenge III. Lecture Notes in Computer Science, vol. 7686, pp. 1---12. Springer (2012b)
[5]
Bangerth, W., Hartmann, R., Kanschat, G.: Deal.II--a general purpose object oriented finite element library. ACM Trans. Math. Softw. 33(4), 1---27 (2007)
[6]
Becchi, M., Crowley, P.: Dynamic thread assignment on heterogeneous multiprocessor architectures. In: Proceedings of the 3rd Conference on Computing Frontiers, ACM, New York, NY, USA, CF '06, pp. 29---40 (2006)
[7]
Behrens, J.: Efficiency for adaptive triangular meshes: key issues of future approaches. In: Hamilton, K., Lohmann, G., Mysak, L. A. (eds.) Earth System Modelling, vol. 2. Springer (2012)
[8]
Bhadauria, M., McKee, S.: An approach to resource-aware co-scheduling for CMPs. In: Proceedings of the 24th ACM International Conference on Supercomputing, ACM, ICS '10, pp. 189---199 (2010)
[9]
BODC.: Centenary Edition of the GEBCO Digital Atlas (2013)
[10]
Bolosky, W.J., Scott, M.L.: False sharing and its effect on shared memory performance. In: 4th Symposium on Experimental Distributed and Multiprocessor Systems, pp. 57---71 (1993)
[11]
Burstedde, C., Wilcox, L.C., Ghattas, O.: p4est: scalable algorithms for parallel adaptive mesh refinement on forests of octrees. SIAM J. Sci. Comput. 33(3), 1103---1133 (2011).
[12]
Castro, C., Käser, M., Toro, E.: Space-time adaptive numerical methods for geophysical applications. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 367, 4613---4631 (2009)
[13]
Corbalán, J., Martorell, X., Labarta, J.: Performance-driven processor allocation. In: Proceedings of the 4th Conference on Symposium on Operating System Design & Implementation, vol. 4 (2000)
[14]
Corbalan, J., Martorell, X., Labarta, J.: Performance-driven processor allocation. IEEE Trans. Parallel Distrib. Syst. 16(7), 599---611 (2005)
[15]
De Grande, R., Boukerche, A.: Dynamic load redistribution based on migration latency analysis for distributed virtual simulations. In: 2011 IEEE International Workshop on Haptic Audio Visual Environments and Games (HAVE), pp. 88---93 (2011).
[16]
Drosinos, N., Koziris, N.: Performance comparison of pure MPI vs hybrid MPI-OpenMP parallelization models on SMP clusters. In: Parallel and Distributed Processing Symposium 2004 IEEE (2004)
[17]
Falby, J.S., Zyda, M.J., Pratt, D.R., Mackey, R.L.: NPSNET: hierarchical data structures for real-time three-dimensional visual simulation. Comput. Graph. 17(1), 65---69 (1993)
[18]
Fleisch, B.D.: Distributed system V IPC in LOCUS: a design and implementation retrospective. ACM SIGCOMM Comput. Commun. Rev. ACM 16, 386---396 (1986)
[19]
Fletcher, R., Powell, M.J.: A rapidly convergent descent method for minimization. Comput. J. 6(2), 163---168 (1963)
[20]
Garcia, M., Corbalan, J., Badia Maria, R., Labarta, J.: A dynamic load balancing approach with SMPSuperscalar and MPI. In: Keller, R., Kramer, D., Weiss, J.P. (eds.) Facing the Multicore-Challenge II, Springer Berlin Heidelberg, Stuttgart (2012)
[21]
George, D.: Augmented Riemann solvers for the shallow water equations over variable topography with steady states and inundation. J. Comput. Phys. 227(6), 3089---3113 (2008)
[22]
Gerndt, M., Hollmann, A., Meyer, M., Schreiber, M., Weidendorfer, J.: Invasive computing with iOMP. In: Specification and Design Languages (FDL), pp. 225---231. IEEE, Vienna (2012)
[23]
Hesthaven, J.S., Warburton, T.: Nodal Discontinuous Galerkin Methods: Algorithms, Analysis, and Applications, pp. 97---107. Springer Verlag, New York (2008)
[24]
Hsieh, W.C.Y.: Dynamic computation migration in distributed shared memory systems. PhD thesis, MIT (1995)
[25]
Keyes, D.E.: Four horizons for enhancing the performance of parallel simulations based on partial differential equations. In: Euro-Par 2000 Parallel Processing, pp. 1---17. Springer (2000)
[26]
Kobbe, S., Bauer, L., Lohmann, D., Schröder-Preikschat, W., Henkel, J.: DistRM: Distributed resource management for on-chip many-core systems. In: Proceedings of the Seventh IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, ACM, pp. 119---128 (2011)
