Abstract
The use of parallel/distributed programming increases as it enables high performance computing. There are many tools that help a user in the performance analysis of the application, and that allow to improve the application execution. As there is a high demand of computational power, new systems, such as large scale computer clusters, have become more common and accessible to everyone to solve complex problems. However, these systems generate a new set of problems related to the scalability of current analysis and tuning tools. Our automatic and dynamic tuning environment MATE does not scale well because it has a set of common bottlenecks in its architecture, and hence we have decided to improve the tool for providing dynamic tuning on large scale systems too. For this purpose, we are designing a new tool that introduces a tree-based overlay network infrastructure for scalable metrics collection, and to substitutes the current centralized performance analysis by a distributed one, in order to provide better scalability.
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Arnold, D.C., Pack, G.D., Miller, B.P.: Tree-based Overlay Networks for Scalable Applications. In: 11th International Workshop on High-Level Parallel Programming Models and Supportive Environments (HIPS 2006), Rhodes, Greece (2006)
Benedict, S., Petkov, V., Gerndt, M.: PERISCOPE: An Online-based Distributed Performance Analysis Tool. In: Proc. 3rd International Workshop on Parallel Tools for High Performance (2009)
Buck, B., Hollingsworth, J.: An API for Runtime Code Patching. International Journal of High Performance Computing Applications 14, 317–329 (2000)
Caymes-Scutari, P.: Extending the Usability of a Dynamic Tuning Environment. Ph.D. thesis, Universitat Autònoma de Barcelona (2007)
Caymes-Scutari, P., Morajko, A., Margalef, T., Luque, E.: Automatic Generation of Dynamic Tuning Techniques. In: Kermarrec, A.-M., Bougé, L., Priol, T. (eds.) Euro-Par 2007. LNCS, vol. 4641, pp. 13–22. Springer, Heidelberg (2007)
César, E., Moreno, A., Sorribes, J., Luque, E.: Modeling Master/Worker Applications for Automatic Performance Tuning. Parallel Computing 32, 568–589 (2006)
DeRose, L., Homer, B., Johnson, D., Kaufmann, S., Poxon, H.: Cray Performance Analysis Tools. In: Tools for High Performance Computing, pp. 191–199 (2008)
Guevara Quintero, J.: Definition of a Resource Management Strategy for Dynamic Performance Tuning of Complex Applications. Ph.D. thesis, Universitat Autònoma de Barcelona (2010)
Jorba, J., Margalef, T., Luque, E., André, J., Viegas, D.: Application of Parallel Computing to the Simulation of Forest Fire Propagation. In: 3rd International Conference in Forest Fire Propagation, vol. 1, pp. 891–900 (1998)
Mohr, B., Wylie, B.J.N., Wolf, F.: Performance Measurement and Analysis Tools for Extremely Scalable Systems. Concurrency and Computation: Practice and Experience 22, 2212–2229 (2010)
Morajko, A.: Dynamic Tuning of Parallel/Distributed Applications. Ph.D. thesis, Universitat Autònoma de Barcelona (2004)
Morajko, A., Caymes, P., Margalef, T., Luque, E.: Automatic Tuning of Data Distribution Using Factoring in Master/Worker Applications. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2005. LNCS, vol. 3515, pp. 132–139. Springer, Heidelberg (2005)
Morajko, A., Caymes-Scutari, P., Margalef, T., Luque, E.: MATE: Monitoring, Analysis and Tuning Environment for parallel/distributed applications. Concurrency and Computation: Practice and Experience 19, 1517–1531 (2007)
Morajko, A., Margalef, T., Luque, E.: Design and Implementation of a Dynamic Tuning Environment. Journal of Parallel and Distributed Computing 67(4), 474–490 (2007)
Moreno, A., César, E., Guevara, A., Sorribes, J., Margalef, T., Luque, E.: Dynamic Pipeline Mapping (DPM). In: Luque, E., Margalef, T., Benítez, D. (eds.) Euro-Par 2008. LNCS, vol. 5168, pp. 295–304. Springer, Heidelberg (2008), http://dx.doi.org/10.1007/978-3-540-85451-7_32
Ribler, R., Vetter, J., Simitci, H., Reed, D.A.: Autopilot: Adaptive Control of Distributed Applications. In: Proc. of IEEE Symposium on HPDC, pp. 172–179 (1998)
Roth, P.C., Arnold, D.C., Miller, B.P.: MRNet: A software-based multicast/reduction network for scalable tools. In: Proc. IEEE/ACM Supercomputing 2003, p. 21 (2003)
Roth, P.C., Miller, B.P.: On-line automated performance diagnosis on thousands of processes. In: Proc. of the Eleventh ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2006, pp. 69–80. ACM, New York (2006), http://doi.acm.org/10.1145/1122971.1122984
Tapus, C., Chung, I.H., Hollingsworth, J.: Active Harmony: Towards Automated Performance Tuning. In: Proc. from the Conference on High Performance Networking and Computing, pp. 1–11 (2003)
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Morajko, A., Martínez, A., César, E., Margalef, T., Sorribes, J. (2012). MATE: Toward Scalable Automated and Dynamic Performance Tuning Environment. In: Jónasson, K. (eds) Applied Parallel and Scientific Computing. PARA 2010. Lecture Notes in Computer Science, vol 7134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28145-7_42
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