Abstract
In recent years heterogeneous multi-core systems have been given much attention. However, performance optimization on these platforms remains a big challenge. Optimizations performed by compilers are often limited due to lack of dynamic information and run time environment, which makes applications often not performance portable. One current approach is to provide multiple implementations for the same interface that could be used interchangeably depending on the call context, and expose the composition choices to a compiler, deployment-time composition tool and/or run-time system. Using off-line machine-learning techniques allows to improve the precision and reduce the run-time overhead of run-time composition and leads to an improvement of performance portability. In this work we extend the run-time composition mechanism in the PEPPHER composition tool by off-line composition and present an adaptive machine learning algorithm for generating compact and efficient dispatch data structures with low training time. As dispatch data structure we propose an adaptive decision tree structure, which implies an adaptive training algorithm that allows to control the trade-off between training time, dispatch precision and run-time dispatch overhead.
We have evaluated our optimization strategy with simple kernels (matrix-multiplication and sorting) as well as applications from RODINIA benchmark on two GPU-based heterogeneous systems. On average, the precision for composition choices reaches 83.6 percent with approximately 34 minutes off-line training time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ansel, J., Chan, C.P., Wong, Y.L., Olszewski, M., Zhao, Q., Edelman, A., Amarasinghe, S.P.: PetaBricks: A language and compiler for algorithmic choice. In: Proceedings of the 2009 ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2009, pp. 38–49. ACM (2009)
Augonnet, C., Thibault, S., Namyst, R.: Automatic calibration of performance models on heterogeneous multicore architectures. In: Lin, H.-X., Alexander, M., Forsell, M., Knüpfer, A., Prodan, R., Sousa, L., Streit, A. (eds.) Euro-Par 2009. LNCS, vol. 6043, pp. 56–65. Springer, Heidelberg (2010)
Augonnet, C., Thibault, S., Namyst, R., Wacrenier, P.-A.: StarPU: A Unified Platform for Task Scheduling on Heterogeneous Multicore Architectures. Concurrency and Computation: Practice and Experience, Special Issue: Euro-Par 2009 23, 187–198 (2011)
Benkner, S., Pllana, S., Träff, J.L., Tsigas, P., Dolinsky, U., Augonnet, C., Bachmayer, B., Kessler, C., Moloney, D., Osipov, V.: PEPPHER: Efficient and productive usage of hybrid computing systems. IEEE Micro 31(5), 28–41 (2011)
Danylenko, A., Kessler, C., Löwe, W.: Comparing machine learning approaches for context-aware composition. In: Apel, S., Jackson, E. (eds.) SC 2011. LNCS, vol. 6708, pp. 18–33. Springer, Heidelberg (2011)
Dastgeer, U., Li, L., Kessler, C.: Performance-aware dynamic composition of applications for heterogeneous multicore systems with the PEPPHER composition tool. In: Proc. 16th Int. Workshop on Compilers for Parallel Computers (CPC 2012), Padova, Italy (January 2012)
de Mesmay, F., Voronenko, Y., Püschel, M.: Offline library adaptation using automatically generated heuristics. In: Int. Parallel and Distr. Processing Symp. (IPDPS 2010), pp. 1–10 (2010)
Frigo, M., Johnsson, S.G.: Fftw: An adaptive software architecture for the FFT. In: Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, vol. 3, pp. 1381–1384 (May 1998)
Grewe, D., O’Boyle, M.F.P.: A static task partitioning approach for heterogeneous systems using openCL. In: Knoop, J. (ed.) CC 2011. LNCS, vol. 6601, pp. 286–305. Springer, Heidelberg (2011)
Katagiri, T., Kise, K., Honda, H., Yuba, T.: Abclibscript: a directive to support specification of an auto-tuning facility for numerical software. Parallel Computing 32(1), 92–112 (2006)