[27]
Li, D., De Supinski, B., Schulz, M., Cameron, K., Nikolopoulos, D.: Hybrid MPI/OpenMP power-aware computing. In: Parallel Distributed Processing (IPDPS), pp. 1---12 (2010)
[28]
Meister, O., Rahnema, K., Bader, M.: A software concept for cache-efficient simulation on dynamically adaptive structured triangular grids. In: PARCO, pp. 251---260 (2011)
[29]
Michael, M.M.: Scalable lock-free dynamic memory allocation. ACM SIGPLAN Not. ACM 39, 35---46 (2004)
[30]
Neckel, T.: The PDE framework peano: an environment for efficient flow simulations. Dissertation, Institut für Informatik, Technische Universität München (2009)
[31]
Nogina, S., Unterweger, K., Weinzierl, T.: Autotuning of adaptive mesh refinement PDE solvers on shared memory architectures. In: PPAM 2011. Lecture Notes in Computer Science, vol. 7203, pp. 671---680. Springer, Heidelberg (2012)
[32]
Reinders, J.: Intel Threading Building Blocks: Outfitting C++ for Multi-core Processor Parallelism. O'Reilly Media Inc, Sebastopol (2010)
[33]
Rosu, D., Schwan, K., Yalamanchili, S., Jha, R.: On adaptive resource allocation for complex real-time applications. In: Proceedings of the 18th IEEE Real-Time Systems Symposium, IEEE Computer Society, Washington, DC, USA, RTSS '97, p. 320 (1997).
[34]
Rüde, U.: Fully adaptive multigrid methods. SIAM J. Numer. Anal. 30(1), 230---248 (1993)
[35]
Rusanov, V.V.: Calculation of interaction of non-steady shock waves with obstacles. NRC, Division of Mechanical Engineering (1962)
[36]
Sagan, H.: Space-Filling Curves, vol. 18. Springer, New York (1994)
[37]
Schmidl, D., Cramer, T., Wienke, S., Terboven, C., Müller, M.: Assessing the performance of openmp programs on the intel xeon phi. In: Wolf, F., Mohr, B., Mey, D. (eds.) Euro-Par 2013 Parallel Processing. Lecture Notes in Computer Science, vol. 8097, pp. 547---558. Springer, Berlin (2013)
[38]
Schreiber, M., Bungartz, H.J., Bader, M.: Shared memory parallelization of fully-adaptive simulations using a dynamic tree-split and -join approach. In: IEEE International Conference on High Performance Computing (HiPC), IEEE Xplore, Puna, India (2012)
[39]
Schreiber, M., Weinzierl, T., Bungartz, H.J.: Cluster optimization of parallel simulations with dynamically adaptive grids. In: EuroPar 2013, Aachen, Germany (2013a)
[40]
Schreiber, M., Weinzierl, T., Bungartz, H.J.: SFC-based communication metadata encoding for adaptive mesh. In: Proceedings of the International Conference on Parallel Computing (ParCo) (2013b)
[41]
Shao, G., Li, X., Ji, C., Maeda, T.: Focal mechanism and slip history of the 2011 Mw 9.1 off the Pacific coast of Tohoku Earthquake, constrained with teleseismic body and surface waves. Earth Planets Space 63(7), 559---564 (2011)
[42]
Teich, J., Henkel, J., Herkersdorf, A., Schmitt-Landsiedel, D., Schröder-Preikschat, W., Snelting, G.: Invasive computing: an overview. In: Multiprocessor SoC, pp. 241---268. Springer (2011)
[43]
Tradowsky, C., Schreiber, M., Vesper, M., Domladovec, I., Braun, M., Bungartz, H.J., Becker, J.: Towards Dynamic Cache and Bandwidth Invasion, pp. 97---107. Springer International Publishing (2014)
[44]
Vigh, C.A.: Parallel simulations of the shallow water equations on structured dynamically adaptive triangular grids. Dissertation, Institut für Informatik, Technische Universität München (2012)
[45]
Vuchener, C., Esnard, A.: Dynamic load-balancing with variable number of processors based on graph repartitioning. In: Proceedings of High Performance Computing (HiPC 2012), pp. 1---9 (2012)
[46]
Weinzierl, T.: A framework for parallel PDE solvers on multiscale adaptive cartesian grids. Dissertation, Institut für Informatik, Technische Universität München, München (2009)
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Published: 01 December 2015
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View all- Huber DSchreiber MSchulz M(2023)A Case Study on PMIx-Usage for Dynamic Resource ManagementHigh Performance Computing10.1007/978-3-031-40843-4_4(42-55)Online publication date: 21-May-2023