Abstract
The increasing complexity of hardware mechanisms for recent processors makes high performance code generation very challenging. One of the main issue for high performance is the optimization of memory accesses. General purpose compilers, with no knowledge of the application context and approximate memory model, seem inappropriate for this task. Combining application-dependent optimizations on the source code and exploration of optimization parameters as it is achieved with ATLAS, has been shown as one way to improve performance. Yet, hand-tuned codes such as in the MKL library still outperform ATLAS with an important speed-up and some effort has to be done in order to bridge the gap between performance obtained by automatic and manual optimizations.
In this paper, a new iterative compilation approach for the generation of high performance codes is proposed. This approach is not application-dependent, compared to ATLAS. The idea is to separate the memory optimization phase from the computation optimization phase. The first step automatically finds all possible decompositions of the code into kernels. With datasets that fit into the cache and simplified memory accesses, these kernels are simpler to optimize, either with the compiler, at source level, or with a dedicated code generator. The best decomposition is then found by a model-guided approach, performing on the source code the required memory optimizations.
Exploration of optimization sequences and their parameters is achieved with a meta-compilation language, X language. The first results on linear algebra codes for Itanium show that the performance obtained reduce the gap with those of highly optimized hand-tuned codes.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Alias, C., Barthou, D.: On Domain Specific Languages Re-Engineering. In: Glück, R., Lowry, M. (eds.) GPCE 2005. LNCS, vol. 3676, pp. 63–77. Springer, Heidelberg (2005)
Bodin, F., Mevel, Y., Quiniou, R.: A user level program transformation tool. In: ACM Int. Conf. on Supercomputing, Melbourne, Australia, pp. 180–187. ACM Press, New York (1998), doi:10.1145/277830.277868
Clauss, P.: Counting solutions to linear and nonlinear constraints through Ehrhart polynomials: Applications to analyze and transform scientific programs. In: ACM Int. Conf. on Supercomputing, pp. 278–295. ACM Press, New York (1996)
Coleman, S., McKinley, K.S.: Tile size selection using cache organization and data layout. In: ACM Conf. on Programming Language Design and Implementation, La Jolla, California, United States, pp. 279–290. ACM Press, New York (1995), doi:10.1145/207110.207162
Cooper, K.D., Waterman, T.: Investigating Adaptive Compilation using the MIPSPro Compiler. In: Symp. of the Los Alamos Computer Science Institute, October (2003)
Djoudi, L., et al.: Exploring application performance: a new tool for a static/dynamic approach. In: Symp. of the Los Alamos Computer Science Institute, Santa Fe, NM, Oct. (2005)
Donadio, S., et al.: A language for the Compact Representation of Multiple Program Versions. In: Ayguadé, E., et al. (eds.) LCPC 2005. LNCS, vol. 4339, Springer, Heidelberg (2006)
Engineering and scientific subroutine library. Guide and Reference. IBM.
Feautrier, P.: Dataflow analysis of scalar and array references. Int. J. of Parallel Programming 20(1), 23–53 (1991)
Fraguela, B., Doallo, R., Zapata, E.: Automatic analytical modeling for the estimation of cache misses. In: Int. Conf. on Parallel Architectures and Compilation Techniques, Washington, DC, USA, p. 221. IEEE Computer Society Press, Los Alamitos (1999)
Goto, K., van de Geijn, R.: On reducing tlb misses in matrix multiplication. Technical report, The University of Texas at Austin, Department of Computer Sciences (2002)
Jalby, W., Lemuet, C., Le Pasteur, X.: Wbtk: a new set of microbenchmarks to explore memory system performance for scientific computing. Int. J. High Perform. Comput. Appl. 18(2), 211–224 (2004), doi:10.1177/1094342004038945
Kodukula, I., Ahmed, N., Pingali, K.: Data-centric multi-level blocking. In: ACM Conf. on Programming Language Design and Implementation, pp. 346–357. ACM, New York (1997), citeseer.ist.psu.edu/kodukula97datacentric.html
Kodukula, I., Pingali, K.: Transformations for imperfectly nested loops. In: ACM Int. Conf. on Supercomputing, Pittsburgh, Pennsylvania, United States, p. 12. IEEE Computer Society, Washington (1996), doi:10.1145/369028.369051
Metzger, R., Wen, Z.: Automatic Algorithm Recognition: A New Approach to Program Optimization. MIT Press, Cambridge (2000)
Intel math kernel library (intel mkl). Intel.
Triantafyllis, S., Vachharajani, M., August, D.I.: Compiler Optimization-Space Exploration. Journal of Instruction-level Parallelism (2005)
Whaley, R., Dongarra, J.: Automatically tuned linear algebra software (1997)
Wolfe, M.: Iteration space tiling for memory hierarchies. In: Conf. on Parallel Processing for Scientific Computing, pp. 357–361. Society for Industrial and Applied Mathematics, Philadelphia (1989)
Caps entreprise. http://www.caps-entreprise.com
Yotov, K., et al.: Is search really necessary to generate high-performance blas (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Barthou, D., Donadio, S., Duchateau, A., Jalby, W., Courtois, E. (2007). Iterative Compilation with Kernel Exploration. In: Almási, G., Caşcaval, C., Wu, P. (eds) Languages and Compilers for Parallel Computing. LCPC 2006. Lecture Notes in Computer Science, vol 4382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72521-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-72521-3_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72520-6
Online ISBN: 978-3-540-72521-3
eBook Packages: Computer ScienceComputer Science (R0)