Kessler, C.W., Löwe, W.: A framework for performance-aware composition of explicitly parallel components. In: Parallel Computing: Architectures, Algorithms and Applications (ParCo 2007). Advances in Parallel Computing, vol. 15, pp. 227–234. IOS Press (2007)
Kessler, C.W., Löwe, W.: Optimized composition of performance-aware parallel components. In: Proc. 15th Int. Workshop on Compilers for Parallel Computers (CPC 2010) (July 2010)
Kessler, C.W., Löwe, W.: Optimized composition of performance-aware parallel components. Concurrency and Computation: Practice and Experience 24(5), 481–498 (2012); Published online in Wiley Online Library, doi: 10.1002/cpe.1844 (September 2011)
Li, X., Garzarán, M.J.: Optimizing matrix multiplication with a classifier learning system. In: Ayguadé, E., Baumgartner, G., Ramanujam, J., Sadayappan, P. (eds.) LCPC 2005. LNCS, vol. 4339, pp. 121–135. Springer, Heidelberg (2006)
Li, X., Garzarán, M.J., Padua, D.: A dynamically tuned sorting library. In: Proc. ACM Symp. on Code Generation and Optimization (CGO 2004), pp. 111–124 (2004)
Park, E., Kulkarni, S., Cavazos, J.: An evaluation of different modeling techniques for iterative compilation. In: Proc. Int. Conf. on Compilers, Architectures and Synthesis for Embedded Systems (CASES 2011) (October 2011)
Püschel, M., Moura, J.M.F., Johnson, J.R., Padua, D., Veloso, M.M., Singer, B.W., Xiong, J., Franchetti, F., Gacic, A., Voronenko, Y., Chen, K., Johnson, R.W., Rizzolo, N.: Spiral: Code generation for DSP transforms. Proceedings of the IEEE 93(2) (February 2005)
Ross Quinlan, J.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Singer, B., Veloso, M.: Learning to predict performance from formula modeling and training data. In: Proc. 17th Int. Conf. on Machine Learning, pp. 887–894 (2000)
Singer, B., Veloso, M.: Learning to construct fast signal processing implementations. Journal of Machine Learning Research 3, 887–919 (2002)
Thomas, N., Tanase, G., Tkachyshyn, O., Perdue, J., Amato, N.M., Rauchwerger, L.: A framework for adaptive algorithm selection in STAPL. In: Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), pp. 277–288. ACM (2005)
Thomson, J., O’Boyle, M., Fursin, G., Franke, B.: Reducing training time in a one-shot machine learning-based compiler. In: Gao, G.R., Pollock, L.L., Cavazos, J., Li, X. (eds.) LCPC 2009. LNCS, vol. 5898, pp. 399–407. Springer, Heidelberg (2010)
Wang, Z., O’Boyle, M.F.P.: Mapping parallelism to multi-cores: a machine learning based approach. SIGPLAN Not. 44(4), 75–84 (2009)
Wernsing, J.R., Stitt, G.: Elastic computing: A framework for transparent, portable, and adaptive multi-core heterogeneous computing. In: Proceedings of the ACM SIGPLAN/SIGBED 2010 Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES), pp. 115–124. ACM (2010)
Whaley, R.C., Petitet, A., Dongarra, J.: Automated empirical optimizations of software and the ATLAS project. Parallel Computing 27(1-2), 3–35 (2001)
Yu, H., Rauchwerger, L.: An adaptive algorithm selection framework for reduction parallelization. IEEE Trans. on Par. and Distr. Syst. 17(10), 1084–1096 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, L., Dastgeer, U., Kessler, C. (2013). Adaptive Off-Line Tuning for Optimized Composition of Components for Heterogeneous Many-Core Systems. In: Daydé, M., Marques, O., Nakajima, K. (eds) High Performance Computing for Computational Science - VECPAR 2012. VECPAR 2012. Lecture Notes in Computer Science, vol 7851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38718-0_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-38718-0_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38717-3
Online ISBN: 978-3-642-38718-0
eBook Packages: Computer ScienceComputer Science (R0)